This script builds on top of a previous script to extract actions and reactions from the notes on fighter X1 and X2 in the UFC. These extracted features are used as dummy variables to predict the outcome of a landed hit or other selected target variable out of the given features and added features using machine learning on these numeric values. This takes the first round of three separate fights for Felicia Spencer with Zarah, Anderson, and Cyborg.
Github files repository
These are the actions extracting and a brief description:
cross: punch/strike/overhead hit/etc to hit with the top four knuckles of hand upon opponent’s body or more likely face or side of the head, this could mean right cross or left cross, even when in the mount position of ground work the fighter could choose to hit opponent with a cross instead of a hammer or hook or upper cut punch, usually lifting the hand to add in gravity and acceleration with own strength and downward body movement intending to knock out, add to get opponent to release grip if in a lock, or to damage the face or cause pain
jab: This is done normally when breaking the ice or trying to open up opponent, it is not intended to be a power shot or heavy hit, usually used repetitively or to stun before delivering a cross which is a heavier hit powered from momentum added from the hips etc. Typically done in the stand up dance when testing out opponent when neither is getting too close for combat
hammer: This is a strike typically done in a ground in pound mount position, but can be while in the mount on top or on the bottom loosening a choke hold. The hit is done to smash and stun the face of opponent, and typically not a favorite, because it can hurt the fighter as much as the opponent due to the part of the hand being used. This type of strike is used for fast repetitive bursts to stun and get out of a lock when on the floor striking or preventing a choke or joint lock. The side of the fist used is the pinky side in alignment with the elbow. Can be lifted above the head to get more power in accelerating with gravity and body weight to hit opponent’s face. This is why some fighters hit with a regular jab or cross instead to lessen damage to wrist and hand while stiking opponent. All strikes cause damage, but this is not likely to knock an opponent out, also the hand can be thrown off side to make the person get off balance and lose body control in the ground fight. The male fighters use this more than the females.
hook: This is a heavy hit or used in combination with other strikes like the jab or cross, because if the first hit, opponent can just back away unless their against the cage or ropes. It stuns and can knock the breath out of opponent, adds to chances of getting a TKO or technical knock out or knockout. Its a sneaky punch delivered when opponents arms are up or down blocking head shots and/or body kicks.
upper: This is a puch delivered from upward momentum to strike the chin when up close in a clinch or hold of some sort. Not a powerful punch compared to other strikes, used to stun and to set up for other heavy hits or barrages of various strikes at once.
kick: The push kick is the most powerful to move opponent off balance with little effort, but the muay thai kick (mt abrev.) is a very powerful kick if your skilled in the pivot and trained your shins to deliver bone to bone or head kicks with its snapping movement. It breaks down the balance and you see fighters deliver this to the lead leg to knock the opponent off balance and prevent leverage needed for heavy hits or crosses, it can also make the opponent stumble if they get their lead leg kicked hard enough to knock out of stance. Some go for the inner leg as well to knock off balance. This kick is also shown aimed at the outer lead leg Iliotibial or IT band to cause pain and test out their kick to lead up to a head kick or the opponent’s defenses. Usually some punches or an attempt at a takedown of opponent occurs, unless they step backwards.
elbow: This is a strike used standing up at close range, or in a ground and pound full mount position to drop with gravity while aiming the elbow towards the opponents face, not sure if the target is the forehead, but most opponents getting hit with the elbow end up with cuts/slashes/gashes on their forehead. This could be a technical knockout or some sort of stoppage if the opponent can’t fight because blood gets in their eyes, and a doctor dismisses the fight due to inability to continue from injury. When cut, blood drips into the eyes. And it is said can cause blindness if the calcium hardens from the blood in the eye.
takedown: This happens when the fighter thinks they want to either body slam the opponent or have a better chance at ground and pound and a chance at mounting opponent once taken down, or will out wrestle the opponent with a joint lock or choke. Wrestling may look less violent, but wrestling can leave joints in repair for 6-12 months in rehab, break ribs from the chest locks, and make the opponent lose oxygen with neck holds and pass out. Usually fighters tap out if they think they will pass out, and would rather tap out than get choked out or know they are not a strong enough wrestler to break apart the hold. Once it is locked, it can be held for many minutes until the fight round ends, but usually skilled fighters will find other ways to disarm their opponent with wrestling, hits, body shifting, etc.
knees: These are used in clinches or holds of some sort, and can be aimed with high knees, jumping knees, knees striking the IT bands, or the abdomen, to stun and loosen grip to gain control. They can be very powerful if used right. Mazvidal knocked out a guy in under 5 seconds with a flying knee to the forehead. That was the only time I recognized seeing a knockout from a knee. It can cut the face and make the nose or lips bleed if hit with the knee.
We are going to run this script first, but the notes do keep track of which arm was used when making a cross, knee, or other strike other than takedown. Left is L and right is R before the name of the action/strike used and after either lands or misses.
As the extracted actions are listed, there is not an extracted description of which arm was used for the strike or action. There are also some types of wrestling moves noted in the notes, but the entirety of the observations didn’t continually list the hold if still in a specific hold that second, only any changes observed. We can add in these features at the end.
Here is a brief description pulled from Wikipedia on BJJ moves that aren’t extracted as features but could be useful:
Guard: person dominating on back with legs around opponent’s waste, controls top opponent
half Guard: person dominating on back controling with one leg of the top opponent preventing the opponent from passing or gaining side control
open Guard: variations where person is on back but legs not wrapped around top opponent’s waste, preventing the top opponent from striking or passing with feet or shins
side control: top person dominates at the side pinning shoulders and/or hips while striking and trying to gain a lock or choke
full mount: person on top dominates with legs above person on ground’s hips and knees in armpits to prevent bottom opponent from striking or trying to gain control
back mount: ankles in thighs of person who is being dominated by person on back to maneuver a choke or lock
Aside: Having experienced this myself I know that there are things that happen in the hold that aren’t illustrated to the viewers. Such as, if the person locks there legs around the rib cage, they are squeezing in an attempt to break their ribs or lower their ability to breathe with limited lung cavity movement, and the idea is to cut off the carotid with a foot, ankle arm and your arm when choking until the person taps out of passes out. FYI, experience matters. Having had 3 months training and going up against someone with 2 years experience and a height advantage I did not last longer than 59 seconds in limited mma, where punches to the face are illegal. FYI I was 27 and the opponent was 14 and both of us females at that time (I plan on dying a female with no changes in that feature until that finite point in time). Do not underestimate age. Also, it takes quite a bit of maneuvering to get out of a hold once locked. Also, too much wrestling leaves viewers who want to see hits or strikes and movement on the floor towards the opponent and not dancing around the ring to avoid opponent is how these fighters make a name for themselves. In other words, these fighters have to be to some extent bat crazy.
library(dplyr)felicia <- read.csv('Felicia3fights.csv', header=TRUE, sep=',',
na.strings=c('','NA'))
colnames(felicia)## [1] "Round" "SecondsIntoRound"
## [3] "SecondsLastRoundAction" "cmTotHitsR.X1"
## [5] "cmTotHitsL.X1" "cmTotHitsM.X1"
## [7] "Hits.Recvd.X1" "Hits.Lnd.X1"
## [9] "Hits.Mssd.X1" "cmTotHitsR.X2"
## [11] "cmTotHitsL.X2" "cmTotHitsM.X2"
## [13] "Hits.Recvd.X2" "Hits.Lnd.X2"
## [15] "Hits.Mssd.X2" "Time"
## [17] "FighterActionReactions.X1" "FightersActionsReactions.X2"
## [19] "Notes"Remove the instances with no action from either fighter X1 or X2.
Added <- filter(felicia, felicia$FighterActionReactions.X1 !=0 | felicia$FightersActionsReactions.X2 !=0)Look at the notes on each fighter. The X1 is Felicia, the X2 is one of the three opponent’s.
head(Added$FighterActionReactions.X1, 10)## [1] missed L jab <NA>
## [3] <NA> missed R cross, missed L jab
## [5] missed L jab missed R cross
## [7] <NA> missed L mt kick to low leg
## [9] <NA> misses R cross
## 134 Levels: breaks body hold and holding full mount hold continues ...head(Added$FightersActionsReactions.X2,10)## [1] <NA> missed R cross missed R cross <NA> <NA>
## [6] missed L cross missed L hook <NA> misses L jab <NA>
## 91 Levels: blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold ...List the unique notes for X1.
unique(Added$FighterActionReactions.X1)## [1] missed L jab
## [2] <NA>
## [3] missed R cross, missed L jab
## [4] missed R cross
## [5] missed L mt kick to low leg
## [6] misses R cross
## [7] misses L jab
## [8] lands R push kick to upper body
## [9] lands L mt kick to inside leg
## [10] misses L jab to face
## [11] missed R cross
## [12] misses attempted clinch
## [13] misses L hook
## [14] misses L mt kick to upper body and caught in L foot hold
## [15] breaks L foot hold
## [16] lands L jab to body
## [17] misses body takedown
## [18] holding upper body hold starts and caught in L arm hold
## [19] breaks L arm hold and caught in R arm hold while holding upper body continues
## [20] caught in R arm hold while holding upper body hold
## [21] lands L knee to back of L leg while caught in R arm hold while holding upper body hold
## [22] lands judo type takedown while caught in R arm hold and while holding upper body
## [23] mount attempt and holding full mount hold starts
## [24] caught in R arm hold while holding full mount hold
## [25] lands L hook to head while caught in R arm hold and holding full mount hold
## [26] breaks R arm hold while holding full mount hold
## [27] holding full mount hold continues
## [28] lands elbow to face holding full mount hold continues
## [29] lands R hook to face holding full mount hold continues
## [30] caught in body hold and holding full mount hold continues
## [31] breaks body hold and holding full mount hold continues
## [32] breaks body hold and lands L elbow to face and holding full mount hold continues
## [33] lands L cross to face and holding full mount hold continues
## [34] holding full mount hold continues and lands R elbow to face, lands R elbow to face
## [35] lands R elbow to face and holding full mount hold continues
## [36] lands L elbow to face and holding full mount hold continues
## [37] loses full mount hold and holding side mount hold starts
## [38] holding side mount hold continues
## [39] holding back mount hold starts and loses side mount hold
## [40] holding back mount hold continues
## [41] misses L hook to face holding back mount hold continues
## [42] loses back mount hold and holding full mount hold starts
## [43] holding full mount hold on top and caught in upper body hold
## [44] lands R hook to face holding full mount hold continues and caught in upper body hold
## [45] lands L elbow to face holding full mount hold continues and caught in upper body hold
## [46] holding full mount hold continues and caught in upper body hold
## [47] loses full mount hold and holding side control mount starts and breaks upper body hold
## [48] lands L hammer to face holding side mount hold continues
## [49] loses side mount hold and holding full mount hold starts
## [50] holding full mount hold lands R hook to face
## [51] holding full mount hold
## [52] holding full mount hold lands L hook to face
## [53] holding full mount hold misses L elbow to face
## [54] holding full mount hold misses L elbow to face, misses L elbow to face
## [55] TKO referree stoppage
## [56] testing L push kick
## [57] testing L mt kick to low leg
## [58] misses L push kick
## [59] lands R cross
## [60] misses L mt kick to face, misses R cross
## [61] misses R push kick
## [62] misses upper body takedown and holding upper body hold starts
## [63] lands L knee to body holding upper body hold continues
## [64] holding upper body hold continues
## [65] holding upper body hold continues and caught in R arm hold
## [66] pushes against cage holding upper body hold continues and caught in R arm hold
## [67] lands R knee to L leg holding upper body hold continues and caught in R arm hold, lands L knee to inner leg
## [68] lands R knee to inner leg holding upper body hold continues and caught in R arm hold, lands L knee to inner leg
## [69] lands R knee to inner leg and holding upper body hold continues and caught in R arm hold
## [70] lands R knee to outer leg and holding upper body hold continues and caught in R arm hold
## [71] breaks R arm hold and loses upper body hold
## [72] lands judo type takedown
## [73] moves out attempting a ground takedown flip
## [74] holding under shoulders and upper body hold starts on ground
## [75] holding upper body continues
## [76] jumps on back losing upper body hold and holding back mount hold starts
## [77] sinks feet in thighs while holding back mount hold continues
## [78] holding back mount hold continues and holding L underarm choke hold starts
## [79] flips forward taking the back, lands on side holding back mount hold continues and holding L underarm choke hold continues
## [80] grabs under arms holding back mount hold continues and holding L underarm choke hold continues
## [81] holding back mount hold continues and holding L underarm choke hold continues
## [82] holding back mount hold continues and loses L underarm choke hold
## [83] holding back mount hold
## [84] holding back mount hold and holding R underarm choke hold starts
## [85] holding back mount continues on top from on back
## [86] holding back mount
## [87] loses back mount hold and holding side mount hold starts
## [88] misses elbow holding side mount hold continues
## [89] loses side mount hold and holding back mount hold starts
## [90] lands R hammer hit holding back mount hold continues, lands R hammer hit
## [91] loses back mount hold and holding back hold starts
## [92] holding back hold continues
## [93] lands R hook holding back hold continues, lands R hook
## [94] loses back hold and holding back mount hold starts
## [95] lands R hook holding back mount hold continues, lands R hook
## [96] lands R hook holding back mount hold continues
## [97] holding back mount hold continues and holding neck hold starts
## [98] lands R hook holding back mount hold continues and holding neck hold continues
## [99] holding back mount hold continues and holding neck hold continues
## [100] holding neck choke hold starts while holding back mount hold continues and loses neck hold
## [101] holding neck choke hold starts while holding back mount hold continues
## [102] wins by submission
## [103] lands R mt kick
## [104] grabs leg, misses single leg takedown
## [105] missed L knee to body, missed L to face
## [106] lands flying elbow to face
## [107] missed R cross to face
## [108] lands R push kick
## [109] grabs head against cage
## [110] pushes against cage
## [111] misses takedown holding upper bodyhold starts and caught in R arm hold
## [112] caught in R arm hold and holding upper body hold continues
## [113] lands L knee to inner R upper leg while caught in R arm hold and holding upper body hold continues
## [114] lands R knee to inner R leg caught in R arm hold and holding upper body hold continues
## [115] lands R knee to outer L leg caught in R arm hold and holding upper body hold continues
## [116] gets up holding upper body hold continues and caught in R arm hold
## [117] breaks R arm hold caught in L arm hold and holding upper body hold continues
## [118] caught in L arm hold and holding upper body hold continues
## [119] breaks L arm hold and caught in R arm hold while holding upper body hold continues
## [120] lands R knee to inner R leg and caught in R arm hold and holding upper body hold continues
## [121] lands L hook to head and caught in R arm hold and holding upper body hold continues
## [122] misses L hook to face
## [123] falls
## [124] gets up
## [125] misses flying elbow to face, misses R uppercut to face
## [126] misses R jab
## [127] misses attempted takedown
## [128] misses takedown
## [129] lands L flying elbow to face
## [130] lands R elbow to face
## [131] holding upper body hold starts
## [132] misses backwards upward elbow to face while holding upper body hold continues and caught in R arm hold
## [133] lands R knee to inner R leg holding upper body hold continues and caught in R arm hold
## [134] misses single leg takedown attempt while holding upper body hold continues and caught in R arm hold
## [135] pulls off opponent's R hand blocking airway with L hand while holding upper body hold continues and caught in R arm hold
## 134 Levels: breaks body hold and holding full mount hold continues ...There are 134 unique actions for Felicia amongst these three opponents. Out of how many observations?
dim(Added)## [1] 529 19There are 524 observations in the data that excludes observations with no noticeable action or reaction from X1 or X2.
The unique actions/reactions from the opponent’s separately but collectively are:
unique(Added$FightersActionsReactions.X2)## [1] <NA>
## [2] missed R cross
## [3] missed L cross
## [4] missed L hook
## [5] misses L jab
## [6] missed L jab
## [7] missed L hook to face
## [8] misses L hook to face
## [9] landed jab to face
## [10] holding L foot hold starts while pushing back into cage
## [11] loses L foot hold
## [12] lands L jab to body
## [13] caught in upper body hold and holding L arm hold starts
## [14] loses L arm hold and holding R arm hold starts while caught in upper body hold
## [15] holding R arm hold while caught in upper body hold
## [16] on side preventing full mount and passing while caught in full mount hold and holding R arm hold continues
## [17] holding R arm while caught in full mount hold
## [18] loses R arm hold while caught in full mount hold
## [19] caught in full mount hold
## [20] caught in full mount hold and holding body hold starts
## [21] caught in full mount hold and loses body hold
## [22] breaks full mount hold and caught in side mount hold
## [23] caught in side mount hold
## [24] caught in back mount hold
## [25] breaks back mount hold and caught in full mount hold
## [26] caught in full mount hold and holding upper body hold continues
## [27] breaks full mount hold and caught in side mount hold and loses upper body hold
## [28] breaks side mount hold and caught in full mount hold
## [29] misses L jab, misses L jab
## [30] misses single leg takedown with L leg catch pulling up to throw off balance
## [31] misses L knee to body
## [32] misses R cross, misses L cross
## [33] caught in upper body hold
## [34] caught in upper body hold and holding R arm
## [35] breaks out of upper body hold and loses R arm hold
## [36] on top shoulders
## [37] breaks upper body hold and caught in back mount hold
## [38] caught in back mount hold and caught in L underarm choke hold
## [39] protects L underarm from choke and caught in back mount hold and caught in L underarm choke hold
## [40] caught in back mount hold and breaks L underarm choke hold
## [41] caught in back mount hold and caught in R underarm choke hold
## [42] breaks back mount hold and caught in side mount hold
## [43] breaks side mount hold and caught in back mount hold
## [44] breaks back mount hold and caught in back hold getting up on knees pressing opponent's L thigh against cage
## [45] caught in back hold
## [46] breaks back hold and caught inback mount hold
## [47] caught in back mount hold and slams opponent forward and down over her head while crouched with opponent holding her back
## [48] caught in back mount hold and caught in neck hold
## [49] caught in back mount hold and caught in neck choke hold and breaks neck hold
## [50] caught in back mount hold and caught in neck choke hold
## [51] taps out
## [52] missed jab
## [53] missed jab, missed jab
## [54] misses R cross
## [55] misses L mt kick to body
## [56] lands R cross to face
## [57] missed R mt kick to head
## [58] missed L jab to face, missed R cross to face
## [59] lands L hook to face
## [60] missed L jab to face
## [61] misses R cross, lands L jab
## [62] misses R hook to head
## [63] holding R arm hold starts and caught in upper body hold lands R uppercut to face
## [64] holding R arm hold continues and caught in upper body hold
## [65] lands R hook to head holding R arm hold continues and caught in upper body hold
## [66] lands R knee to L body holding R arm hold continues and caught in upper body hold
## [67] lands takedown holding R arm hold and caught in upper body hold
## [68] lands L knee to body while holding R arm hold and caught in upper body hold
## [69] loses R arm hold holding L arm starts
## [70] holding L arm hold continues and caught in upper body hold
## [71] caught in upper body hold and loses L arm hold , holding R arm hold starts
## [72] holding R arm hold and caught in upper body hold
## [73] loses R arm hold and breaks upper body hold
## [74] lands R knee to body, lands L jab to face
## [75] lands R cross to face, lands L jab to face
## [76] lands L hook to head
## [77] lands L jab, lands R cross, misses R cross
## [78] lands L mt kick to low L leg
## [79] lands R mt kick to leg
## [80] misses L cross, misses R cross
## [81] misses L mt kick
## [82] misses R mt kick to body
## [83] lands L jab to face
## [84] misses L cross
## [85] lands L mt kick to inner low leg
## [86] lands L cross
## [87] holding R arm hold starts and caught in upper body hold
## [88] misses L knee to body and holding R arm hold continues and caught in upper body hold
## [89] holding R arm hold continues and caught in upper body hold
## [90] lands R knee to body holding R arm hold continues and caught in upper body hold
## [91] blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold
## [92] lands R knee to body holding R arm hold continues and caught in upper body hold
## 91 Levels: blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold ...There are 92 unique actions from these three opponent’s as X2, in this case the opponents are Zarah, Megan Anderson, and Cyborg.
Get fighter X1’s list of actions and reactions to split on.
Sym <- strsplit(as.character(Added$FighterActionReactions.X1), ',')Create 1st sequence to grab actions from the table by index of observation that the action occured; this grabs the index of occurence in the table from each observation as a vector.
sq1 <- lapply(Sym,'[',1)
head(sq1,10)## [[1]]
## [1] "missed L jab"
##
## [[2]]
## [1] NA
##
## [[3]]
## [1] NA
##
## [[4]]
## [1] "missed R cross"
##
## [[5]]
## [1] "missed L jab"
##
## [[6]]
## [1] "missed R cross "
##
## [[7]]
## [1] NA
##
## [[8]]
## [1] "missed L mt kick to low leg"
##
## [[9]]
## [1] NA
##
## [[10]]
## [1] "misses R cross"sq2 <- lapply(Sym,'[',2)
sq3 <- lapply(Sym,'[',3)Lets also get the ground moves added to our data as features. The ground moves are the ‘holding…hold’ for the fighter holding some hold or body part as either an arm, back, full mount, back mount, etc. There is also a note of when the fighter ‘loses…hold’ or stops holding that was added but doesn’t mean it isn’t ground work done or submitted but that in some instances switched from a full mount hold to a side mount control hold, of lost back mount hold to holding back when legs not fully locked, etc. There is also the options for the opponent who ‘breaks…hold’ or ‘caught in…hold’ to add to this data for machine learning. I also want to distinguish between muay thai (mt) and push kick instead of just counting all kicks. The ground work was intentionally added to the first sequence before the comma separator, so we just need to add it as a grab from each fighter. But there are multiple holds in some instances and breaks, caught, and lost holds. So we need to account for the multiple holds.
hold <- grep('holding.+hold',sq1)
hold## [1] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
## [19] 45 46 47 48 49 50 51 52 53 54 55 56 58 59 60 61 62 63
## [37] 64 65 66 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82
## [55] 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
## [73] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
## [91] 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
## [109] 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154
## [127] 155 156 157 158 159 160 161 162 163 164 165 166 167 168 177 178 179 180
## [145] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [163] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [181] 217 218 219 220 221 222 223 224 225 226 227 232 235 236 237 238 239 240
## [199] 241 242 243 244 246 247 248 249 250 251 252 253 254 255 256 257 258 259
## [217] 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277
## [235] 278 279 280 281 282 285 286 287 288 289 290 291 292 293 294 295 296 297
## [253] 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315
## [271] 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333
## [289] 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351
## [307] 352 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389
## [325] 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407
## [343] 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425
## [361] 426 427 428 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478
## [379] 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
## [397] 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514
## [415] 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529There are a lot of instances for this fighter that involves ground work, clinches or holds while standing, and wrestling from the number of instances that a hold is used by this fighter. Now look at the number of instances this fighter is caught in a hold.This can be an arm hold when clinching while standing up or on the ground when the opponent grabs the arm to prevent getting hit by it or prevent a wrestling submission.
caught <- grep('caught.*hold',sq1)
caught## [1] 22 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
## [19] 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
## [37] 62 63 64 65 66 67 69 70 71 72 73 74 75 76 77 78 79 80
## [55] 81 82 83 84 85 86 87 88 89 99 105 106 139 140 141 142 143 144
## [73] 145 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199
## [91] 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217
## [109] 218 219 220 221 222 223 224 225 226 227 373 374 375 376 377 378 379 380
## [127] 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398
## [145] 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
## [163] 417 418 419 420 421 422 423 424 425 426 427 428 468 469 470 471 472 473
## [181] 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491
## [199] 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509
## [217] 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527
## [235] 528 529There aren’t that many instances that the fighter is caught in a hold compared to holding the opponent.
Lets look at the lost holds.
lost <- grep('loses.*hold', sq1)
lost## [1] 132 134 138 146 152 228 254 285 289 297 322 347 429Not many holds were lost or quit by this fighter.
Now for the number of holds this fighter broke out of or the opponent quit holding.
breaks <- grep('breaks.*hold',sq1)
breaks## [1] 23 57 90 100 107 146 228 416 425 429There were some holds this fighter broke out of. They could have been quit by the opponent, and some are arm or limb holds of some sort.
Now lets check out the number of muay thai kicks and push kicks separately in the first sequence of each instance.
mtKicks <- grep('mt kick',sq1)
pushKicks <- grep('push kick',sq1)
mtKicks;pushKicks## [1] 8 14 22 172 175 357## [1] 13 171 173 176 369 445Lets look at the right (R) or left (L) legs used to deliver the muay thai kicks or push kicks separately.
LmtKicks <- grep('L mt kick',sq1)
RmtKicks <- grep('R mt kick',sq1)
Lpush <- grep('L push kick', sq1)
Rpush <- grep('R push kick', sq1)
LmtKicks;RmtKicks;Lpush;Rpush## [1] 8 14 22 172 175## [1] 357## [1] 171 173## [1] 13 176 369 445For muay thai kicks this fighter prefers the left leg to deliver them, and for push kicks this fighter prefers using the right leg. And this is only in the start of a second in time of a three sequence max second of time as an observation.
Lets look at the muay thai and push kicks by X1 in the 2nd and 3rd sequences as well to get an idea of how this fighter rates their own kicks in sequential order or usefulness.
mtKicks2 <- grep('mt kick',sq2)
pushKicks2 <- grep('push kick',sq2)
mtKicks2;pushKicks2## integer(0)## integer(0)mtKicks3 <- grep('mt kick',sq3)
pushKicks3 <- grep('push kick',sq3)
mtKicks3;pushKicks3## integer(0)## integer(0)This fighter X1 didn’t use any kicks in the 2nd and 3rd sequence, so there is no point in seeing which ones are delivered with the right or left leg.
Lets now look at the opponent’s or X2’s preference in the first second as an action or reaction by starting with the number of holds that X2 used.
This creates a list of the first sequence of three to select actions from for X2.
SymX2 <- strsplit(as.character(paste(Added$FightersActionsReactions.X2)),',')
sq1X2 <- lapply(SymX2,'[',1)
sq2X2 <- lapply(SymX2,'[',2)
sq3X2 <- lapply(SymX2,'[',3)holdX2 <- grep('holding.+hold',sq1X2)
holdX2## [1] 22 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
## [19] 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
## [37] 62 63 64 65 66 68 69 70 71 72 73 74 75 76 77 78 79 80
## [55] 81 82 83 84 85 86 87 88 89 99 105 106 139 140 141 142 143 144
## [73] 145 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389
## [91] 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407
## [109] 408 409 410 411 412 413 414 415 417 418 419 420 421 422 423 424 426 427
## [127] 428 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484
## [145] 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502
## [163] 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520
## [181] 521 522 523 524 525 526 527 528 529The opponent’s acting as X2 didn’t use as many holds as this X1 fighter did.
Lets see the number of holds X2 was caught in.
caughtX2 <- grep('caught.*hold',sq1X2)
caughtX2## [1] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
## [19] 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62
## [37] 63 64 65 66 68 69 70 71 72 73 74 75 76 77 78 79 80 81
## [55] 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
## [73] 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117
## [91] 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
## [109] 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153
## [127] 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 177 178
## [145] 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196
## [163] 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214
## [181] 215 216 217 218 219 220 221 222 223 224 225 226 227 232 233 234 235 236
## [199] 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254
## [217] 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
## [235] 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290
## [253] 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308
## [271] 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326
## [289] 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344
## [307] 345 346 347 348 349 350 351 352 373 374 375 376 377 378 379 380 381 382
## [325] 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## [343] 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 417 418 419
## [361] 420 421 422 423 424 425 426 427 428 464 465 466 467 468 469 470 471 472
## [379] 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490
## [397] 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508
## [415] 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526
## [433] 527 528 529The opponent’s were caught in many holds from X1, as we expect to see because X1 had a lot of holds initiated for many instances.
Lets look at the lost holds by X2.
lostX2 <- grep('loses.*hold', sq1X2)
lostX2## [1] 23 57 90 100 107 146 228 416 425 429The opponents lost some of the holds they had, but not all of them on X1.
Now for the number of holds that x2 broke out of or the opponent quit holding.
breaksX2 <- grep('breaks.*hold',sq1X2)
breaksX2## [1] 132 138 146 152 228 235 254 285 289 297 322 323 324 325 347 429The opponents broke out of quite a bit of holds. Some are mounts and moving up to the striking instead of wrestling ground work.
Now lets check out the number of muay thai kicks and push kicks separately in the first sequence of each instance that X2 used.
mtKicksX2 <- grep('mt kick',sq1X2)
pushKicksX2 <- grep('push kick',sq1X2)
mtKicksX2;pushKicksX2## [1] 357 358 362 436 440 448 451 456 461## integer(0)None of these opponents used any push kicks, but did use some muay thai kicks in the first sequence. Keep in mind there are two other sequences that kicks could have been delivered, like in an open guard, where the opponent X2 uses an ‘upward kick’ which could be considered a push kick. Lets see.
openPushX2 <- grep('upward.*kick',sq1X2)
openPushX2## integer(0)But not for these opponents, in the first sequence any how. How about in the 2nd and 3rd sequences, did X2 use any muay thai or push kicks?
mtKicksX2b <- grep('mt kick',sq2X2)
pushKicksX2b <- grep('push kick',sq2X2)
mtKicksX2b;pushKicksX2b## integer(0)## integer(0)mtKicksX2c <- grep('mt kick',sq3X2)
pushKicksX2c <- grep('push kick',sq3X2)
mtKicksX2c;pushKicksX2c## integer(0)## integer(0)No kicks by X2 in the 2nd or 3rd sequences of any instance.
Lets look at the right (R) or left (L) legs used by X2 to deliver the muay thai kicks.
LmtKicksX2 <- grep('L mt kick',sq1X2)
RmtKicksX2 <- grep('R mt kick',sq1X2)
LmtKicksX2;RmtKicksX2## [1] 357 358 436 448 456## [1] 362 440 451 461It was almost an equal amount of muay thai kicks by all opponents in left or right leg used, but the majority of muay thai kicks delivered to X1 by X2 (3 different opponents separately) used the left leg. This could be toward the lead leg to lessen the stability of X1 or knock off balance to reduce striking power delivered by X1’s right cross, or to the head or body. Lets see.
sq1X2[LmtKicksX2]## [[1]]
## [1] "misses L mt kick to body"
##
## [[2]]
## [1] "misses L mt kick to body"
##
## [[3]]
## [1] "lands L mt kick to low L leg"
##
## [[4]]
## [1] "misses L mt kick"
##
## [[5]]
## [1] "lands L mt kick to inner low leg"It looks like the opponents are equal in landed muay thai kicks to the inner or L low leg, for this X1’s lead leg is the left (L) leg, or the body.
This will grab the indices of each specific action grep’d from the action text fields to count each action. We will do this for up to three sequences of actions/reactions in a second. An example of a few of the action vectors are displayed.
kicks_sq1 <- grep('land.*kick', sq1)
elbows_sq1 <- grep('land.*elbow', sq1)
knees_sq1 <- grep('land.*knee', sq1)
jab_sq1 <- grep('land.*jab', sq1)
cross_sq1 <- grep('land.*cross', sq1)
hook_sq1 <- grep('land.*hook', sq1)
upper_sq1 <- grep('land.*uppercut', sq1)
takedown_sq1 <- grep('land.*takedown', sq1)
hammer_sq1 <- grep('land.*hammer', sq1)
hammer_sq1;takedown_sq1;upper_sq1;cross_sq1## [1] 149 292 293## [1] 67 229## integer(0)## [1] 112 129 174Missed in 1st sequence; Blocks aren’t accounted for and when missed, it is assumed because the opponent was blocking or ducking, not that the fighter didn’t land the hit but because it was blocked either with a block an opposing strike, grab, or ducked.
kicks_sq1m <- grep('miss.*kick', sq1)
elbows_sq1m <- grep('miss.*elbow', sq1)
knees_sq1m <- grep('miss.*knee', sq1)
jab_sq1m <- grep('miss.*jab', sq1)
cross_sq1m <- grep('miss.*cross', sq1)
hook_sq1m <- grep('miss.*hook', sq1)
upper_sq1m <- grep('miss.*upp', sq1)
takedown_sq1m <- grep('miss.*takedown', sq1)
hammer_sq1m <- grep('miss.*hammer', sq1)landed in second sequence
kicks_sq2 <- grep('land*kick', sq2)
elbows_sq2 <- grep('land.*elbow', sq2)
knees_sq2 <- grep('land.*knee', sq2)
jab_sq2 <- grep('land.*jab', sq2)
cross_sq2 <- grep('land.*cross', sq2)
hook_sq2 <- grep('land.*hook', sq2)
upper_sq2 <- grep('land.*upp', sq2)
takedown_sq2 <- grep('land.*takedown', sq2)
hammer_sq2 <- grep('land.*hammer', sq2)missed in 2nd sequence
kicks_sq2m <- grep('miss.*kick', sq2)
elbows_sq2m <- grep('miss.*elbow', sq2)
knees_sq2m <- grep('miss.*knee', sq2)
jab_sq2m <- grep('miss.*jab', sq2)
cross_sq2m <- grep('miss.*cross', sq2)
hook_sq2m <- grep('miss.*hook', sq2)
upper_sq2m <- grep('miss.*upp', sq2)
takedown_sq2m <- grep('miss.*takedown', sq2)
hammer_sq2m <- grep('miss.*hammer', sq2)landed in 3rd sequence
kicks_sq3 <- grep('land.*kick', sq3)
elbows_sq3 <- grep('land.*elbow', sq3)
knees_sq3 <- grep('land.*knee', sq3)
jab_sq3 <- grep('land.*jab', sq3)
cross_sq3 <- grep('land.*cross', sq3)
hook_sq3 <- grep('land.*hook', sq3)
upper_sq3 <- grep('land.*upp', sq3)
takedown_sq3 <- grep('land.*takedown', sq3)
hammer_sq3 <- grep('land.*hammer', sq3)missed in 3rd sequence
kicks_sq3m <- grep('miss.*kick', sq3)
elbows_sq3m <- grep('miss.*elbow', sq3)
knees_sq3m <- grep('miss.*knee', sq3)
jab_sq3m <- grep('miss.*jab', sq3)
cross_sq3m <- grep('miss.*cross', sq3)
hook_sq3m <- grep('miss.*hook', sq3)
upper_sq3m <- grep('miss.*upp', sq3)
takedown_sq3m <- grep('miss.*takedown', sq3)
hammer_sq3m <- grep('miss.*hammer', sq3)get fighter2’s list of actions/reactions
sq1b <- sq1X2lands 1st sequence X2, ends with ‘b’, no ‘l’ to either for lands
kicks_sq1b <- grep('land.*kick', sq1b)
elbows_sq1b <- grep('land.*elbow', sq1b)
knees_sq1b <- grep('land.*knee', sq1b)
jab_sq1b <- grep('land.*jab', sq1b)
cross_sq1b <- grep('land.*cross', sq1b)
hook_sq1b <- grep('land.*hook', sq1b)
upper_sq1b <- grep('land.*upp', sq1b)
takedown_sq1b <- grep('land.*takedown', sq1b)
hammer_sq1b <- grep('land.*hammer', sq1b)received by X1 in 1st sequence, duplicated above as equivalent to hits landed 1st seq of x2
kicks_sq1r <- grep('land.*kick', sq1b)
elbows_sq1r <- grep('land.*elbow', sq1b)
knees_sq1r <- grep('land.*knee', sq1b)
jab_sq1r <- grep('land.*jab', sq1b)
cross_sq1r <- grep('land.*cross', sq1b)
hook_sq1r <- grep('land.*hook', sq1b)
upper_sq1r <- grep('land.*upp', sq1b)
takedown_sq1r <- grep('land.*takedown', sq1b)
hammer_sq1r <- grep('land.*hammer', sq1b)missed in 1st sequence X2
kicks_sq1bm <- grep('miss.*kick', sq1b)
elbows_sq1bm <- grep('miss.*elbow', sq1b)
knees_sq1bm <- grep('miss.*knee', sq1b)
jab_sq1bm <- grep('miss.*jab', sq1b)
cross_sq1bm <- grep('miss.*cross', sq1b)
hook_sq1bm <- grep('miss.*hook', sq1b)
upper_sq1bm <- grep('miss.*upp', sq1b)
takedown_sq1bm <- grep('miss.*takedown', sq1b)
hammer_sq1bm <- grep('miss.*hammer', sq1b)received by x2 in 1st seq equivalent to lands by x1
kicks_sq1br <- grep('land.*kick', sq1)
elbows_sq1br <- grep('land.*elbow', sq1)
knees_sq1br <- grep('land.*knee', sq1)
jab_sq1br <- grep('land.*jab', sq1)
cross_sq1br <- grep('land.*cross', sq1)
hook_sq1br <- grep('land.*hook', sq1)
upper_sq1br <- grep('land.*upp', sq1)
takedown_sq1br <- grep('land.*takedown', sq1)
hammer_sq1br <- grep('land.*hammer', sq1)lands 2nd sequence x2
sq2b <- sq2X2kicks_sq2b <- grep('land.*kick', sq2b)
elbows_sq2b <- grep('land.*elbow', sq2b)
knees_sq2b <- grep('land.*knee', sq2b)
jab_sq2b <- grep('land.*jab', sq2b)
cross_sq2b <- grep('land.*cross', sq2b)
hook_sq2b <- grep('land.*hook', sq2b)
upper_sq2b <- grep('land.*upp', sq2b)
takedown_sq2b <- grep('land.*takedown', sq2b)
hammer_sq2b <- grep('land.*hammer', sq2b)received by X1 in 2nd sequence equivalent to hits landed by x2 seq 2
kicks_sq2r <- grep('land.*kick', sq2b)
elbows_sq2r <- grep('land.*elbow', sq2b)
knees_sq2r <- grep('land.*knee', sq2b)
jab_sq2r <- grep('land.*jab', sq2b)
cross_sq2r <- grep('land.*cross', sq2b)
hook_sq2r <- grep('land.*hook', sq2b)
upper_sq2r <- grep('land.*upp', sq2b)
takedown_sq2r <- grep('land.*takedown', sq2b)
hammer_sq2r <- grep('land.*hammer', sq2b)missed in 2nd sequence x2
kicks_sq2bm <- grep('miss.*kick', sq2b)
elbows_sq2bm <- grep('miss.*elbow', sq2b)
knees_sq2bm <- grep('miss.*knee', sq2b)
jab_sq2bm <- grep('miss.*jab', sq2b)
cross_sq2bm <- grep('miss.*cross', sq2b)
hook_sq2bm <- grep('miss.*hook', sq2b)
upper_sq2bm <- grep('miss.*upp', sq2b)
takedown_sq2bm <- grep('miss.*takedown', sq2b)
hammer_sq2bm <- grep('miss.*hammer', sq2b)received 2nd seq by x2 equivalent to hits landed by x1 in seq 2
kicks_sq2br <- grep('land.*kick', sq2)
elbows_sq2br <- grep('land.*elbow', sq2)
knees_sq2br <- grep('land.*knee', sq2)
jab_sq2br <- grep('land.*jab', sq2)
cross_sq2br <- grep('land.*cross', sq2)
hook_sq2br <- grep('land.*hook', sq2)
upper_sq2br <- grep('land.*upp', sq2)
takedown_sq2br <- grep('land.*takedown', sq2)
hammer_sq2br <- grep('land.*hammer', sq2)lands 3rd sequence x2
sq3b <- sq3X2kicks_sq3b <- grep('land.*kick', sq3b)
elbows_sq3b <- grep('land.*elbow', sq3b)
knees_sq3b <- grep('land.*knee', sq3b)
jab_sq3b <- grep('land.*jab', sq3b)
cross_sq3b <- grep('land.*cross', sq3b)
hook_sq3b <- grep('land.*hook', sq3b)
upper_sq3b <- grep('land.*upp', sq3b)
takedown_sq3b <- grep('land.*takedown', sq3b)
hammer_sq3b <- grep('land.*hammer', sq3b)received by X1 in 3rd sequence equivalent to hits landed by X2 in seq 3
kicks_sq3r <- grep('land.*kick', sq3b)
elbows_sq3r <- grep('land.*elbow', sq3b)
knees_sq3r <- grep('land.*knee', sq3b)
jab_sq3r <- grep('land.*jab', sq3b)
cross_sq3r <- grep('land.*cross', sq3b)
hook_sq3r <- grep('land.*hook', sq3b)
upper_sq3r <- grep('land.*upp', sq3b)
takedown_sq3r <- grep('land.*takedown', sq3b)
hammer_sq3r <- grep('land.*hammer', sq3b)missed in 3rd sequence x2
kicks_sq3bm <- grep('miss.*kick', sq3b)
elbows_sq3bm <- grep('miss.*elbow', sq3b)
knees_sq3bm <- grep('miss.*knee', sq3b)
jab_sq3bm <- grep('miss.*jab', sq3b)
cross_sq3bm <- grep('miss.*cross', sq3b)
hook_sq3bm <- grep('miss.*hook', sq3b)
upper_sq3bm <- grep('miss.*upp', sq3b)
takedown_sq3bm <- grep('miss.*takedown', sq3b)
hammer_sq3bm <- grep('miss.*hammer', sq3b)received in seq 3 by x2 equivalent to hits landed by x1 in seq3
kicks_sq3br <- grep('land.*kick', sq3)
elbows_sq3br <- grep('land.*elbow', sq3)
knees_sq3br <- grep('land.*knee', sq3)
jab_sq3br <- grep('land.*jab', sq3)
cross_sq3br <- grep('land.*cross', sq3)
hook_sq3br <- grep('land.*hook', sq3)
upper_sq3br <- grep('land.*upp', sq3)
takedown_sq3br <- grep('land.*takedown', sq3)
hammer_sq3br <- grep('land.*hammer', sq3)This adds the fields (54 fields for each sequence) to the table of actions per second, by creating table extensions of Added, then renaming the 3rd sequence of actions.
added_landed <- mutate(Added, Crossl.X1=0, Kneel.X1=0, Elbowl.X1=0, Hookl.X1=0, Jabl.X1=0, Kickl.X1=0,
Crossl.X2=0, Kneel.X2=0, Elbowl.X2=0, Hookl.X2=0, Jabl.X2=0, Kickl.X2=0, upperl.X1=0,
upperl.X2=0, takedownl.X1=0, takedownl.X2=0, hammerl.X1=0, hammerl.X2=0
, Cross2l.X1=0, Knee2l.X1=0, Elbow2l.X1=0, Hook2l.X1=0, Jab2l.X1=0, Kick2l.X1=0,
Cross2l.X2=0, Knee2l.X2=0, Elbow2l.X2=0, Hook2l.X2=0, Jab2l.X2=0, Kick2l.X2=0, upper2l.X1=0,
upper2l.X2=0, takedown2l.X1=0, takedown2l.X2=0, hammer2l.X1=0, hammer2l.X2=0
, Cross3l.X1=0, Knee3l.X1=0, Elbow3l.X1=0, Hook3l.X1=0, Jab3l.X1=0, Kick3l.X1=0,
Cross3l.X2=0, Knee3l.X2=0, Elbow3l.X2=0, Hook3l.X2=0, Jab3l.X2=0, Kick3l.X2=0, upper3l.X1=0,
upper3l.X2=0, takedown3l.X1=0, takedown3l.X2=0, hammer3l.X1=0, hammer3l.X2=0)
added_missed <- mutate(added_landed, Crossm.X1=0, Kneem.X1=0, Elbowm.X1=0, Hookm.X1=0, Jabm.X1=0, Kickm.X1=0,
Crossm.X2=0, Kneem.X2=0, Elbowm.X2=0, Hookm.X2=0, Jabm.X2=0, Kickm.X2=0, upperm.X1=0,
upperm.X2=0, takedownm.X1=0, takedownm.X2=0, hammerm.X1=0, hammerm.X2=0
, Cross2m.X1=0, Knee2m.X1=0, Elbow2m.X1=0, Hook2m.X1=0, Jab2m.X1=0, Kick2m.X1=0,
Cross2m.X2=0, Knee2m.X2=0, Elbow2m.X2=0, Hook2m.X2=0, Jab2m.X2=0, Kick2m.X2=0, upper2m.X1=0,
upper2m.X2=0, takedown2m.X1=0, takedown2m.X2=0, hammer2m.X1=0, hammer2m.X2=0
, Cross3m.X1=0, Knee3m.X1=0, Elbow3m.X1=0, Hook3m.X1=0, Jab3m.X1=0, Kick3m.X1=0,
Cross3m.X2=0, Knee3m.X2=0, Elbow3m.X2=0, Hook3m.X2=0, Jab3m.X2=0, Kick3m.X2=0, upper3m.X1=0,
upper3m.X2=0, takedown3m.X1=0, takedown3m.X2=0, hammer3m.X1=0, hammer3m.X2=0)
added_received <- mutate(added_missed, Crossr.X1=0, Kneer.X1=0, Elbowr.X1=0, Hookr.X1=0, Jabr.X1=0, Kickr.X1=0,
Crossr.X2=0, Kneer.X2=0, Elbowr.X2=0, Hookr.X2=0, Jabr.X2=0, Kickr.X2=0, upperr.X1=0,
upperr.X2=0, takedownr.X1=0, takedownr.X2=0, hammerr.X1=0, hammerr.X2=0
, Cross2r.X1=0, Knee2r.X1=0, Elbow2r.X1=0, Hook2r.X1=0, Jab2r.X1=0, Kick2r.X1=0,
Cross2r.X2=0, Knee2r.X2=0, Elbow2r.X2=0, Hook2r.X2=0, Jab2r.X2=0, Kick2r.X2=0, upper2r.X1=0,
upper2r.X2=0, takedown2r.X1=0, takedown2r.X2=0, hammer2r.X1=0, hammer2r.X2=0
, Cross3r.X1=0, Knee3r.X1=0, Elbow3r.X1=0, Hook3r.X1=0, Jab3r.X1=0, Kick3r.X1=0,
Cross3r.X2=0, Knee3r.X2=0, Elbow3r.X2=0, Hook3r.X2=0, Jab3r.X2=0, Kick3r.X2=0, upper3r.X1=0,
upper3r.X2=0, takedown3r.X1=0, takedown3r.X2=0, hammer3r.X1=0, hammer3r.X2=0)Save original Added data table and make a new table called Added that is the combined received, missed, and landed binary/dummy columns just mutated to each other above using dplyr.
Added1 <- AddedAdded <- added_received
head(Added,10)## Round SecondsIntoRound SecondsLastRoundAction cmTotHitsR.X1 cmTotHitsL.X1
## 1 1 NA NA NA NA
## 2 1 NA NA NA NA
## 3 1 NA NA NA NA
## 4 1 NA NA NA NA
## 5 1 NA NA NA NA
## 6 1 NA NA NA NA
## 7 1 NA NA NA NA
## 8 1 NA NA NA NA
## 9 1 NA NA NA NA
## 10 1 NA NA NA NA
## cmTotHitsM.X1 Hits.Recvd.X1 Hits.Lnd.X1 Hits.Mssd.X1 cmTotHitsR.X2
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## 7 NA NA NA NA NA
## 8 NA NA NA NA NA
## 9 NA NA NA NA NA
## 10 NA NA NA NA NA
## cmTotHitsL.X2 cmTotHitsM.X2 Hits.Recvd.X2 Hits.Lnd.X2 Hits.Mssd.X2 Time
## 1 NA NA NA NA NA 4:55
## 2 NA NA NA NA NA 4:54
## 3 NA NA NA NA NA 4:50
## 4 NA NA NA NA NA 4:48
## 5 NA NA NA NA NA 4:46
## 6 NA NA NA NA NA 4:40
## 7 NA NA NA NA NA 4:39
## 8 NA NA NA NA NA 4:36
## 9 NA NA NA NA NA 4:32
## 10 NA NA NA NA NA 4:31
## FighterActionReactions.X1 FightersActionsReactions.X2 Notes Crossl.X1
## 1 missed L jab <NA> Zarah 0
## 2 <NA> missed R cross Zarah 0
## 3 <NA> missed R cross Zarah 0
## 4 missed R cross, missed L jab <NA> Zarah 0
## 5 missed L jab <NA> Zarah 0
## 6 missed R cross missed L cross Zarah 0
## 7 <NA> missed L hook Zarah 0
## 8 missed L mt kick to low leg <NA> Zarah 0
## 9 <NA> misses L jab Zarah 0
## 10 misses R cross <NA> Zarah 0
## Kneel.X1 Elbowl.X1 Hookl.X1 Jabl.X1 Kickl.X1 Crossl.X2 Kneel.X2 Elbowl.X2
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0
## Hookl.X2 Jabl.X2 Kickl.X2 upperl.X1 upperl.X2 takedownl.X1 takedownl.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## hammerl.X1 hammerl.X2 Cross2l.X1 Knee2l.X1 Elbow2l.X1 Hook2l.X1 Jab2l.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Kick2l.X1 Cross2l.X2 Knee2l.X2 Elbow2l.X2 Hook2l.X2 Jab2l.X2 Kick2l.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## upper2l.X1 upper2l.X2 takedown2l.X1 takedown2l.X2 hammer2l.X1 hammer2l.X2
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Cross3l.X1 Knee3l.X1 Elbow3l.X1 Hook3l.X1 Jab3l.X1 Kick3l.X1 Cross3l.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Knee3l.X2 Elbow3l.X2 Hook3l.X2 Jab3l.X2 Kick3l.X2 upper3l.X1 upper3l.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## takedown3l.X1 takedown3l.X2 hammer3l.X1 hammer3l.X2 Crossm.X1 Kneem.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Elbowm.X1 Hookm.X1 Jabm.X1 Kickm.X1 Crossm.X2 Kneem.X2 Elbowm.X2 Hookm.X2
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0
## Jabm.X2 Kickm.X2 upperm.X1 upperm.X2 takedownm.X1 takedownm.X2 hammerm.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## hammerm.X2 Cross2m.X1 Knee2m.X1 Elbow2m.X1 Hook2m.X1 Jab2m.X1 Kick2m.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Cross2m.X2 Knee2m.X2 Elbow2m.X2 Hook2m.X2 Jab2m.X2 Kick2m.X2 upper2m.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## upper2m.X2 takedown2m.X1 takedown2m.X2 hammer2m.X1 hammer2m.X2 Cross3m.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Knee3m.X1 Elbow3m.X1 Hook3m.X1 Jab3m.X1 Kick3m.X1 Cross3m.X2 Knee3m.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Elbow3m.X2 Hook3m.X2 Jab3m.X2 Kick3m.X2 upper3m.X1 upper3m.X2 takedown3m.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## takedown3m.X2 hammer3m.X1 hammer3m.X2 Crossr.X1 Kneer.X1 Elbowr.X1 Hookr.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Jabr.X1 Kickr.X1 Crossr.X2 Kneer.X2 Elbowr.X2 Hookr.X2 Jabr.X2 Kickr.X2
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0
## upperr.X1 upperr.X2 takedownr.X1 takedownr.X2 hammerr.X1 hammerr.X2
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Cross2r.X1 Knee2r.X1 Elbow2r.X1 Hook2r.X1 Jab2r.X1 Kick2r.X1 Cross2r.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Knee2r.X2 Elbow2r.X2 Hook2r.X2 Jab2r.X2 Kick2r.X2 upper2r.X1 upper2r.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## takedown2r.X1 takedown2r.X2 hammer2r.X1 hammer2r.X2 Cross3r.X1 Knee3r.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Elbow3r.X1 Hook3r.X1 Jab3r.X1 Kick3r.X1 Cross3r.X2 Knee3r.X2 Elbow3r.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Hook3r.X2 Jab3r.X2 Kick3r.X2 upper3r.X1 upper3r.X2 takedown3r.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## takedown3r.X2 hammer3r.X1 hammer3r.X2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0The following code adds a value of 1 if the grep’d binary field has a count in that index of observation, otherwise, it will be 0.
Added[cross_sq1,'Crossl.X1'] <- 1
Added[cross_sq1b,'Crossl.X2'] <- 1
Added[hook_sq1,'Hookl.X1'] <- 1
Added[hook_sq1b,'Hookl.X2'] <- 1
Added[jab_sq1,'Jabl.X1'] <- 1
Added[jab_sq1b,'Jabl.X2'] <- 1
Added[knees_sq1,'Kneel.X1'] <- 1
Added[knees_sq1b,'Kneel.X2'] <- 1
Added[elbows_sq1,'Elbowl.X1'] <- 1
Added[elbows_sq1b,'Elbowl.X2'] <- 1
Added[kicks_sq1,'Kickl.X1'] <- 1
Added[kicks_sq1b,'Kickl.X2'] <- 1
Added[upper_sq1,'upperl.X1'] <- 1
Added[upper_sq1b,'upperl.X2'] <- 1
Added[takedown_sq1,'takedownl.X1'] <- 1
Added[takedown_sq1b,'takedownl.X2'] <- 1
Added[hammer_sq1,'hammerl.X1'] <- 1
Added[hammer_sq1b,'hammerl.X2'] <- 1
Added[cross_sq2,'Cross2l.X1'] <- 1
Added[cross_sq2b,'Cross2l.X2'] <- 1
Added[hook_sq2,'Hook2l.X1'] <- 1
Added[hook_sq2b,'Hook2l.X2'] <- 1
Added[jab_sq2,'Jab2l.X1'] <- 1
Added[jab_sq2b,'Jab2l.X2'] <- 1
Added[knees_sq2,'Knee2l.X1'] <- 1
Added[knees_sq2b,'Knee2l.X2'] <- 1
Added[elbows_sq2,'Elbow2l.X1'] <- 1
Added[elbows_sq2b,'Elbow2l.X2'] <- 1
Added[kicks_sq2,'Kick2l.X1'] <- 1
Added[kicks_sq2b,'Kick2l.X2'] <- 1
Added[upper_sq2,'upper2l.X1'] <- 1
Added[upper_sq2b,'upper2l.X2'] <- 1
Added[takedown_sq2,'takedown2l.X1'] <- 1
Added[takedown_sq2b,'takedown2l.X2'] <- 1
Added[hammer_sq2,'hammer2l.X1'] <- 1
Added[hammer_sq2b,'hammer2l.X2'] <- 1
Added[cross_sq3,'Cross3l.X1'] <- 1
Added[cross_sq3b,'Cross3l.X2'] <- 1
Added[hook_sq3,'Hook3l.X1'] <- 1
Added[hook_sq3b,'Hook3l.X2'] <- 1
Added[jab_sq3,'Jab3l.X1'] <- 1
Added[jab_sq3b,'Jab3l.X2'] <- 1
Added[knees_sq3,'Knee3l.X1'] <- 1
Added[knees_sq3b,'Knee3l.X2'] <- 1
Added[elbows_sq3,'Elbow3l.X1'] <- 1
Added[elbows_sq3b,'Elbow3l.X2'] <- 1
Added[kicks_sq3,'Kick3l.X1'] <- 1
Added[kicks_sq3b,'Kick3l.X2'] <- 1
Added[upper_sq3,'upper3l.X1'] <- 1
Added[upper_sq3b,'upper3l.X2'] <- 1
Added[takedown_sq3,'takedown3l.X1'] <- 1
Added[takedown_sq3b,'takedown3l.X2'] <- 1
Added[hammer_sq3,'hammer3l.X1'] <- 1
Added[hammer_sq3b,'hammer3l.X2'] <- 1
Added[cross_sq1m,'Crossm.X1'] <- 1
Added[cross_sq1bm,'Crossm.X2'] <- 1
Added[hook_sq1m,'Hookm.X1'] <- 1
Added[hook_sq1bm,'Hookm.X2'] <- 1
Added[jab_sq1m,'Jabm.X1'] <- 1
Added[jab_sq1bm,'Jabm.X2'] <- 1
Added[knees_sq1m,'Kneem.X1'] <- 1
Added[knees_sq1bm,'Kneem.X2'] <- 1
Added[elbows_sq1m,'Elbowm.X1'] <- 1
Added[elbows_sq1bm,'Elbowm.X2'] <- 1
Added[kicks_sq1m,'Kickm.X1'] <- 1
Added[kicks_sq1bm,'Kickm.X2'] <- 1
Added[upper_sq1m,'upperm.X1'] <- 1
Added[upper_sq1bm,'upperm.X2'] <- 1
Added[takedown_sq1m,'takedownm.X1'] <- 1
Added[takedown_sq1bm,'takedownm.X2'] <- 1
Added[hammer_sq1m,'hammerm.X1'] <- 1
Added[hammer_sq1bm,'hammerm.X2'] <- 1
Added[cross_sq2m,'Cross2m.X1'] <- 1
Added[cross_sq2bm,'Cross2m.X2'] <- 1
Added[hook_sq2m,'Hook2m.X1'] <- 1
Added[hook_sq2bm,'Hook2m.X2'] <- 1
Added[jab_sq2m,'Jab2m.X1'] <- 1
Added[jab_sq2bm,'Jab2m.X2'] <- 1
Added[knees_sq2m,'Knee2m.X1'] <- 1
Added[knees_sq2bm,'Knee2m.X2'] <- 1
Added[elbows_sq2m,'Elbow2m.X1'] <- 1
Added[elbows_sq2bm,'Elbow2m.X2'] <- 1
Added[kicks_sq2m,'Kick2m.X1'] <- 1
Added[kicks_sq2bm,'Kick2m.X2'] <- 1
Added[upper_sq2m,'upper2m.X1'] <- 1
Added[upper_sq2bm,'upper2m.X2'] <- 1
Added[takedown_sq2m,'takedown2m.X1'] <- 1
Added[takedown_sq2bm,'takedown2m.X2'] <- 1
Added[hammer_sq2m,'hammer2m.X1'] <- 1
Added[hammer_sq2bm,'hammer2m.X2'] <- 1
Added[cross_sq3m,'Cross3m.X1'] <- 1
Added[cross_sq3bm,'Cross3m.X2'] <- 1
Added[hook_sq3m,'Hook3m.X1'] <- 1
Added[hook_sq3bm,'Hook3m.X2'] <- 1
Added[jab_sq3m,'Jab3m.X1'] <- 1
Added[jab_sq3bm,'Jab3m.X2'] <- 1
Added[knees_sq3m,'Knee3m.X1'] <- 1
Added[knees_sq3bm,'Knee3m.X2'] <- 1
Added[elbows_sq3m,'Elbow3m.X1'] <- 1
Added[elbows_sq3bm,'Elbow3m.X2'] <- 1
Added[kicks_sq3m,'Kick3m.X1'] <- 1
Added[kicks_sq3bm,'Kick3m.X2'] <- 1
Added[upper_sq3m,'upper3m.X1'] <- 1
Added[upper_sq3bm,'upper3m.X2'] <- 1
Added[takedown_sq3m,'takedown3m.X1'] <- 1
Added[takedown_sq3bm,'takedown3m.X2'] <- 1
Added[hammer_sq3m,'hammer3m.X1'] <- 1
Added[hammer_sq3bm,'hammer3m.X2'] <- 1
Added[cross_sq1r,'Crossr.X1'] <- 1
Added[cross_sq1br,'Crossr.X2'] <- 1
Added[hook_sq1r,'Hookr.X1'] <- 1
Added[hook_sq1br,'Hookr.X2'] <- 1
Added[jab_sq1r,'Jabr.X1'] <- 1
Added[jab_sq1br,'Jabr.X2'] <- 1
Added[knees_sq1r,'Kneer.X1'] <- 1
Added[knees_sq1br,'Kneer.X2'] <- 1
Added[elbows_sq1r,'Elbowr.X1'] <- 1
Added[elbows_sq1br,'Elbowr.X2'] <- 1
Added[kicks_sq1r,'Kickr.X1'] <- 1
Added[kicks_sq1br,'Kickr.X2'] <- 1
Added[upper_sq1r,'upperr.X1'] <- 1
Added[upper_sq1br,'upperr.X2'] <- 1
Added[takedown_sq1r,'takedownr.X1'] <- 1
Added[takedown_sq1br,'takedownr.X2'] <- 1
Added[hammer_sq1r,'hammerr.X1'] <- 1
Added[hammer_sq1br,'hammerr.X2'] <- 1
Added[cross_sq2r,'Cross2r.X1'] <- 1
Added[cross_sq2br,'Cross2r.X2'] <- 1
Added[hook_sq2r,'Hook2r.X1'] <- 1
Added[hook_sq2br,'Hook2r.X2'] <- 1
Added[jab_sq2r,'Jab2r.X1'] <- 1
Added[jab_sq2br,'Jab2r.X2'] <- 1
Added[knees_sq2r,'Knee2r.X1'] <- 1
Added[knees_sq2br,'Knee2r.X2'] <- 1
Added[elbows_sq2r,'Elbow2r.X1'] <- 1
Added[elbows_sq2br,'Elbow2r.X2'] <- 1
Added[kicks_sq2r,'Kick2r.X1'] <- 1
Added[kicks_sq2br,'Kick2r.X2'] <- 1
Added[upper_sq2r,'upper2r.X1'] <- 1
Added[upper_sq2br,'upper2r.X2'] <- 1
Added[takedown_sq2r,'takedown2r.X1'] <- 1
Added[takedown_sq2br,'takedown2r.X2'] <- 1
Added[hammer_sq2r,'hammer2r.X1'] <- 1
Added[hammer_sq2br,'hammer2r.X2'] <- 1
Added[cross_sq3r,'Cross3r.X1'] <- 1
Added[cross_sq3br,'Cross3r.X2'] <- 1
Added[hook_sq3r,'Hook3r.X1'] <- 1
Added[hook_sq3br,'Hook3r.X2'] <- 1
Added[jab_sq3r,'Jab3r.X1'] <- 1
Added[jab_sq3br,'Jab3r.X2'] <- 1
Added[knees_sq3r,'Knee3r.X1'] <- 1
Added[knees_sq3br,'Knee3r.X2'] <- 1
Added[elbows_sq3r,'Elbow3r.X1'] <- 1
Added[elbows_sq3br,'Elbow3r.X2'] <- 1
Added[kicks_sq3r,'Kick3r.X1'] <- 1
Added[kicks_sq3br,'Kick3r.X2'] <- 1
Added[upper_sq3r,'upper3r.X1'] <- 1
Added[upper_sq3br,'upper3r.X2'] <- 1
Added[takedown_sq3r,'takedown3r.X1'] <- 1
Added[takedown_sq3br,'takedown3r.X2'] <- 1
Added[hammer_sq3r,'hammer3r.X1'] <- 1
Added[hammer_sq3br,'hammer3r.X2'] <- 1colnames(Added)## [1] "Round" "SecondsIntoRound"
## [3] "SecondsLastRoundAction" "cmTotHitsR.X1"
## [5] "cmTotHitsL.X1" "cmTotHitsM.X1"
## [7] "Hits.Recvd.X1" "Hits.Lnd.X1"
## [9] "Hits.Mssd.X1" "cmTotHitsR.X2"
## [11] "cmTotHitsL.X2" "cmTotHitsM.X2"
## [13] "Hits.Recvd.X2" "Hits.Lnd.X2"
## [15] "Hits.Mssd.X2" "Time"
## [17] "FighterActionReactions.X1" "FightersActionsReactions.X2"
## [19] "Notes" "Crossl.X1"
## [21] "Kneel.X1" "Elbowl.X1"
## [23] "Hookl.X1" "Jabl.X1"
## [25] "Kickl.X1" "Crossl.X2"
## [27] "Kneel.X2" "Elbowl.X2"
## [29] "Hookl.X2" "Jabl.X2"
## [31] "Kickl.X2" "upperl.X1"
## [33] "upperl.X2" "takedownl.X1"
## [35] "takedownl.X2" "hammerl.X1"
## [37] "hammerl.X2" "Cross2l.X1"
## [39] "Knee2l.X1" "Elbow2l.X1"
## [41] "Hook2l.X1" "Jab2l.X1"
## [43] "Kick2l.X1" "Cross2l.X2"
## [45] "Knee2l.X2" "Elbow2l.X2"
## [47] "Hook2l.X2" "Jab2l.X2"
## [49] "Kick2l.X2" "upper2l.X1"
## [51] "upper2l.X2" "takedown2l.X1"
## [53] "takedown2l.X2" "hammer2l.X1"
## [55] "hammer2l.X2" "Cross3l.X1"
## [57] "Knee3l.X1" "Elbow3l.X1"
## [59] "Hook3l.X1" "Jab3l.X1"
## [61] "Kick3l.X1" "Cross3l.X2"
## [63] "Knee3l.X2" "Elbow3l.X2"
## [65] "Hook3l.X2" "Jab3l.X2"
## [67] "Kick3l.X2" "upper3l.X1"
## [69] "upper3l.X2" "takedown3l.X1"
## [71] "takedown3l.X2" "hammer3l.X1"
## [73] "hammer3l.X2" "Crossm.X1"
## [75] "Kneem.X1" "Elbowm.X1"
## [77] "Hookm.X1" "Jabm.X1"
## [79] "Kickm.X1" "Crossm.X2"
## [81] "Kneem.X2" "Elbowm.X2"
## [83] "Hookm.X2" "Jabm.X2"
## [85] "Kickm.X2" "upperm.X1"
## [87] "upperm.X2" "takedownm.X1"
## [89] "takedownm.X2" "hammerm.X1"
## [91] "hammerm.X2" "Cross2m.X1"
## [93] "Knee2m.X1" "Elbow2m.X1"
## [95] "Hook2m.X1" "Jab2m.X1"
## [97] "Kick2m.X1" "Cross2m.X2"
## [99] "Knee2m.X2" "Elbow2m.X2"
## [101] "Hook2m.X2" "Jab2m.X2"
## [103] "Kick2m.X2" "upper2m.X1"
## [105] "upper2m.X2" "takedown2m.X1"
## [107] "takedown2m.X2" "hammer2m.X1"
## [109] "hammer2m.X2" "Cross3m.X1"
## [111] "Knee3m.X1" "Elbow3m.X1"
## [113] "Hook3m.X1" "Jab3m.X1"
## [115] "Kick3m.X1" "Cross3m.X2"
## [117] "Knee3m.X2" "Elbow3m.X2"
## [119] "Hook3m.X2" "Jab3m.X2"
## [121] "Kick3m.X2" "upper3m.X1"
## [123] "upper3m.X2" "takedown3m.X1"
## [125] "takedown3m.X2" "hammer3m.X1"
## [127] "hammer3m.X2" "Crossr.X1"
## [129] "Kneer.X1" "Elbowr.X1"
## [131] "Hookr.X1" "Jabr.X1"
## [133] "Kickr.X1" "Crossr.X2"
## [135] "Kneer.X2" "Elbowr.X2"
## [137] "Hookr.X2" "Jabr.X2"
## [139] "Kickr.X2" "upperr.X1"
## [141] "upperr.X2" "takedownr.X1"
## [143] "takedownr.X2" "hammerr.X1"
## [145] "hammerr.X2" "Cross2r.X1"
## [147] "Knee2r.X1" "Elbow2r.X1"
## [149] "Hook2r.X1" "Jab2r.X1"
## [151] "Kick2r.X1" "Cross2r.X2"
## [153] "Knee2r.X2" "Elbow2r.X2"
## [155] "Hook2r.X2" "Jab2r.X2"
## [157] "Kick2r.X2" "upper2r.X1"
## [159] "upper2r.X2" "takedown2r.X1"
## [161] "takedown2r.X2" "hammer2r.X1"
## [163] "hammer2r.X2" "Cross3r.X1"
## [165] "Knee3r.X1" "Elbow3r.X1"
## [167] "Hook3r.X1" "Jab3r.X1"
## [169] "Kick3r.X1" "Cross3r.X2"
## [171] "Knee3r.X2" "Elbow3r.X2"
## [173] "Hook3r.X2" "Jab3r.X2"
## [175] "Kick3r.X2" "upper3r.X1"
## [177] "upper3r.X2" "takedown3r.X1"
## [179] "takedown3r.X2" "hammer3r.X1"
## [181] "hammer3r.X2"head(Added,10)## Round SecondsIntoRound SecondsLastRoundAction cmTotHitsR.X1 cmTotHitsL.X1
## 1 1 NA NA NA NA
## 2 1 NA NA NA NA
## 3 1 NA NA NA NA
## 4 1 NA NA NA NA
## 5 1 NA NA NA NA
## 6 1 NA NA NA NA
## 7 1 NA NA NA NA
## 8 1 NA NA NA NA
## 9 1 NA NA NA NA
## 10 1 NA NA NA NA
## cmTotHitsM.X1 Hits.Recvd.X1 Hits.Lnd.X1 Hits.Mssd.X1 cmTotHitsR.X2
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## 7 NA NA NA NA NA
## 8 NA NA NA NA NA
## 9 NA NA NA NA NA
## 10 NA NA NA NA NA
## cmTotHitsL.X2 cmTotHitsM.X2 Hits.Recvd.X2 Hits.Lnd.X2 Hits.Mssd.X2 Time
## 1 NA NA NA NA NA 4:55
## 2 NA NA NA NA NA 4:54
## 3 NA NA NA NA NA 4:50
## 4 NA NA NA NA NA 4:48
## 5 NA NA NA NA NA 4:46
## 6 NA NA NA NA NA 4:40
## 7 NA NA NA NA NA 4:39
## 8 NA NA NA NA NA 4:36
## 9 NA NA NA NA NA 4:32
## 10 NA NA NA NA NA 4:31
## FighterActionReactions.X1 FightersActionsReactions.X2 Notes Crossl.X1
## 1 missed L jab <NA> Zarah 0
## 2 <NA> missed R cross Zarah 0
## 3 <NA> missed R cross Zarah 0
## 4 missed R cross, missed L jab <NA> Zarah 0
## 5 missed L jab <NA> Zarah 0
## 6 missed R cross missed L cross Zarah 0
## 7 <NA> missed L hook Zarah 0
## 8 missed L mt kick to low leg <NA> Zarah 0
## 9 <NA> misses L jab Zarah 0
## 10 misses R cross <NA> Zarah 0
## Kneel.X1 Elbowl.X1 Hookl.X1 Jabl.X1 Kickl.X1 Crossl.X2 Kneel.X2 Elbowl.X2
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0
## Hookl.X2 Jabl.X2 Kickl.X2 upperl.X1 upperl.X2 takedownl.X1 takedownl.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## hammerl.X1 hammerl.X2 Cross2l.X1 Knee2l.X1 Elbow2l.X1 Hook2l.X1 Jab2l.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Kick2l.X1 Cross2l.X2 Knee2l.X2 Elbow2l.X2 Hook2l.X2 Jab2l.X2 Kick2l.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## upper2l.X1 upper2l.X2 takedown2l.X1 takedown2l.X2 hammer2l.X1 hammer2l.X2
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Cross3l.X1 Knee3l.X1 Elbow3l.X1 Hook3l.X1 Jab3l.X1 Kick3l.X1 Cross3l.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Knee3l.X2 Elbow3l.X2 Hook3l.X2 Jab3l.X2 Kick3l.X2 upper3l.X1 upper3l.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## takedown3l.X1 takedown3l.X2 hammer3l.X1 hammer3l.X2 Crossm.X1 Kneem.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 1 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 1 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 1 0
## Elbowm.X1 Hookm.X1 Jabm.X1 Kickm.X1 Crossm.X2 Kneem.X2 Elbowm.X2 Hookm.X2
## 1 0 0 1 0 0 0 0 0
## 2 0 0 0 0 1 0 0 0
## 3 0 0 0 0 1 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 1 0 0 0 0 0
## 6 0 0 0 0 1 0 0 0
## 7 0 0 0 0 0 0 0 1
## 8 0 0 0 1 0 0 0 0
## 9 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0
## Jabm.X2 Kickm.X2 upperm.X1 upperm.X2 takedownm.X1 takedownm.X2 hammerm.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 1 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## hammerm.X2 Cross2m.X1 Knee2m.X1 Elbow2m.X1 Hook2m.X1 Jab2m.X1 Kick2m.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 1 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Cross2m.X2 Knee2m.X2 Elbow2m.X2 Hook2m.X2 Jab2m.X2 Kick2m.X2 upper2m.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## upper2m.X2 takedown2m.X1 takedown2m.X2 hammer2m.X1 hammer2m.X2 Cross3m.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Knee3m.X1 Elbow3m.X1 Hook3m.X1 Jab3m.X1 Kick3m.X1 Cross3m.X2 Knee3m.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Elbow3m.X2 Hook3m.X2 Jab3m.X2 Kick3m.X2 upper3m.X1 upper3m.X2 takedown3m.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## takedown3m.X2 hammer3m.X1 hammer3m.X2 Crossr.X1 Kneer.X1 Elbowr.X1 Hookr.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Jabr.X1 Kickr.X1 Crossr.X2 Kneer.X2 Elbowr.X2 Hookr.X2 Jabr.X2 Kickr.X2
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0
## upperr.X1 upperr.X2 takedownr.X1 takedownr.X2 hammerr.X1 hammerr.X2
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Cross2r.X1 Knee2r.X1 Elbow2r.X1 Hook2r.X1 Jab2r.X1 Kick2r.X1 Cross2r.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Knee2r.X2 Elbow2r.X2 Hook2r.X2 Jab2r.X2 Kick2r.X2 upper2r.X1 upper2r.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## takedown2r.X1 takedown2r.X2 hammer2r.X1 hammer2r.X2 Cross3r.X1 Knee3r.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## Elbow3r.X1 Hook3r.X1 Jab3r.X1 Kick3r.X1 Cross3r.X2 Knee3r.X2 Elbow3r.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## Hook3r.X2 Jab3r.X2 Kick3r.X2 upper3r.X1 upper3r.X2 takedown3r.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## takedown3r.X2 hammer3r.X1 hammer3r.X2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0Removes SecondsIntoRound.
Added2 <- Added[,-2]
Seconds <- mutate(Added2, SecondsIntoRound=300-(as.numeric(Added2$Time)))
seconds <- Seconds[,c(1,181,2:180)]seconds$lastAction <- as.character(paste(lag(seconds$SecondsIntoRound,1)))
seconds$lastAction <- gsub('NA','0',seconds$lastAction)
seconds$lastAction <- as.numeric(paste(seconds$lastAction))Get the first and last neighborhood of features or columns in this data table.
colnames(seconds)[c(1:5,180:182)]## [1] "Round" "SecondsIntoRound" "SecondsLastRoundAction"
## [4] "cmTotHitsR.X1" "cmTotHitsL.X1" "hammer3r.X1"
## [7] "hammer3r.X2" "lastAction"Rearrange the columns and remove the empty SecondsLastRoundAction field.
seconds <- seconds[,c(1:2,182,4:181)]
colnames(seconds)[1:8]## [1] "Round" "SecondsIntoRound" "lastAction" "cmTotHitsR.X1"
## [5] "cmTotHitsL.X1" "cmTotHitsM.X1" "Hits.Recvd.X1" "Hits.Lnd.X1"Extension of seconds using mutate() of dplyr library, keeps value of seconds into round as seconds since last action if no action that observation.
last <- mutate(seconds, SecondsLastRoundAction = if_else(seconds$SecondsIntoRound -
seconds$lastAction > 0,
seconds$SecondsIntoRound -
seconds$lastAction,
seconds$SecondsIntoRound))Reorders so that SecondsLastRoundAction is at front fields location.
last <- last[,c(1:3,182,4:181)] Rearrange the order of the actions by fighter and landed, missed, and received. Also, add the counts for each accumulated landed actions, missed actions, and received actions per second observed.
landX1 <- colnames(last)[c(21:26,33,35,37,39:44,51,53,55,57:62,69,71,73)]
landX2 <- colnames(last)[c(27:32,34,36,38,45:50,52,54,56,63:68,70,72,74)]
missX1 <- colnames(last)[c(75:80,87,89,91,93:98,105,107,109,111:116,123,125,127)]
missX2 <- colnames(last)[c(81:86,88,90,92,99:104,106,108,110,117:122,124,126,128)]
recvX1 <- colnames(last)[c(129:134,141,143,145,147:152,159,161,163,165:170,177,179,181)]
recvX2 <- colnames(last)[c(135:140,142,144,146,153:158,160,162,164,171:176,178,180,182)]
x1l <- mutate(last, TotLandsX1=last[,21]+last[,22]+last[,23]+last[,24]+last[,25]+
last[,26]+last[,33]+last[,35]+last[,37]+last[,39]+last[,40]+
last[,41]+last[,42]+last[,43]+last[,44]+last[,51]+last[,53]+
last[,55]+last[,57]+last[,58]+last[,59]+last[,60]+last[,61]+
last[,62]+last[,69]+last[,71]+last[,73])
x1m <- mutate(x1l, TotMissedX1=last[,75]+last[,76]+last[,77]+last[,78]+last[,79]+
last[,80]+last[,87]+last[,89]+last[,91]+last[,93]+last[,94]+
last[,95]+last[,96]+last[,97]+last[,98]+last[,105]+last[,107]+
last[,109]+last[,111]+last[,112]+last[,113]+last[,114]+last[,115]+
last[,116]+last[,123]+last[,125]+last[,127])
x1r <- mutate(x1m, TotReceivedX1=last[,129]+last[,130]+last[,131]+last[,132]+last[,133]+
last[,134]+last[,141]+last[,143]+last[,145]+last[,147]+last[,148]+
last[,149]+last[,150]+last[,151]+last[,152]+last[,159]+last[,161]+
last[,163]+last[,165]+last[,166]+last[,167]+last[,168]+last[,169]+
last[,170]+last[,177]+last[,179]+last[,181])
x2l <- mutate(x1r, TotLandsX2=last[,27]+last[,28]+last[,29]+last[,30]+last[,31]+
last[,32]+last[,34]+last[,36]+last[,38]+last[,45]+last[,46]+
last[,47]+last[,48]+last[,49]+last[,50]+last[,52]+last[,54]+
last[,56]+last[,63]+last[,64]+last[,65]+last[,66]+last[,67]+
last[,68]+last[,70]+last[,72]+last[,74])
x2m <- mutate(x2l, TotMissedX2=last[,81]+last[,82]+last[,83]+last[,84]+last[,85]+
last[,86]+last[,88]+last[,90]+last[,92]+last[,99]+last[,100]+
last[,101]+last[,102]+last[,103]+last[,104]+last[,106]+last[,108]+
last[,110]+last[,117]+last[,118]+last[,119]+last[,120]+last[,121]+
last[,122]+last[,124]+last[,126]+last[,128])
x2r <- mutate(x2m, TotReceivedX2=last[,135]+last[,136]+last[,137]+last[,138]+last[,139]+
last[,140]+last[,142]+last[,144]+last[,146]+last[,153]+last[,154]+
last[,155]+last[,156]+last[,157]+last[,158]+last[,160]+last[,162]+
last[,164]+last[,171]+last[,172]+last[,173]+last[,174]+last[,175]+
last[,176]+last[,178]+last[,180]+last[,182])
Added3 <- x2r[,c(1,2,3,4:7,183:185,11:13,186:188,17:182)]
colnames(Added3)## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X1" "cmTotHitsL.X1"
## [7] "cmTotHitsM.X1" "TotLandsX1"
## [9] "TotMissedX1" "TotReceivedX1"
## [11] "cmTotHitsR.X2" "cmTotHitsL.X2"
## [13] "cmTotHitsM.X2" "TotLandsX2"
## [15] "TotMissedX2" "TotReceivedX2"
## [17] "Time" "FighterActionReactions.X1"
## [19] "FightersActionsReactions.X2" "Notes"
## [21] "Crossl.X1" "Kneel.X1"
## [23] "Elbowl.X1" "Hookl.X1"
## [25] "Jabl.X1" "Kickl.X1"
## [27] "Crossl.X2" "Kneel.X2"
## [29] "Elbowl.X2" "Hookl.X2"
## [31] "Jabl.X2" "Kickl.X2"
## [33] "upperl.X1" "upperl.X2"
## [35] "takedownl.X1" "takedownl.X2"
## [37] "hammerl.X1" "hammerl.X2"
## [39] "Cross2l.X1" "Knee2l.X1"
## [41] "Elbow2l.X1" "Hook2l.X1"
## [43] "Jab2l.X1" "Kick2l.X1"
## [45] "Cross2l.X2" "Knee2l.X2"
## [47] "Elbow2l.X2" "Hook2l.X2"
## [49] "Jab2l.X2" "Kick2l.X2"
## [51] "upper2l.X1" "upper2l.X2"
## [53] "takedown2l.X1" "takedown2l.X2"
## [55] "hammer2l.X1" "hammer2l.X2"
## [57] "Cross3l.X1" "Knee3l.X1"
## [59] "Elbow3l.X1" "Hook3l.X1"
## [61] "Jab3l.X1" "Kick3l.X1"
## [63] "Cross3l.X2" "Knee3l.X2"
## [65] "Elbow3l.X2" "Hook3l.X2"
## [67] "Jab3l.X2" "Kick3l.X2"
## [69] "upper3l.X1" "upper3l.X2"
## [71] "takedown3l.X1" "takedown3l.X2"
## [73] "hammer3l.X1" "hammer3l.X2"
## [75] "Crossm.X1" "Kneem.X1"
## [77] "Elbowm.X1" "Hookm.X1"
## [79] "Jabm.X1" "Kickm.X1"
## [81] "Crossm.X2" "Kneem.X2"
## [83] "Elbowm.X2" "Hookm.X2"
## [85] "Jabm.X2" "Kickm.X2"
## [87] "upperm.X1" "upperm.X2"
## [89] "takedownm.X1" "takedownm.X2"
## [91] "hammerm.X1" "hammerm.X2"
## [93] "Cross2m.X1" "Knee2m.X1"
## [95] "Elbow2m.X1" "Hook2m.X1"
## [97] "Jab2m.X1" "Kick2m.X1"
## [99] "Cross2m.X2" "Knee2m.X2"
## [101] "Elbow2m.X2" "Hook2m.X2"
## [103] "Jab2m.X2" "Kick2m.X2"
## [105] "upper2m.X1" "upper2m.X2"
## [107] "takedown2m.X1" "takedown2m.X2"
## [109] "hammer2m.X1" "hammer2m.X2"
## [111] "Cross3m.X1" "Knee3m.X1"
## [113] "Elbow3m.X1" "Hook3m.X1"
## [115] "Jab3m.X1" "Kick3m.X1"
## [117] "Cross3m.X2" "Knee3m.X2"
## [119] "Elbow3m.X2" "Hook3m.X2"
## [121] "Jab3m.X2" "Kick3m.X2"
## [123] "upper3m.X1" "upper3m.X2"
## [125] "takedown3m.X1" "takedown3m.X2"
## [127] "hammer3m.X1" "hammer3m.X2"
## [129] "Crossr.X1" "Kneer.X1"
## [131] "Elbowr.X1" "Hookr.X1"
## [133] "Jabr.X1" "Kickr.X1"
## [135] "Crossr.X2" "Kneer.X2"
## [137] "Elbowr.X2" "Hookr.X2"
## [139] "Jabr.X2" "Kickr.X2"
## [141] "upperr.X1" "upperr.X2"
## [143] "takedownr.X1" "takedownr.X2"
## [145] "hammerr.X1" "hammerr.X2"
## [147] "Cross2r.X1" "Knee2r.X1"
## [149] "Elbow2r.X1" "Hook2r.X1"
## [151] "Jab2r.X1" "Kick2r.X1"
## [153] "Cross2r.X2" "Knee2r.X2"
## [155] "Elbow2r.X2" "Hook2r.X2"
## [157] "Jab2r.X2" "Kick2r.X2"
## [159] "upper2r.X1" "upper2r.X2"
## [161] "takedown2r.X1" "takedown2r.X2"
## [163] "hammer2r.X1" "hammer2r.X2"
## [165] "Cross3r.X1" "Knee3r.X1"
## [167] "Elbow3r.X1" "Hook3r.X1"
## [169] "Jab3r.X1" "Kick3r.X1"
## [171] "Cross3r.X2" "Knee3r.X2"
## [173] "Elbow3r.X2" "Hook3r.X2"
## [175] "Jab3r.X2" "Kick3r.X2"
## [177] "upper3r.X1" "upper3r.X2"
## [179] "takedown3r.X1" "takedown3r.X2"
## [181] "hammer3r.X1" "hammer3r.X2"Create break points where last action in seconds is greater than the amount of seconds of the round. This is because there are three round 1’s from three fights, where the cut off needs to be in place for the end of a round when counting seconds since last action of that round.
break1 <- Added3$lastAction > Added3$SecondsIntoRound
break1## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSEbreak2 <- row.names(Added3[break1,])
break2## [1] "170" "354"bk2 <- as.numeric(break2)
bk2## [1] 170 354split1 <- bk2[1]
split1## [1] 170split2 <- bk2[2]
split2## [1] 354First opponent:
Table1 <- Added3[1:(split1-1),]
Table1## Round SecondsIntoRound lastAction SecondsLastRoundAction cmTotHitsR.X1
## 1 1 4 0 4 NA
## 2 1 5 4 1 NA
## 3 1 9 5 4 NA
## 4 1 11 9 2 NA
## 5 1 13 11 2 NA
## 6 1 19 13 6 NA
## 7 1 20 19 1 NA
## 8 1 23 20 3 NA
## 9 1 27 23 4 NA
## 10 1 28 27 1 NA
## 11 1 30 28 2 NA
## 12 1 37 30 7 NA
## 13 1 38 37 1 NA
## 14 1 40 38 2 NA
## 15 1 41 40 1 NA
## 16 1 45 41 4 NA
## 17 1 46 45 1 NA
## 18 1 47 46 1 NA
## 19 1 51 47 4 NA
## 20 1 57 51 6 NA
## 21 1 63 57 6 NA
## 22 1 68 63 5 NA
## 23 1 69 68 1 NA
## 24 1 70 69 1 NA
## 25 1 72 70 2 NA
## 26 1 73 72 1 NA
## 27 1 74 73 1 NA
## 28 1 75 74 1 NA
## 29 1 76 75 1 NA
## 30 1 77 76 1 NA
## 31 1 78 77 1 NA
## 32 1 79 78 1 NA
## 33 1 80 79 1 NA
## 34 1 81 80 1 NA
## 35 1 82 81 1 NA
## 36 1 83 82 1 NA
## 37 1 84 83 1 NA
## 38 1 85 84 1 NA
## 39 1 86 85 1 NA
## 40 1 87 86 1 NA
## 41 1 88 87 1 NA
## 42 1 89 88 1 NA
## 43 1 90 89 1 NA
## 44 1 91 90 1 NA
## 45 1 92 91 1 NA
## 46 1 93 92 1 NA
## 47 1 94 93 1 NA
## 48 1 95 94 1 NA
## 49 1 96 95 1 NA
## 50 1 97 96 1 NA
## 51 1 98 97 1 NA
## 52 1 99 98 1 NA
## 53 1 100 99 1 NA
## 54 1 101 100 1 NA
## 55 1 102 101 1 NA
## 56 1 103 102 1 NA
## 57 1 104 103 1 NA
## 58 1 105 104 1 NA
## 59 1 106 105 1 NA
## 60 1 107 106 1 NA
## 61 1 108 107 1 NA
## 62 1 109 108 1 NA
## 63 1 110 109 1 NA
## 64 1 111 110 1 NA
## 65 1 112 111 1 NA
## 66 1 113 112 1 NA
## 67 1 114 113 1 NA
## 68 1 115 114 1 NA
## 69 1 116 115 1 NA
## 70 1 117 116 1 NA
## 71 1 118 117 1 NA
## 72 1 119 118 1 NA
## 73 1 120 119 1 NA
## 74 1 121 120 1 NA
## 75 1 122 121 1 NA
## 76 1 123 122 1 NA
## 77 1 124 123 1 NA
## 78 1 125 124 1 NA
## 79 1 126 125 1 NA
## 80 1 127 126 1 NA
## 81 1 128 127 1 NA
## 82 1 129 128 1 NA
## 83 1 130 129 1 NA
## 84 1 131 130 1 NA
## 85 1 132 131 1 NA
## 86 1 133 132 1 NA
## 87 1 134 133 1 NA
## 88 1 135 134 1 NA
## 89 1 136 135 1 NA
## 90 1 137 136 1 NA
## 91 1 138 137 1 NA
## 92 1 139 138 1 NA
## 93 1 140 139 1 NA
## 94 1 141 140 1 NA
## 95 1 142 141 1 NA
## 96 1 143 142 1 NA
## 97 1 144 143 1 NA
## 98 1 145 144 1 NA
## 99 1 146 145 1 NA
## 100 1 147 146 1 NA
## 101 1 148 147 1 NA
## 102 1 149 148 1 NA
## 103 1 150 149 1 NA
## 104 1 151 150 1 NA
## 105 1 152 151 1 NA
## 106 1 153 152 1 NA
## 107 1 154 153 1 NA
## 108 1 155 154 1 NA
## 109 1 156 155 1 NA
## 110 1 157 156 1 NA
## 111 1 158 157 1 NA
## 112 1 159 158 1 NA
## 113 1 160 159 1 NA
## 114 1 161 160 1 NA
## 115 1 162 161 1 NA
## 116 1 163 162 1 NA
## 117 1 164 163 1 NA
## 118 1 165 164 1 NA
## 119 1 166 165 1 NA
## 120 1 167 166 1 NA
## 121 1 168 167 1 NA
## 122 1 169 168 1 NA
## 123 1 170 169 1 NA
## 124 1 171 170 1 NA
## 125 1 172 171 1 NA
## 126 1 173 172 1 NA
## 127 1 174 173 1 NA
## 128 1 175 174 1 NA
## 129 1 176 175 1 NA
## 130 1 177 176 1 NA
## 131 1 178 177 1 NA
## 132 1 179 178 1 NA
## 133 1 180 179 1 NA
## 134 1 181 180 1 NA
## 135 1 182 181 1 NA
## 136 1 183 182 1 NA
## 137 1 184 183 1 NA
## 138 1 185 184 1 NA
## 139 1 186 185 1 NA
## 140 1 187 186 1 NA
## 141 1 188 187 1 NA
## 142 1 189 188 1 NA
## 143 1 190 189 1 NA
## 144 1 191 190 1 NA
## 145 1 192 191 1 NA
## 146 1 193 192 1 NA
## 147 1 194 193 1 NA
## 148 1 195 194 1 NA
## 149 1 196 195 1 NA
## 150 1 197 196 1 NA
## 151 1 198 197 1 NA
## 152 1 199 198 1 NA
## 153 1 200 199 1 NA
## 154 1 201 200 1 NA
## 155 1 202 201 1 NA
## 156 1 203 202 1 NA
## 157 1 204 203 1 NA
## 158 1 205 204 1 NA
## 159 1 206 205 1 NA
## 160 1 207 206 1 NA
## 161 1 208 207 1 NA
## 162 1 209 208 1 NA
## 163 1 210 209 1 NA
## 164 1 211 210 1 NA
## 165 1 212 211 1 NA
## 166 1 213 212 1 NA
## 167 1 214 213 1 NA
## 168 1 215 214 1 NA
## 169 1 216 215 1 NA
## cmTotHitsL.X1 cmTotHitsM.X1 TotLandsX1 TotMissedX1 TotReceivedX1
## 1 NA NA 0 1 0
## 2 NA NA 0 0 0
## 3 NA NA 0 0 0
## 4 NA NA 0 2 0
## 5 NA NA 0 1 0
## 6 NA NA 0 1 0
## 7 NA NA 0 0 0
## 8 NA NA 0 1 0
## 9 NA NA 0 0 0
## 10 NA NA 0 1 0
## 11 NA NA 0 0 0
## 12 NA NA 0 1 0
## 13 NA NA 1 0 0
## 14 NA NA 1 0 0
## 15 NA NA 0 1 0
## 16 NA NA 0 1 0
## 17 NA NA 0 0 0
## 18 NA NA 0 0 0
## 19 NA NA 0 1 0
## 20 NA NA 0 0 1
## 21 NA NA 0 1 0
## 22 NA NA 0 2 0
## 23 NA NA 0 0 0
## 24 NA NA 1 0 0
## 25 NA NA 0 0 1
## 26 NA NA 0 1 0
## 27 NA NA 0 0 0
## 28 NA NA 0 0 0
## 29 NA NA 0 0 0
## 30 NA NA 0 0 0
## 31 NA NA 0 0 0
## 32 NA NA 0 0 0
## 33 NA NA 0 0 0
## 34 NA NA 0 0 0
## 35 NA NA 0 0 0
## 36 NA NA 0 0 0
## 37 NA NA 0 0 0
## 38 NA NA 0 0 0
## 39 NA NA 0 0 0
## 40 NA NA 0 0 0
## 41 NA NA 0 0 0
## 42 NA NA 0 0 0
## 43 NA NA 0 0 0
## 44 NA NA 0 0 0
## 45 NA NA 0 0 0
## 46 NA NA 0 0 0
## 47 NA NA 0 0 0
## 48 NA NA 0 0 0
## 49 NA NA 0 0 0
## 50 NA NA 0 0 0
## 51 NA NA 0 0 0
## 52 NA NA 0 0 0
## 53 NA NA 0 0 0
## 54 NA NA 0 0 0
## 55 NA NA 0 0 0
## 56 NA NA 0 0 0
## 57 NA NA 0 0 0
## 58 NA NA 0 0 0
## 59 NA NA 0 0 0
## 60 NA NA 1 0 0
## 61 NA NA 0 0 0
## 62 NA NA 0 0 0
## 63 NA NA 0 0 0
## 64 NA NA 0 0 0
## 65 NA NA 0 0 0
## 66 NA NA 0 0 0
## 67 NA NA 1 0 0
## 68 NA NA 0 0 0
## 69 NA NA 0 0 0
## 70 NA NA 0 0 0
## 71 NA NA 0 0 0
## 72 NA NA 1 0 0
## 73 NA NA 0 0 0
## 74 NA NA 0 0 0
## 75 NA NA 0 0 0
## 76 NA NA 0 0 0
## 77 NA NA 0 0 0
## 78 NA NA 0 0 0
## 79 NA NA 0 0 0
## 80 NA NA 0 0 0
## 81 NA NA 0 0 0
## 82 NA NA 0 0 0
## 83 NA NA 0 0 0
## 84 NA NA 0 0 0
## 85 NA NA 0 0 0
## 86 NA NA 0 0 0
## 87 NA NA 0 0 0
## 88 NA NA 0 0 0
## 89 NA NA 0 0 0
## 90 NA NA 0 0 0
## 91 NA NA 0 0 0
## 92 NA NA 0 0 0
## 93 NA NA 0 0 0
## 94 NA NA 1 0 0
## 95 NA NA 0 0 0
## 96 NA NA 0 0 0
## 97 NA NA 1 0 0
## 98 NA NA 0 0 0
## 99 NA NA 0 0 0
## 100 NA NA 0 0 0
## 101 NA NA 0 0 0
## 102 NA NA 0 0 0
## 103 NA NA 0 0 0
## 104 NA NA 0 0 0
## 105 NA NA 0 0 0
## 106 NA NA 0 0 0
## 107 NA NA 1 0 0
## 108 NA NA 0 0 0
## 109 NA NA 0 0 0
## 110 NA NA 0 0 0
## 111 NA NA 0 0 0
## 112 NA NA 1 0 0
## 113 NA NA 0 0 0
## 114 NA NA 0 0 0
## 115 NA NA 0 0 0
## 116 NA NA 2 0 0
## 117 NA NA 1 0 0
## 118 NA NA 0 0 0
## 119 NA NA 0 0 0
## 120 NA NA 0 0 0
## 121 NA NA 0 0 0
## 122 NA NA 1 0 0
## 123 NA NA 1 0 0
## 124 NA NA 0 0 0
## 125 NA NA 0 0 0
## 126 NA NA 0 0 0
## 127 NA NA 0 0 0
## 128 NA NA 0 0 0
## 129 NA NA 1 0 0
## 130 NA NA 0 0 0
## 131 NA NA 0 0 0
## 132 NA NA 0 0 0
## 133 NA NA 0 0 0
## 134 NA NA 0 0 0
## 135 NA NA 0 0 0
## 136 NA NA 0 0 0
## 137 NA NA 0 1 0
## 138 NA NA 0 0 0
## 139 NA NA 0 0 0
## 140 NA NA 0 0 0
## 141 NA NA 1 0 0
## 142 NA NA 1 0 0
## 143 NA NA 1 0 0
## 144 NA NA 1 0 0
## 145 NA NA 0 0 0
## 146 NA NA 0 0 0
## 147 NA NA 0 0 0
## 148 NA NA 0 0 0
## 149 NA NA 1 0 0
## 150 NA NA 0 0 0
## 151 NA NA 0 0 0
## 152 NA NA 0 0 0
## 153 NA NA 0 0 0
## 154 NA NA 0 0 0
## 155 NA NA 0 0 0
## 156 NA NA 0 0 0
## 157 NA NA 0 0 0
## 158 NA NA 0 0 0
## 159 NA NA 0 0 0
## 160 NA NA 1 0 0
## 161 NA NA 1 0 0
## 162 NA NA 0 0 0
## 163 NA NA 1 0 0
## 164 NA NA 1 0 0
## 165 NA NA 0 0 0
## 166 NA NA 0 0 0
## 167 NA NA 0 1 0
## 168 NA NA 0 2 0
## 169 NA NA 0 0 0
## cmTotHitsR.X2 cmTotHitsL.X2 cmTotHitsM.X2 TotLandsX2 TotMissedX2
## 1 NA NA NA 0 0
## 2 NA NA NA 0 1
## 3 NA NA NA 0 1
## 4 NA NA NA 0 0
## 5 NA NA NA 0 0
## 6 NA NA NA 0 1
## 7 NA NA NA 0 1
## 8 NA NA NA 0 0
## 9 NA NA NA 0 1
## 10 NA NA NA 0 0
## 11 NA NA NA 0 1
## 12 NA NA NA 0 1
## 13 NA NA NA 0 0
## 14 NA NA NA 0 0
## 15 NA NA NA 0 1
## 16 NA NA NA 0 0
## 17 NA NA NA 0 0
## 18 NA NA NA 0 1
## 19 NA NA NA 0 0
## 20 NA NA NA 1 0
## 21 NA NA NA 0 0
## 22 NA NA NA 0 0
## 23 NA NA NA 0 0
## 24 NA NA NA 0 0
## 25 NA NA NA 1 0
## 26 NA NA NA 0 0
## 27 NA NA NA 0 0
## 28 NA NA NA 0 0
## 29 NA NA NA 0 0
## 30 NA NA NA 0 0
## 31 NA NA NA 0 0
## 32 NA NA NA 0 0
## 33 NA NA NA 0 0
## 34 NA NA NA 0 0
## 35 NA NA NA 0 0
## 36 NA NA NA 0 0
## 37 NA NA NA 0 0
## 38 NA NA NA 0 0
## 39 NA NA NA 0 0
## 40 NA NA NA 0 0
## 41 NA NA NA 0 0
## 42 NA NA NA 0 0
## 43 NA NA NA 0 0
## 44 NA NA NA 0 0
## 45 NA NA NA 0 0
## 46 NA NA NA 0 0
## 47 NA NA NA 0 0
## 48 NA NA NA 0 0
## 49 NA NA NA 0 0
## 50 NA NA NA 0 0
## 51 NA NA NA 0 0
## 52 NA NA NA 0 0
## 53 NA NA NA 0 0
## 54 NA NA NA 0 0
## 55 NA NA NA 0 0
## 56 NA NA NA 0 0
## 57 NA NA NA 0 0
## 58 NA NA NA 0 0
## 59 NA NA NA 0 0
## 60 NA NA NA 0 0
## 61 NA NA NA 0 0
## 62 NA NA NA 0 0
## 63 NA NA NA 0 0
## 64 NA NA NA 0 0
## 65 NA NA NA 0 0
## 66 NA NA NA 0 0
## 67 NA NA NA 0 0
## 68 NA NA NA 0 0
## 69 NA NA NA 0 0
## 70 NA NA NA 0 0
## 71 NA NA NA 0 0
## 72 NA NA NA 0 0
## 73 NA NA NA 0 0
## 74 NA NA NA 0 0
## 75 NA NA NA 0 0
## 76 NA NA NA 0 0
## 77 NA NA NA 0 0
## 78 NA NA NA 0 0
## 79 NA NA NA 0 0
## 80 NA NA NA 0 0
## 81 NA NA NA 0 0
## 82 NA NA NA 0 0
## 83 NA NA NA 0 0
## 84 NA NA NA 0 0
## 85 NA NA NA 0 0
## 86 NA NA NA 0 0
## 87 NA NA NA 0 0
## 88 NA NA NA 0 0
## 89 NA NA NA 0 0
## 90 NA NA NA 0 0
## 91 NA NA NA 0 0
## 92 NA NA NA 0 0
## 93 NA NA NA 0 0
## 94 NA NA NA 0 0
## 95 NA NA NA 0 0
## 96 NA NA NA 0 0
## 97 NA NA NA 0 0
## 98 NA NA NA 0 0
## 99 NA NA NA 0 0
## 100 NA NA NA 0 0
## 101 NA NA NA 0 0
## 102 NA NA NA 0 0
## 103 NA NA NA 0 0
## 104 NA NA NA 0 0
## 105 NA NA NA 0 0
## 106 NA NA NA 0 0
## 107 NA NA NA 0 0
## 108 NA NA NA 0 0
## 109 NA NA NA 0 0
## 110 NA NA NA 0 0
## 111 NA NA NA 0 0
## 112 NA NA NA 0 0
## 113 NA NA NA 0 0
## 114 NA NA NA 0 0
## 115 NA NA NA 0 0
## 116 NA NA NA 0 0
## 117 NA NA NA 0 0
## 118 NA NA NA 0 0
## 119 NA NA NA 0 0
## 120 NA NA NA 0 0
## 121 NA NA NA 0 0
## 122 NA NA NA 0 0
## 123 NA NA NA 0 0
## 124 NA NA NA 0 0
## 125 NA NA NA 0 0
## 126 NA NA NA 0 0
## 127 NA NA NA 0 0
## 128 NA NA NA 0 0
## 129 NA NA NA 0 0
## 130 NA NA NA 0 0
## 131 NA NA NA 0 0
## 132 NA NA NA 0 0
## 133 NA NA NA 0 0
## 134 NA NA NA 0 0
## 135 NA NA NA 0 0
## 136 NA NA NA 0 0
## 137 NA NA NA 0 0
## 138 NA NA NA 0 0
## 139 NA NA NA 0 0
## 140 NA NA NA 0 0
## 141 NA NA NA 0 0
## 142 NA NA NA 0 0
## 143 NA NA NA 0 0
## 144 NA NA NA 0 0
## 145 NA NA NA 0 0
## 146 NA NA NA 0 0
## 147 NA NA NA 0 0
## 148 NA NA NA 0 0
## 149 NA NA NA 0 0
## 150 NA NA NA 0 0
## 151 NA NA NA 0 0
## 152 NA NA NA 0 0
## 153 NA NA NA 0 0
## 154 NA NA NA 0 0
## 155 NA NA NA 0 0
## 156 NA NA NA 0 0
## 157 NA NA NA 0 0
## 158 NA NA NA 0 0
## 159 NA NA NA 0 0
## 160 NA NA NA 0 0
## 161 NA NA NA 0 0
## 162 NA NA NA 0 0
## 163 NA NA NA 0 0
## 164 NA NA NA 0 0
## 165 NA NA NA 0 0
## 166 NA NA NA 0 0
## 167 NA NA NA 0 0
## 168 NA NA NA 0 0
## 169 NA NA NA 0 0
## TotReceivedX2 Time
## 1 0 4:55
## 2 0 4:54
## 3 0 4:50
## 4 0 4:48
## 5 0 4:46
## 6 0 4:40
## 7 0 4:39
## 8 0 4:36
## 9 0 4:32
## 10 0 4:31
## 11 0 4:29
## 12 0 4:22
## 13 2 4:21
## 14 1 4:19
## 15 0 4:18
## 16 0 4:14
## 17 0 4:13
## 18 0 4:12
## 19 0 4:08
## 20 0 4:02
## 21 0 3:56
## 22 0 3:51
## 23 0 3:50
## 24 1 3:49
## 25 0 3:47
## 26 0 3:46
## 27 0 3:45
## 28 0 3:44
## 29 0 3:43
## 30 0 3:42
## 31 0 3:41
## 32 0 3:40
## 33 0 3:39
## 34 0 3:38
## 35 0 3:37
## 36 0 3:36
## 37 0 3:35
## 38 0 3:34
## 39 0 3:33
## 40 0 3:32
## 41 0 3:31
## 42 0 3:30
## 43 0 3:29
## 44 0 3:28
## 45 0 3:27
## 46 0 3:26
## 47 0 3:25
## 48 0 3:24
## 49 0 3:23
## 50 0 3:22
## 51 0 3:21
## 52 0 3:20
## 53 0 3:19
## 54 0 3:18
## 55 0 3:17
## 56 0 3:16
## 57 0 3:15
## 58 0 3:14
## 59 0 3:13
## 60 2 3:12
## 61 0 3:11
## 62 0 3:10
## 63 0 3:09
## 64 0 3:08
## 65 0 3:07
## 66 0 3:06
## 67 2 3:05
## 68 0 3:04
## 69 0 3:03
## 70 0 3:02
## 71 0 3:01
## 72 1 3:00
## 73 0 2:59
## 74 0 2:58
## 75 0 2:57
## 76 0 2:56
## 77 0 2:55
## 78 0 2:54
## 79 0 2:53
## 80 0 2:52
## 81 0 2:51
## 82 0 2:50
## 83 0 2:49
## 84 0 2:48
## 85 0 2:47
## 86 0 2:46
## 87 0 2:45
## 88 0 2:44
## 89 0 2:43
## 90 0 2:42
## 91 0 2:41
## 92 0 2:40
## 93 0 2:39
## 94 1 2:38
## 95 0 2:37
## 96 0 2:36
## 97 1 2:35
## 98 0 2:34
## 99 0 2:33
## 100 0 2:32
## 101 0 2:31
## 102 0 2:30
## 103 0 2:29
## 104 0 2:28
## 105 0 2:27
## 106 0 2:26
## 107 1 2:25
## 108 0 2:24
## 109 0 2:23
## 110 0 2:22
## 111 0 2:21
## 112 1 2:20
## 113 0 2:19
## 114 0 2:18
## 115 0 2:17
## 116 2 2:16
## 117 1 2:15
## 118 0 2:14
## 119 0 2:13
## 120 0 2:12
## 121 0 2:11
## 122 1 2:10
## 123 1 2:09
## 124 0 2:08
## 125 0 2:07
## 126 0 2:06
## 127 0 2:05
## 128 0 2:04
## 129 1 2:03
## 130 0 2:02
## 131 0 2:01
## 132 0 2:00
## 133 0 1:59
## 134 0 1:58
## 135 0 1:57
## 136 0 1:56
## 137 0 1:55
## 138 0 1:54
## 139 0 1:53
## 140 0 1:52
## 141 2 1:51
## 142 2 1:50
## 143 2 1:49
## 144 2 1:48
## 145 0 1:47
## 146 0 1:46
## 147 0 1:45
## 148 0 1:44
## 149 1 1:43
## 150 0 1:42
## 151 0 1:41
## 152 0 1:40
## 153 0 1:39
## 154 0 1:38
## 155 0 1:37
## 156 0 1:36
## 157 0 1:35
## 158 0 1:34
## 159 0 1:33
## 160 1 1:32
## 161 1 1:31
## 162 0 1:30
## 163 1 1:29
## 164 1 1:28
## 165 0 1:27
## 166 0 1:26
## 167 0 1:25
## 168 0 1:24
## 169 0 1:23
## FighterActionReactions.X1
## 1 missed L jab
## 2 <NA>
## 3 <NA>
## 4 missed R cross, missed L jab
## 5 missed L jab
## 6 missed R cross
## 7 <NA>
## 8 missed L mt kick to low leg
## 9 <NA>
## 10 misses R cross
## 11 <NA>
## 12 misses L jab
## 13 lands R push kick to upper body
## 14 lands L mt kick to inside leg
## 15 misses L jab to face
## 16 missed R cross
## 17 misses attempted clinch
## 18 <NA>
## 19 misses L hook
## 20 <NA>
## 21 misses L jab to face
## 22 misses L mt kick to upper body and caught in L foot hold
## 23 breaks L foot hold
## 24 lands L jab to body
## 25 <NA>
## 26 misses body takedown
## 27 holding upper body hold starts and caught in L arm hold
## 28 holding upper body hold starts and caught in L arm hold
## 29 holding upper body hold starts and caught in L arm hold
## 30 holding upper body hold starts and caught in L arm hold
## 31 holding upper body hold starts and caught in L arm hold
## 32 holding upper body hold starts and caught in L arm hold
## 33 holding upper body hold starts and caught in L arm hold
## 34 holding upper body hold starts and caught in L arm hold
## 35 holding upper body hold starts and caught in L arm hold
## 36 holding upper body hold starts and caught in L arm hold
## 37 holding upper body hold starts and caught in L arm hold
## 38 holding upper body hold starts and caught in L arm hold
## 39 holding upper body hold starts and caught in L arm hold
## 40 holding upper body hold starts and caught in L arm hold
## 41 holding upper body hold starts and caught in L arm hold
## 42 holding upper body hold starts and caught in L arm hold
## 43 holding upper body hold starts and caught in L arm hold
## 44 holding upper body hold starts and caught in L arm hold
## 45 holding upper body hold starts and caught in L arm hold
## 46 holding upper body hold starts and caught in L arm hold
## 47 holding upper body hold starts and caught in L arm hold
## 48 holding upper body hold starts and caught in L arm hold
## 49 holding upper body hold starts and caught in L arm hold
## 50 holding upper body hold starts and caught in L arm hold
## 51 holding upper body hold starts and caught in L arm hold
## 52 holding upper body hold starts and caught in L arm hold
## 53 holding upper body hold starts and caught in L arm hold
## 54 holding upper body hold starts and caught in L arm hold
## 55 holding upper body hold starts and caught in L arm hold
## 56 holding upper body hold starts and caught in L arm hold
## 57 breaks L arm hold and caught in R arm hold while holding upper body continues
## 58 caught in R arm hold while holding upper body hold
## 59 caught in R arm hold while holding upper body hold
## 60 lands L knee to back of L leg while caught in R arm hold while holding upper body hold
## 61 caught in R arm hold while holding upper body hold
## 62 caught in R arm hold while holding upper body hold
## 63 caught in R arm hold while holding upper body hold
## 64 caught in R arm hold while holding upper body hold
## 65 caught in R arm hold while holding upper body hold
## 66 caught in R arm hold while holding upper body hold
## 67 lands judo type takedown while caught in R arm hold and while holding upper body
## 68 mount attempt and holding full mount hold starts
## 69 caught in R arm hold while holding full mount hold
## 70 caught in R arm hold while holding full mount hold
## 71 caught in R arm hold while holding full mount hold
## 72 lands L hook to head while caught in R arm hold and holding full mount hold
## 73 caught in R arm hold while holding full mount hold
## 74 caught in R arm hold while holding full mount hold
## 75 caught in R arm hold while holding full mount hold
## 76 caught in R arm hold while holding full mount hold
## 77 caught in R arm hold while holding full mount hold
## 78 caught in R arm hold while holding full mount hold
## 79 caught in R arm hold while holding full mount hold
## 80 caught in R arm hold while holding full mount hold
## 81 caught in R arm hold while holding full mount hold
## 82 caught in R arm hold while holding full mount hold
## 83 caught in R arm hold while holding full mount hold
## 84 caught in R arm hold while holding full mount hold
## 85 caught in R arm hold while holding full mount hold
## 86 caught in R arm hold while holding full mount hold
## 87 caught in R arm hold while holding full mount hold
## 88 caught in R arm hold while holding full mount hold
## 89 caught in R arm hold while holding full mount hold
## 90 breaks R arm hold while holding full mount hold
## 91 holding full mount hold continues
## 92 holding full mount hold continues
## 93 holding full mount hold continues
## 94 lands elbow to face holding full mount hold continues
## 95 holding full mount hold continues
## 96 holding full mount hold continues
## 97 lands R hook to face holding full mount hold continues
## 98 holding full mount hold continues
## 99 caught in body hold and holding full mount hold continues
## 100 breaks body hold and holding full mount hold continues
## 101 holding full mount hold continues
## 102 holding full mount hold continues
## 103 holding full mount hold continues
## 104 holding full mount hold continues
## 105 caught in body hold and holding full mount hold continues
## 106 caught in body hold and holding full mount hold continues
## 107 breaks body hold and lands L elbow to face and holding full mount hold continues
## 108 holding full mount hold continues
## 109 holding full mount hold continues
## 110 holding full mount hold continues
## 111 holding full mount hold continues
## 112 lands L cross to face and holding full mount hold continues
## 113 holding full mount hold continues
## 114 holding full mount hold continues
## 115 holding full mount hold continues
## 116 holding full mount hold continues and lands R elbow to face, lands R elbow to face
## 117 lands R elbow to face and holding full mount hold continues
## 118 holding full mount hold continues
## 119 holding full mount hold continues
## 120 holding full mount hold continues
## 121 holding full mount hold continues
## 122 lands L elbow to face and holding full mount hold continues
## 123 lands L elbow to face and holding full mount hold continues
## 124 holding full mount hold continues
## 125 holding full mount hold continues
## 126 holding full mount hold continues
## 127 holding full mount hold continues
## 128 holding full mount hold continues
## 129 lands L cross to face and holding full mount hold continues
## 130 holding full mount hold continues
## 131 holding full mount hold continues
## 132 loses full mount hold and holding side mount hold starts
## 133 holding side mount hold continues
## 134 holding back mount hold starts and loses side mount hold
## 135 holding back mount hold continues
## 136 holding back mount hold continues
## 137 misses L hook to face holding back mount hold continues
## 138 loses back mount hold and holding full mount hold starts
## 139 holding full mount hold on top and caught in upper body hold
## 140 holding full mount hold on top and caught in upper body hold
## 141 lands R hook to face holding full mount hold continues and caught in upper body hold
## 142 lands R hook to face holding full mount hold continues and caught in upper body hold
## 143 lands L elbow to face holding full mount hold continues and caught in upper body hold
## 144 lands L elbow to face holding full mount hold continues and caught in upper body hold
## 145 holding full mount hold continues and caught in upper body hold
## 146 loses full mount hold and holding side control mount starts and breaks upper body hold
## 147 holding side mount hold continues
## 148 holding side mount hold continues
## 149 lands L hammer to face holding side mount hold continues
## 150 holding side mount hold continues
## 151 holding side mount hold continues
## 152 loses side mount hold and holding full mount hold starts
## 153 holding full mount hold continues
## 154 holding full mount hold continues
## 155 holding full mount hold continues
## 156 holding full mount hold continues
## 157 holding full mount hold continues
## 158 holding full mount hold continues
## 159 holding full mount hold continues
## 160 holding full mount hold lands R hook to face
## 161 holding full mount hold lands R hook to face
## 162 holding full mount hold
## 163 holding full mount hold lands L hook to face
## 164 holding full mount hold lands L hook to face
## 165 holding full mount hold
## 166 holding full mount hold
## 167 holding full mount hold misses L elbow to face
## 168 holding full mount hold misses L elbow to face, misses L elbow to face
## 169 TKO referree stoppage
## FightersActionsReactions.X2
## 1 <NA>
## 2 missed R cross
## 3 missed R cross
## 4 <NA>
## 5 <NA>
## 6 missed L cross
## 7 missed L hook
## 8 <NA>
## 9 misses L jab
## 10 <NA>
## 11 missed L jab
## 12 misses L jab
## 13 <NA>
## 14 <NA>
## 15 missed L hook to face
## 16 <NA>
## 17 <NA>
## 18 misses L hook to face
## 19 <NA>
## 20 landed jab to face
## 21 <NA>
## 22 holding L foot hold starts while pushing back into cage
## 23 loses L foot hold
## 24 <NA>
## 25 lands L jab to body
## 26 <NA>
## 27 caught in upper body hold and holding L arm hold starts
## 28 caught in upper body hold and holding L arm hold starts
## 29 caught in upper body hold and holding L arm hold starts
## 30 caught in upper body hold and holding L arm hold starts
## 31 caught in upper body hold and holding L arm hold starts
## 32 caught in upper body hold and holding L arm hold starts
## 33 caught in upper body hold and holding L arm hold starts
## 34 caught in upper body hold and holding L arm hold starts
## 35 caught in upper body hold and holding L arm hold starts
## 36 caught in upper body hold and holding L arm hold starts
## 37 caught in upper body hold and holding L arm hold starts
## 38 caught in upper body hold and holding L arm hold starts
## 39 caught in upper body hold and holding L arm hold starts
## 40 caught in upper body hold and holding L arm hold starts
## 41 caught in upper body hold and holding L arm hold starts
## 42 caught in upper body hold and holding L arm hold starts
## 43 caught in upper body hold and holding L arm hold starts
## 44 caught in upper body hold and holding L arm hold starts
## 45 caught in upper body hold and holding L arm hold starts
## 46 caught in upper body hold and holding L arm hold starts
## 47 caught in upper body hold and holding L arm hold starts
## 48 caught in upper body hold and holding L arm hold starts
## 49 caught in upper body hold and holding L arm hold starts
## 50 caught in upper body hold and holding L arm hold starts
## 51 caught in upper body hold and holding L arm hold starts
## 52 caught in upper body hold and holding L arm hold starts
## 53 caught in upper body hold and holding L arm hold starts
## 54 caught in upper body hold and holding L arm hold starts
## 55 caught in upper body hold and holding L arm hold starts
## 56 caught in upper body hold and holding L arm hold starts
## 57 loses L arm hold and holding R arm hold starts while caught in upper body hold
## 58 holding R arm hold while caught in upper body hold
## 59 holding R arm hold while caught in upper body hold
## 60 holding R arm hold while caught in upper body hold
## 61 holding R arm hold while caught in upper body hold
## 62 holding R arm hold while caught in upper body hold
## 63 holding R arm hold while caught in upper body hold
## 64 holding R arm hold while caught in upper body hold
## 65 holding R arm hold while caught in upper body hold
## 66 holding R arm hold while caught in upper body hold
## 67 <NA>
## 68 on side preventing full mount and passing while caught in full mount hold and holding R arm hold continues
## 69 holding R arm while caught in full mount hold
## 70 holding R arm while caught in full mount hold
## 71 holding R arm while caught in full mount hold
## 72 holding R arm while caught in full mount hold
## 73 holding R arm while caught in full mount hold
## 74 holding R arm while caught in full mount hold
## 75 holding R arm while caught in full mount hold
## 76 holding R arm while caught in full mount hold
## 77 holding R arm while caught in full mount hold
## 78 holding R arm while caught in full mount hold
## 79 holding R arm while caught in full mount hold
## 80 holding R arm while caught in full mount hold
## 81 holding R arm while caught in full mount hold
## 82 holding R arm while caught in full mount hold
## 83 holding R arm while caught in full mount hold
## 84 holding R arm while caught in full mount hold
## 85 holding R arm while caught in full mount hold
## 86 holding R arm while caught in full mount hold
## 87 holding R arm while caught in full mount hold
## 88 holding R arm while caught in full mount hold
## 89 holding R arm while caught in full mount hold
## 90 loses R arm hold while caught in full mount hold
## 91 caught in full mount hold
## 92 caught in full mount hold
## 93 caught in full mount hold
## 94 caught in full mount hold
## 95 caught in full mount hold
## 96 caught in full mount hold
## 97 caught in full mount hold
## 98 caught in full mount hold
## 99 caught in full mount hold and holding body hold starts
## 100 caught in full mount hold and loses body hold
## 101 caught in full mount hold
## 102 caught in full mount hold
## 103 caught in full mount hold
## 104 caught in full mount hold
## 105 caught in full mount hold and holding body hold starts
## 106 caught in full mount hold and holding body hold starts
## 107 caught in full mount hold and loses body hold
## 108 caught in full mount hold
## 109 caught in full mount hold
## 110 caught in full mount hold
## 111 caught in full mount hold
## 112 caught in full mount hold
## 113 caught in full mount hold
## 114 caught in full mount hold
## 115 caught in full mount hold
## 116 caught in full mount hold
## 117 caught in full mount hold
## 118 caught in full mount hold
## 119 caught in full mount hold
## 120 caught in full mount hold
## 121 caught in full mount hold
## 122 caught in full mount hold
## 123 caught in full mount hold
## 124 caught in full mount hold
## 125 caught in full mount hold
## 126 caught in full mount hold
## 127 caught in full mount hold
## 128 caught in full mount hold
## 129 caught in full mount hold
## 130 caught in full mount hold
## 131 caught in full mount hold
## 132 breaks full mount hold and caught in side mount hold
## 133 caught in side mount hold
## 134 caught in back mount hold
## 135 caught in back mount hold
## 136 caught in back mount hold
## 137 caught in back mount hold
## 138 breaks back mount hold and caught in full mount hold
## 139 caught in full mount hold and holding body hold starts
## 140 caught in full mount hold and holding upper body hold continues
## 141 caught in full mount hold and holding upper body hold continues
## 142 caught in full mount hold and holding upper body hold continues
## 143 caught in full mount hold and holding upper body hold continues
## 144 caught in full mount hold and holding upper body hold continues
## 145 caught in full mount hold and holding upper body hold continues
## 146 breaks full mount hold and caught in side mount hold and loses upper body hold
## 147 caught in side mount hold
## 148 caught in side mount hold
## 149 caught in side mount hold
## 150 caught in side mount hold
## 151 caught in side mount hold
## 152 breaks side mount hold and caught in full mount hold
## 153 caught in full mount hold
## 154 caught in full mount hold
## 155 caught in full mount hold
## 156 caught in full mount hold
## 157 caught in full mount hold
## 158 caught in full mount hold
## 159 caught in full mount hold
## 160 caught in full mount hold
## 161 caught in full mount hold
## 162 caught in full mount hold
## 163 caught in full mount hold
## 164 caught in full mount hold
## 165 caught in full mount hold
## 166 caught in full mount hold
## 167 caught in full mount hold
## 168 caught in full mount hold
## 169 caught in full mount hold
## Notes Crossl.X1 Kneel.X1 Elbowl.X1 Hookl.X1 Jabl.X1 Kickl.X1 Crossl.X2
## 1 Zarah 0 0 0 0 0 0 0
## 2 Zarah 0 0 0 0 0 0 0
## 3 Zarah 0 0 0 0 0 0 0
## 4 Zarah 0 0 0 0 0 0 0
## 5 Zarah 0 0 0 0 0 0 0
## 6 Zarah 0 0 0 0 0 0 0
## 7 Zarah 0 0 0 0 0 0 0
## 8 Zarah 0 0 0 0 0 0 0
## 9 Zarah 0 0 0 0 0 0 0
## 10 Zarah 0 0 0 0 0 0 0
## 11 Zarah 0 0 0 0 0 0 0
## 12 Zarah 0 0 0 0 0 0 0
## 13 Zarah 0 0 0 0 0 1 0
## 14 Zarah 0 0 0 0 0 1 0
## 15 Zarah 0 0 0 0 0 0 0
## 16 Zarah 0 0 0 0 0 0 0
## 17 Zarah 0 0 0 0 0 0 0
## 18 Zarah 0 0 0 0 0 0 0
## 19 Zarah 0 0 0 0 0 0 0
## 20 Zarah 0 0 0 0 0 0 0
## 21 Zarah 0 0 0 0 0 0 0
## 22 Zarah 0 0 0 0 0 0 0
## 23 Zarah 0 0 0 0 0 0 0
## 24 Zarah 0 0 0 0 1 0 0
## 25 Zarah 0 0 0 0 0 0 0
## 26 Zarah 0 0 0 0 0 0 0
## 27 Zarah 0 0 0 0 0 0 0
## 28 Zarah 0 0 0 0 0 0 0
## 29 Zarah 0 0 0 0 0 0 0
## 30 Zarah 0 0 0 0 0 0 0
## 31 Zarah 0 0 0 0 0 0 0
## 32 Zarah 0 0 0 0 0 0 0
## 33 Zarah 0 0 0 0 0 0 0
## 34 Zarah 0 0 0 0 0 0 0
## 35 Zarah 0 0 0 0 0 0 0
## 36 Zarah 0 0 0 0 0 0 0
## 37 Zarah 0 0 0 0 0 0 0
## 38 Zarah 0 0 0 0 0 0 0
## 39 Zarah 0 0 0 0 0 0 0
## 40 Zarah 0 0 0 0 0 0 0
## 41 Zarah 0 0 0 0 0 0 0
## 42 Zarah 0 0 0 0 0 0 0
## 43 Zarah 0 0 0 0 0 0 0
## 44 Zarah 0 0 0 0 0 0 0
## 45 Zarah 0 0 0 0 0 0 0
## 46 Zarah 0 0 0 0 0 0 0
## 47 Zarah 0 0 0 0 0 0 0
## 48 Zarah 0 0 0 0 0 0 0
## 49 Zarah 0 0 0 0 0 0 0
## 50 Zarah 0 0 0 0 0 0 0
## 51 Zarah 0 0 0 0 0 0 0
## 52 Zarah 0 0 0 0 0 0 0
## 53 Zarah 0 0 0 0 0 0 0
## 54 Zarah 0 0 0 0 0 0 0
## 55 Zarah 0 0 0 0 0 0 0
## 56 Zarah 0 0 0 0 0 0 0
## 57 Zarah 0 0 0 0 0 0 0
## 58 Zarah 0 0 0 0 0 0 0
## 59 Zarah 0 0 0 0 0 0 0
## 60 Zarah 0 1 0 0 0 0 0
## 61 Zarah 0 0 0 0 0 0 0
## 62 Zarah 0 0 0 0 0 0 0
## 63 Zarah 0 0 0 0 0 0 0
## 64 Zarah 0 0 0 0 0 0 0
## 65 Zarah 0 0 0 0 0 0 0
## 66 Zarah 0 0 0 0 0 0 0
## 67 Zarah 0 0 0 0 0 0 0
## 68 Zarah 0 0 0 0 0 0 0
## 69 Zarah 0 0 0 0 0 0 0
## 70 Zarah 0 0 0 0 0 0 0
## 71 Zarah 0 0 0 0 0 0 0
## 72 Zarah 0 0 0 1 0 0 0
## 73 Zarah 0 0 0 0 0 0 0
## 74 Zarah 0 0 0 0 0 0 0
## 75 Zarah 0 0 0 0 0 0 0
## 76 Zarah 0 0 0 0 0 0 0
## 77 Zarah 0 0 0 0 0 0 0
## 78 Zarah 0 0 0 0 0 0 0
## 79 Zarah 0 0 0 0 0 0 0
## 80 Zarah 0 0 0 0 0 0 0
## 81 Zarah 0 0 0 0 0 0 0
## 82 Zarah 0 0 0 0 0 0 0
## 83 Zarah 0 0 0 0 0 0 0
## 84 Zarah 0 0 0 0 0 0 0
## 85 Zarah 0 0 0 0 0 0 0
## 86 Zarah 0 0 0 0 0 0 0
## 87 Zarah 0 0 0 0 0 0 0
## 88 Zarah 0 0 0 0 0 0 0
## 89 Zarah 0 0 0 0 0 0 0
## 90 Zarah 0 0 0 0 0 0 0
## 91 Zarah 0 0 0 0 0 0 0
## 92 Zarah 0 0 0 0 0 0 0
## 93 Zarah 0 0 0 0 0 0 0
## 94 Zarah 0 0 1 0 0 0 0
## 95 Zarah 0 0 0 0 0 0 0
## 96 Zarah 0 0 0 0 0 0 0
## 97 Zarah 0 0 0 1 0 0 0
## 98 Zarah 0 0 0 0 0 0 0
## 99 Zarah 0 0 0 0 0 0 0
## 100 Zarah 0 0 0 0 0 0 0
## 101 Zarah 0 0 0 0 0 0 0
## 102 Zarah 0 0 0 0 0 0 0
## 103 Zarah 0 0 0 0 0 0 0
## 104 Zarah 0 0 0 0 0 0 0
## 105 Zarah 0 0 0 0 0 0 0
## 106 Zarah 0 0 0 0 0 0 0
## 107 Zarah 0 0 1 0 0 0 0
## 108 Zarah 0 0 0 0 0 0 0
## 109 Zarah 0 0 0 0 0 0 0
## 110 Zarah 0 0 0 0 0 0 0
## 111 Zarah 0 0 0 0 0 0 0
## 112 Zarah 1 0 0 0 0 0 0
## 113 Zarah 0 0 0 0 0 0 0
## 114 Zarah 0 0 0 0 0 0 0
## 115 Zarah 0 0 0 0 0 0 0
## 116 Zarah 0 0 1 0 0 0 0
## 117 Zarah 0 0 1 0 0 0 0
## 118 Zarah 0 0 0 0 0 0 0
## 119 Zarah 0 0 0 0 0 0 0
## 120 Zarah 0 0 0 0 0 0 0
## 121 Zarah 0 0 0 0 0 0 0
## 122 Zarah 0 0 1 0 0 0 0
## 123 Zarah 0 0 1 0 0 0 0
## 124 Zarah 0 0 0 0 0 0 0
## 125 Zarah 0 0 0 0 0 0 0
## 126 Zarah 0 0 0 0 0 0 0
## 127 Zarah 0 0 0 0 0 0 0
## 128 Zarah 0 0 0 0 0 0 0
## 129 Zarah 1 0 0 0 0 0 0
## 130 Zarah 0 0 0 0 0 0 0
## 131 Zarah 0 0 0 0 0 0 0
## 132 Zarah 0 0 0 0 0 0 0
## 133 Zarah 0 0 0 0 0 0 0
## 134 Zarah 0 0 0 0 0 0 0
## 135 Zarah 0 0 0 0 0 0 0
## 136 Zarah 0 0 0 0 0 0 0
## 137 Zarah 0 0 0 0 0 0 0
## 138 Zarah 0 0 0 0 0 0 0
## 139 Zarah 0 0 0 0 0 0 0
## 140 Zarah 0 0 0 0 0 0 0
## 141 Zarah 0 0 0 1 0 0 0
## 142 Zarah 0 0 0 1 0 0 0
## 143 Zarah 0 0 1 0 0 0 0
## 144 Zarah 0 0 1 0 0 0 0
## 145 Zarah 0 0 0 0 0 0 0
## 146 Zarah 0 0 0 0 0 0 0
## 147 Zarah 0 0 0 0 0 0 0
## 148 Zarah 0 0 0 0 0 0 0
## 149 Zarah 0 0 0 0 0 0 0
## 150 Zarah 0 0 0 0 0 0 0
## 151 Zarah 0 0 0 0 0 0 0
## 152 Zarah 0 0 0 0 0 0 0
## 153 Zarah 0 0 0 0 0 0 0
## 154 Zarah 0 0 0 0 0 0 0
## 155 Zarah 0 0 0 0 0 0 0
## 156 Zarah 0 0 0 0 0 0 0
## 157 Zarah 0 0 0 0 0 0 0
## 158 Zarah 0 0 0 0 0 0 0
## 159 Zarah 0 0 0 0 0 0 0
## 160 Zarah 0 0 0 1 0 0 0
## 161 Zarah 0 0 0 1 0 0 0
## 162 Zarah 0 0 0 0 0 0 0
## 163 Zarah 0 0 0 1 0 0 0
## 164 Zarah 0 0 0 1 0 0 0
## 165 Zarah 0 0 0 0 0 0 0
## 166 Zarah 0 0 0 0 0 0 0
## 167 Zarah 0 0 0 0 0 0 0
## 168 Zarah 0 0 0 0 0 0 0
## 169 Zarah 0 0 0 0 0 0 0
## Kneel.X2 Elbowl.X2 Hookl.X2 Jabl.X2 Kickl.X2 upperl.X1 upperl.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0
## 20 0 0 0 1 0 0 0
## 21 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0
## 25 0 0 0 1 0 0 0
## 26 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0
## 32 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0
## 34 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## 37 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 0
## 39 0 0 0 0 0 0 0
## 40 0 0 0 0 0 0 0
## 41 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0
## 43 0 0 0 0 0 0 0
## 44 0 0 0 0 0 0 0
## 45 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0
## 48 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0
## 51 0 0 0 0 0 0 0
## 52 0 0 0 0 0 0 0
## 53 0 0 0 0 0 0 0
## 54 0 0 0 0 0 0 0
## 55 0 0 0 0 0 0 0
## 56 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0
## 58 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0
## 60 0 0 0 0 0 0 0
## 61 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0
## 64 0 0 0 0 0 0 0
## 65 0 0 0 0 0 0 0
## 66 0 0 0 0 0 0 0
## 67 0 0 0 0 0 0 0
## 68 0 0 0 0 0 0 0
## 69 0 0 0 0 0 0 0
## 70 0 0 0 0 0 0 0
## 71 0 0 0 0 0 0 0
## 72 0 0 0 0 0 0 0
## 73 0 0 0 0 0 0 0
## 74 0 0 0 0 0 0 0
## 75 0 0 0 0 0 0 0
## 76 0 0 0 0 0 0 0
## 77 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0
## 80 0 0 0 0 0 0 0
## 81 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0
## 83 0 0 0 0 0 0 0
## 84 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0
## 86 0 0 0 0 0 0 0
## 87 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0
## 89 0 0 0 0 0 0 0
## 90 0 0 0 0 0 0 0
## 91 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0
## 94 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## 96 0 0 0 0 0 0 0
## 97 0 0 0 0 0 0 0
## 98 0 0 0 0 0 0 0
## 99 0 0 0 0 0 0 0
## 100 0 0 0 0 0 0 0
## 101 0 0 0 0 0 0 0
## 102 0 0 0 0 0 0 0
## 103 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## 105 0 0 0 0 0 0 0
## 106 0 0 0 0 0 0 0
## 107 0 0 0 0 0 0 0
## 108 0 0 0 0 0 0 0
## 109 0 0 0 0 0 0 0
## 110 0 0 0 0 0 0 0
## 111 0 0 0 0 0 0 0
## 112 0 0 0 0 0 0 0
## 113 0 0 0 0 0 0 0
## 114 0 0 0 0 0 0 0
## 115 0 0 0 0 0 0 0
## 116 0 0 0 0 0 0 0
## 117 0 0 0 0 0 0 0
## 118 0 0 0 0 0 0 0
## 119 0 0 0 0 0 0 0
## 120 0 0 0 0 0 0 0
## 121 0 0 0 0 0 0 0
## 122 0 0 0 0 0 0 0
## 123 0 0 0 0 0 0 0
## 124 0 0 0 0 0 0 0
## 125 0 0 0 0 0 0 0
## 126 0 0 0 0 0 0 0
## 127 0 0 0 0 0 0 0
## 128 0 0 0 0 0 0 0
## 129 0 0 0 0 0 0 0
## 130 0 0 0 0 0 0 0
## 131 0 0 0 0 0 0 0
## 132 0 0 0 0 0 0 0
## 133 0 0 0 0 0 0 0
## 134 0 0 0 0 0 0 0
## 135 0 0 0 0 0 0 0
## 136 0 0 0 0 0 0 0
## 137 0 0 0 0 0 0 0
## 138 0 0 0 0 0 0 0
## 139 0 0 0 0 0 0 0
## 140 0 0 0 0 0 0 0
## 141 0 0 0 0 0 0 0
## 142 0 0 0 0 0 0 0
## 143 0 0 0 0 0 0 0
## 144 0 0 0 0 0 0 0
## 145 0 0 0 0 0 0 0
## 146 0 0 0 0 0 0 0
## 147 0 0 0 0 0 0 0
## 148 0 0 0 0 0 0 0
## 149 0 0 0 0 0 0 0
## 150 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0
## 152 0 0 0 0 0 0 0
## 153 0 0 0 0 0 0 0
## 154 0 0 0 0 0 0 0
## 155 0 0 0 0 0 0 0
## 156 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## 158 0 0 0 0 0 0 0
## 159 0 0 0 0 0 0 0
## 160 0 0 0 0 0 0 0
## 161 0 0 0 0 0 0 0
## 162 0 0 0 0 0 0 0
## 163 0 0 0 0 0 0 0
## 164 0 0 0 0 0 0 0
## 165 0 0 0 0 0 0 0
## 166 0 0 0 0 0 0 0
## 167 0 0 0 0 0 0 0
## 168 0 0 0 0 0 0 0
## 169 0 0 0 0 0 0 0
## takedownl.X1 takedownl.X2 hammerl.X1 hammerl.X2 Cross2l.X1 Knee2l.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## 10 0 0 0 0 0 0
## 11 0 0 0 0 0 0
## 12 0 0 0 0 0 0
## 13 0 0 0 0 0 0
## 14 0 0 0 0 0 0
## 15 0 0 0 0 0 0
## 16 0 0 0 0 0 0
## 17 0 0 0 0 0 0
## 18 0 0 0 0 0 0
## 19 0 0 0 0 0 0
## 20 0 0 0 0 0 0
## 21 0 0 0 0 0 0
## 22 0 0 0 0 0 0
## 23 0 0 0 0 0 0
## 24 0 0 0 0 0 0
## 25 0 0 0 0 0 0
## 26 0 0 0 0 0 0
## 27 0 0 0 0 0 0
## 28 0 0 0 0 0 0
## 29 0 0 0 0 0 0
## 30 0 0 0 0 0 0
## 31 0 0 0 0 0 0
## 32 0 0 0 0 0 0
## 33 0 0 0 0 0 0
## 34 0 0 0 0 0 0
## 35 0 0 0 0 0 0
## 36 0 0 0 0 0 0
## 37 0 0 0 0 0 0
## 38 0 0 0 0 0 0
## 39 0 0 0 0 0 0
## 40 0 0 0 0 0 0
## 41 0 0 0 0 0 0
## 42 0 0 0 0 0 0
## 43 0 0 0 0 0 0
## 44 0 0 0 0 0 0
## 45 0 0 0 0 0 0
## 46 0 0 0 0 0 0
## 47 0 0 0 0 0 0
## 48 0 0 0 0 0 0
## 49 0 0 0 0 0 0
## 50 0 0 0 0 0 0
## 51 0 0 0 0 0 0
## 52 0 0 0 0 0 0
## 53 0 0 0 0 0 0
## 54 0 0 0 0 0 0
## 55 0 0 0 0 0 0
## 56 0 0 0 0 0 0
## 57 0 0 0 0 0 0
## 58 0 0 0 0 0 0
## 59 0 0 0 0 0 0
## 60 0 0 0 0 0 0
## 61 0 0 0 0 0 0
## 62 0 0 0 0 0 0
## 63 0 0 0 0 0 0
## 64 0 0 0 0 0 0
## 65 0 0 0 0 0 0
## 66 0 0 0 0 0 0
## 67 1 0 0 0 0 0
## 68 0 0 0 0 0 0
## 69 0 0 0 0 0 0
## 70 0 0 0 0 0 0
## 71 0 0 0 0 0 0
## 72 0 0 0 0 0 0
## 73 0 0 0 0 0 0
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## 75 0 0 0 0 0 0
## 76 0 0 0 0 0 0
## 77 0 0 0 0 0 0
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## 80 0 0 0 0 0 0
## 81 0 0 0 0 0 0
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## takedown2r.X2 hammer2r.X1 hammer2r.X2 Cross3r.X1 Knee3r.X1 Elbow3r.X1
## 1 0 0 0 0 0 0
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## Hook3r.X1 Jab3r.X1 Kick3r.X1 Cross3r.X2 Knee3r.X2 Elbow3r.X2 Hook3r.X2
## 1 0 0 0 0 0 0 0
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## Jab3r.X2 Kick3r.X2 upper3r.X1 upper3r.X2 takedown3r.X1 takedown3r.X2
## 1 0 0 0 0 0 0
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## hammer3r.X1 hammer3r.X2
## 1 0 0
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## 169 0 0Second opponent:
Table2 <- Added3[split1:(split2-1),]
Table2## Round SecondsIntoRound lastAction SecondsLastRoundAction cmTotHitsR.X1
## 170 1 2 216 2 NA
## 171 1 6 2 4 NA
## 172 1 7 6 1 NA
## 173 1 8 7 1 NA
## 174 1 11 8 3 NA
## 175 1 17 11 6 NA
## 176 1 21 17 4 NA
## 177 1 24 21 3 NA
## 178 1 25 24 1 NA
## 179 1 26 25 1 NA
## 180 1 27 26 1 NA
## 181 1 28 27 1 NA
## 182 1 29 28 1 NA
## 183 1 30 29 1 NA
## 184 1 31 30 1 NA
## 185 1 32 31 1 NA
## 186 1 33 32 1 NA
## 187 1 34 33 1 NA
## 188 1 35 34 1 NA
## 189 1 36 35 1 NA
## 190 1 37 36 1 NA
## 191 1 38 37 1 NA
## 192 1 39 38 1 NA
## 193 1 40 39 1 NA
## 194 1 41 40 1 NA
## 195 1 42 41 1 NA
## 196 1 43 42 1 NA
## 197 1 44 43 1 NA
## 198 1 45 44 1 NA
## 199 1 46 45 1 NA
## 200 1 47 46 1 NA
## 201 1 48 47 1 NA
## 202 1 49 48 1 NA
## 203 1 50 49 1 NA
## 204 1 51 50 1 NA
## 205 1 52 51 1 NA
## 206 1 53 52 1 NA
## 207 1 54 53 1 NA
## 208 1 55 54 1 NA
## 209 1 56 55 1 NA
## 210 1 57 56 1 NA
## 211 1 58 57 1 NA
## 212 1 59 58 1 NA
## 213 1 60 59 1 NA
## 214 1 61 60 1 NA
## 215 1 62 61 1 NA
## 216 1 63 62 1 NA
## 217 1 64 63 1 NA
## 218 1 65 64 1 NA
## 219 1 66 65 1 NA
## 220 1 67 66 1 NA
## 221 1 68 67 1 NA
## 222 1 69 68 1 NA
## 223 1 70 69 1 NA
## 224 1 71 70 1 NA
## 225 1 72 71 1 NA
## 226 1 73 72 1 NA
## 227 1 74 73 1 NA
## 228 1 75 74 1 NA
## 229 1 76 75 1 NA
## 230 1 77 76 1 NA
## 231 1 79 77 2 NA
## 232 1 81 79 2 NA
## 233 1 82 81 1 NA
## 234 1 83 82 1 NA
## 235 1 84 83 1 NA
## 236 1 85 84 1 NA
## 237 1 86 85 1 NA
## 238 1 87 86 1 NA
## 239 1 88 87 1 NA
## 240 1 89 88 1 NA
## 241 1 90 89 1 NA
## 242 1 91 90 1 NA
## 243 1 92 91 1 NA
## 244 1 93 92 1 NA
## 245 1 94 93 1 NA
## 246 1 95 94 1 NA
## 247 1 96 95 1 NA
## 248 1 97 96 1 NA
## 249 1 98 97 1 NA
## 250 1 99 98 1 NA
## 251 1 100 99 1 NA
## 252 1 101 100 1 NA
## 253 1 102 101 1 NA
## 254 1 103 102 1 NA
## 255 1 104 103 1 NA
## 256 1 105 104 1 NA
## 257 1 106 105 1 NA
## 258 1 107 106 1 NA
## 259 1 108 107 1 NA
## 260 1 109 108 1 NA
## 261 1 110 109 1 NA
## 262 1 111 110 1 NA
## 263 1 112 111 1 NA
## 264 1 113 112 1 NA
## 265 1 114 113 1 NA
## 266 1 115 114 1 NA
## 267 1 116 115 1 NA
## 268 1 117 116 1 NA
## 269 1 118 117 1 NA
## 270 1 119 118 1 NA
## 271 1 120 119 1 NA
## 272 1 121 120 1 NA
## 273 1 122 121 1 NA
## 274 1 123 122 1 NA
## 275 1 124 123 1 NA
## 276 1 125 124 1 NA
## 277 1 126 125 1 NA
## 278 1 127 126 1 NA
## 279 1 128 127 1 NA
## 280 1 129 128 1 NA
## 281 1 130 129 1 NA
## 282 1 131 130 1 NA
## 283 1 132 131 1 NA
## 284 1 133 132 1 NA
## 285 1 134 133 1 NA
## 286 1 135 134 1 NA
## 287 1 136 135 1 NA
## 288 1 137 136 1 NA
## 289 1 138 137 1 NA
## 290 1 139 138 1 NA
## 291 1 140 139 1 NA
## 292 1 141 140 1 NA
## 293 1 142 141 1 NA
## 294 1 143 142 1 NA
## 295 1 144 143 1 NA
## 296 1 145 144 1 NA
## 297 1 146 145 1 NA
## 298 1 147 146 1 NA
## 299 1 148 147 1 NA
## 300 1 149 148 1 NA
## 301 1 150 149 1 NA
## 302 1 151 150 1 NA
## 303 1 152 151 1 NA
## 304 1 153 152 1 NA
## 305 1 154 153 1 NA
## 306 1 155 154 1 NA
## 307 1 156 155 1 NA
## 308 1 157 156 1 NA
## 309 1 158 157 1 NA
## 310 1 159 158 1 NA
## 311 1 160 159 1 NA
## 312 1 161 160 1 NA
## 313 1 162 161 1 NA
## 314 1 163 162 1 NA
## 315 1 164 163 1 NA
## 316 1 165 164 1 NA
## 317 1 166 165 1 NA
## 318 1 167 166 1 NA
## 319 1 168 167 1 NA
## 320 1 169 168 1 NA
## 321 1 170 169 1 NA
## 322 1 171 170 1 NA
## 323 1 172 171 1 NA
## 324 1 173 172 1 NA
## 325 1 174 173 1 NA
## 326 1 175 174 1 NA
## 327 1 176 175 1 NA
## 328 1 177 176 1 NA
## 329 1 178 177 1 NA
## 330 1 179 178 1 NA
## 331 1 180 179 1 NA
## 332 1 181 180 1 NA
## 333 1 182 181 1 NA
## 334 1 183 182 1 NA
## 335 1 184 183 1 NA
## 336 1 185 184 1 NA
## 337 1 186 185 1 NA
## 338 1 187 186 1 NA
## 339 1 188 187 1 NA
## 340 1 189 188 1 NA
## 341 1 190 189 1 NA
## 342 1 191 190 1 NA
## 343 1 192 191 1 NA
## 344 1 193 192 1 NA
## 345 1 194 193 1 NA
## 346 1 195 194 1 NA
## 347 1 196 195 1 NA
## 348 1 197 196 1 NA
## 349 1 198 197 1 NA
## 350 1 199 198 1 NA
## 351 1 200 199 1 NA
## 352 1 201 200 1 NA
## 353 1 202 201 1 NA
## cmTotHitsL.X1 cmTotHitsM.X1 TotLandsX1 TotMissedX1 TotReceivedX1
## 170 NA NA 0 0 0
## 171 NA NA 0 0 0
## 172 NA NA 0 0 0
## 173 NA NA 0 1 0
## 174 NA NA 1 0 0
## 175 NA NA 0 2 0
## 176 NA NA 0 1 0
## 177 NA NA 0 2 0
## 178 NA NA 1 0 0
## 179 NA NA 0 0 0
## 180 NA NA 0 0 0
## 181 NA NA 0 0 0
## 182 NA NA 0 0 0
## 183 NA NA 0 0 0
## 184 NA NA 0 0 0
## 185 NA NA 0 0 0
## 186 NA NA 0 0 0
## 187 NA NA 0 0 0
## 188 NA NA 2 0 0
## 189 NA NA 0 0 0
## 190 NA NA 0 0 0
## 191 NA NA 0 0 0
## 192 NA NA 0 0 0
## 193 NA NA 0 0 0
## 194 NA NA 0 0 0
## 195 NA NA 0 0 0
## 196 NA NA 0 0 0
## 197 NA NA 0 0 0
## 198 NA NA 0 0 0
## 199 NA NA 2 0 0
## 200 NA NA 0 0 0
## 201 NA NA 0 0 0
## 202 NA NA 0 0 0
## 203 NA NA 0 0 0
## 204 NA NA 0 0 0
## 205 NA NA 1 0 0
## 206 NA NA 0 0 0
## 207 NA NA 0 0 0
## 208 NA NA 0 0 0
## 209 NA NA 0 0 0
## 210 NA NA 0 0 0
## 211 NA NA 0 0 0
## 212 NA NA 0 0 0
## 213 NA NA 0 0 0
## 214 NA NA 0 0 0
## 215 NA NA 0 0 0
## 216 NA NA 1 0 0
## 217 NA NA 0 0 0
## 218 NA NA 0 0 0
## 219 NA NA 0 0 0
## 220 NA NA 0 0 0
## 221 NA NA 0 0 0
## 222 NA NA 0 0 0
## 223 NA NA 0 0 0
## 224 NA NA 0 0 0
## 225 NA NA 0 0 0
## 226 NA NA 0 0 0
## 227 NA NA 0 0 0
## 228 NA NA 0 0 0
## 229 NA NA 1 0 0
## 230 NA NA 0 0 0
## 231 NA NA 0 0 0
## 232 NA NA 0 0 0
## 233 NA NA 0 0 0
## 234 NA NA 0 0 0
## 235 NA NA 0 0 0
## 236 NA NA 0 0 0
## 237 NA NA 0 0 0
## 238 NA NA 0 0 0
## 239 NA NA 0 0 0
## 240 NA NA 0 0 0
## 241 NA NA 0 0 0
## 242 NA NA 0 0 0
## 243 NA NA 0 0 0
## 244 NA NA 0 0 0
## 245 NA NA 0 0 0
## 246 NA NA 0 0 0
## 247 NA NA 0 0 0
## 248 NA NA 0 0 0
## 249 NA NA 0 0 0
## 250 NA NA 0 0 0
## 251 NA NA 0 0 0
## 252 NA NA 0 0 0
## 253 NA NA 0 0 0
## 254 NA NA 0 0 0
## 255 NA NA 0 0 0
## 256 NA NA 0 0 0
## 257 NA NA 0 0 0
## 258 NA NA 0 0 0
## 259 NA NA 0 0 0
## 260 NA NA 0 0 0
## 261 NA NA 0 0 0
## 262 NA NA 0 0 0
## 263 NA NA 0 0 0
## 264 NA NA 0 0 0
## 265 NA NA 0 0 0
## 266 NA NA 0 0 0
## 267 NA NA 0 0 0
## 268 NA NA 0 0 0
## 269 NA NA 0 0 0
## 270 NA NA 0 0 0
## 271 NA NA 0 0 0
## 272 NA NA 0 0 0
## 273 NA NA 0 0 0
## 274 NA NA 0 0 0
## 275 NA NA 0 0 0
## 276 NA NA 0 0 0
## 277 NA NA 0 0 0
## 278 NA NA 0 0 0
## 279 NA NA 0 0 0
## 280 NA NA 0 0 0
## 281 NA NA 0 0 0
## 282 NA NA 0 0 0
## 283 NA NA 0 0 0
## 284 NA NA 0 0 0
## 285 NA NA 0 0 0
## 286 NA NA 0 0 0
## 287 NA NA 0 1 0
## 288 NA NA 0 0 0
## 289 NA NA 0 0 0
## 290 NA NA 0 0 0
## 291 NA NA 0 0 0
## 292 NA NA 2 0 0
## 293 NA NA 2 0 0
## 294 NA NA 0 0 0
## 295 NA NA 0 0 0
## 296 NA NA 0 0 0
## 297 NA NA 0 0 0
## 298 NA NA 0 0 0
## 299 NA NA 0 0 0
## 300 NA NA 0 0 0
## 301 NA NA 0 0 0
## 302 NA NA 0 0 0
## 303 NA NA 0 0 0
## 304 NA NA 0 0 0
## 305 NA NA 0 0 0
## 306 NA NA 0 0 0
## 307 NA NA 0 0 0
## 308 NA NA 0 0 0
## 309 NA NA 0 0 0
## 310 NA NA 0 0 0
## 311 NA NA 0 0 0
## 312 NA NA 0 0 0
## 313 NA NA 0 0 0
## 314 NA NA 0 0 0
## 315 NA NA 0 0 0
## 316 NA NA 0 0 0
## 317 NA NA 0 0 0
## 318 NA NA 0 0 0
## 319 NA NA 0 0 0
## 320 NA NA 2 0 0
## 321 NA NA 2 0 0
## 322 NA NA 0 0 0
## 323 NA NA 0 0 0
## 324 NA NA 0 0 0
## 325 NA NA 0 0 0
## 326 NA NA 0 0 0
## 327 NA NA 0 0 0
## 328 NA NA 0 0 0
## 329 NA NA 0 0 0
## 330 NA NA 0 0 0
## 331 NA NA 0 0 0
## 332 NA NA 0 0 0
## 333 NA NA 2 0 0
## 334 NA NA 1 0 0
## 335 NA NA 0 0 0
## 336 NA NA 0 0 0
## 337 NA NA 0 0 0
## 338 NA NA 0 0 0
## 339 NA NA 0 0 0
## 340 NA NA 0 0 0
## 341 NA NA 1 0 0
## 342 NA NA 1 0 0
## 343 NA NA 0 0 0
## 344 NA NA 0 0 0
## 345 NA NA 0 0 0
## 346 NA NA 0 0 0
## 347 NA NA 0 0 0
## 348 NA NA 0 0 0
## 349 NA NA 0 0 0
## 350 NA NA 0 0 0
## 351 NA NA 0 0 0
## 352 NA NA 0 0 0
## 353 NA NA 0 0 0
## cmTotHitsR.X2 cmTotHitsL.X2 cmTotHitsM.X2 TotLandsX2 TotMissedX2
## 170 NA NA NA 0 2
## 171 NA NA NA 0 0
## 172 NA NA NA 0 0
## 173 NA NA NA 0 1
## 174 NA NA NA 0 1
## 175 NA NA NA 0 0
## 176 NA NA NA 0 2
## 177 NA NA NA 0 0
## 178 NA NA NA 0 0
## 179 NA NA NA 0 0
## 180 NA NA NA 0 0
## 181 NA NA NA 0 0
## 182 NA NA NA 0 0
## 183 NA NA NA 0 0
## 184 NA NA NA 0 0
## 185 NA NA NA 0 0
## 186 NA NA NA 0 0
## 187 NA NA NA 0 0
## 188 NA NA NA 0 0
## 189 NA NA NA 0 0
## 190 NA NA NA 0 0
## 191 NA NA NA 0 0
## 192 NA NA NA 0 0
## 193 NA NA NA 0 0
## 194 NA NA NA 0 0
## 195 NA NA NA 0 0
## 196 NA NA NA 0 0
## 197 NA NA NA 0 0
## 198 NA NA NA 0 0
## 199 NA NA NA 0 0
## 200 NA NA NA 0 0
## 201 NA NA NA 0 0
## 202 NA NA NA 0 0
## 203 NA NA NA 0 0
## 204 NA NA NA 0 0
## 205 NA NA NA 0 0
## 206 NA NA NA 0 0
## 207 NA NA NA 0 0
## 208 NA NA NA 0 0
## 209 NA NA NA 0 0
## 210 NA NA NA 0 0
## 211 NA NA NA 0 0
## 212 NA NA NA 0 0
## 213 NA NA NA 0 0
## 214 NA NA NA 0 0
## 215 NA NA NA 0 0
## 216 NA NA NA 0 0
## 217 NA NA NA 0 0
## 218 NA NA NA 0 0
## 219 NA NA NA 0 0
## 220 NA NA NA 0 0
## 221 NA NA NA 0 0
## 222 NA NA NA 0 0
## 223 NA NA NA 0 0
## 224 NA NA NA 0 0
## 225 NA NA NA 0 0
## 226 NA NA NA 0 0
## 227 NA NA NA 0 0
## 228 NA NA NA 0 0
## 229 NA NA NA 0 0
## 230 NA NA NA 0 0
## 231 NA NA NA 0 0
## 232 NA NA NA 0 0
## 233 NA NA NA 0 0
## 234 NA NA NA 0 0
## 235 NA NA NA 0 0
## 236 NA NA NA 0 0
## 237 NA NA NA 0 0
## 238 NA NA NA 0 0
## 239 NA NA NA 0 0
## 240 NA NA NA 0 0
## 241 NA NA NA 0 0
## 242 NA NA NA 0 0
## 243 NA NA NA 0 0
## 244 NA NA NA 0 0
## 245 NA NA NA 0 0
## 246 NA NA NA 0 0
## 247 NA NA NA 0 0
## 248 NA NA NA 0 0
## 249 NA NA NA 0 0
## 250 NA NA NA 0 0
## 251 NA NA NA 0 0
## 252 NA NA NA 0 0
## 253 NA NA NA 0 0
## 254 NA NA NA 0 0
## 255 NA NA NA 0 0
## 256 NA NA NA 0 0
## 257 NA NA NA 0 0
## 258 NA NA NA 0 0
## 259 NA NA NA 0 0
## 260 NA NA NA 0 0
## 261 NA NA NA 0 0
## 262 NA NA NA 0 0
## 263 NA NA NA 0 0
## 264 NA NA NA 0 0
## 265 NA NA NA 0 0
## 266 NA NA NA 0 0
## 267 NA NA NA 0 0
## 268 NA NA NA 0 0
## 269 NA NA NA 0 0
## 270 NA NA NA 0 0
## 271 NA NA NA 0 0
## 272 NA NA NA 0 0
## 273 NA NA NA 0 0
## 274 NA NA NA 0 0
## 275 NA NA NA 0 0
## 276 NA NA NA 0 0
## 277 NA NA NA 0 0
## 278 NA NA NA 0 0
## 279 NA NA NA 0 0
## 280 NA NA NA 0 0
## 281 NA NA NA 0 0
## 282 NA NA NA 0 0
## 283 NA NA NA 0 0
## 284 NA NA NA 0 0
## 285 NA NA NA 0 0
## 286 NA NA NA 0 0
## 287 NA NA NA 0 0
## 288 NA NA NA 0 0
## 289 NA NA NA 0 0
## 290 NA NA NA 0 0
## 291 NA NA NA 0 0
## 292 NA NA NA 0 0
## 293 NA NA NA 0 0
## 294 NA NA NA 0 0
## 295 NA NA NA 0 0
## 296 NA NA NA 0 0
## 297 NA NA NA 0 0
## 298 NA NA NA 0 0
## 299 NA NA NA 0 0
## 300 NA NA NA 0 0
## 301 NA NA NA 0 0
## 302 NA NA NA 0 0
## 303 NA NA NA 0 0
## 304 NA NA NA 0 0
## 305 NA NA NA 0 0
## 306 NA NA NA 0 0
## 307 NA NA NA 0 0
## 308 NA NA NA 0 0
## 309 NA NA NA 0 0
## 310 NA NA NA 0 0
## 311 NA NA NA 0 0
## 312 NA NA NA 0 0
## 313 NA NA NA 0 0
## 314 NA NA NA 0 0
## 315 NA NA NA 0 0
## 316 NA NA NA 0 0
## 317 NA NA NA 0 0
## 318 NA NA NA 0 0
## 319 NA NA NA 0 0
## 320 NA NA NA 0 0
## 321 NA NA NA 0 0
## 322 NA NA NA 0 0
## 323 NA NA NA 0 0
## 324 NA NA NA 0 0
## 325 NA NA NA 0 0
## 326 NA NA NA 0 0
## 327 NA NA NA 0 0
## 328 NA NA NA 0 0
## 329 NA NA NA 0 0
## 330 NA NA NA 0 0
## 331 NA NA NA 0 0
## 332 NA NA NA 0 0
## 333 NA NA NA 0 0
## 334 NA NA NA 0 0
## 335 NA NA NA 0 0
## 336 NA NA NA 0 0
## 337 NA NA NA 0 0
## 338 NA NA NA 0 0
## 339 NA NA NA 0 0
## 340 NA NA NA 0 0
## 341 NA NA NA 0 0
## 342 NA NA NA 0 0
## 343 NA NA NA 0 0
## 344 NA NA NA 0 0
## 345 NA NA NA 0 0
## 346 NA NA NA 0 0
## 347 NA NA NA 0 0
## 348 NA NA NA 0 0
## 349 NA NA NA 0 0
## 350 NA NA NA 0 0
## 351 NA NA NA 0 0
## 352 NA NA NA 0 0
## 353 NA NA NA 0 0
## TotReceivedX2 Time
## 170 0 4:57
## 171 0 4:53
## 172 0 4:52
## 173 0 4:51
## 174 1 4:48
## 175 0 4:42
## 176 0 4:38
## 177 0 4:35
## 178 2 4:34
## 179 0 4:33
## 180 0 4:32
## 181 0 4:31
## 182 0 4:30
## 183 0 4:29
## 184 0 4:28
## 185 0 4:27
## 186 0 4:26
## 187 0 4:25
## 188 3 4:24
## 189 0 4:23
## 190 0 4:22
## 191 0 4:21
## 192 0 4:20
## 193 0 4:19
## 194 0 4:18
## 195 0 4:17
## 196 0 4:16
## 197 0 4:15
## 198 0 4:14
## 199 3 4:13
## 200 0 4:12
## 201 0 4:11
## 202 0 4:10
## 203 0 4:09
## 204 0 4:08
## 205 2 4:07
## 206 0 4:06
## 207 0 4:05
## 208 0 4:04
## 209 0 4:03
## 210 0 4:02
## 211 0 4:01
## 212 0 4:00
## 213 0 3:59
## 214 0 3:58
## 215 0 3:57
## 216 2 3:56
## 217 0 3:55
## 218 0 3:54
## 219 0 3:53
## 220 0 3:52
## 221 0 3:51
## 222 0 3:50
## 223 0 3:49
## 224 0 3:48
## 225 0 3:47
## 226 0 3:46
## 227 0 3:45
## 228 0 3:44
## 229 1 3:43
## 230 0 3:42
## 231 0 3:40
## 232 0 3:38
## 233 0 3:37
## 234 0 3:36
## 235 0 3:35
## 236 0 3:34
## 237 0 3:33
## 238 0 3:32
## 239 0 3:31
## 240 0 3:30
## 241 0 3:29
## 242 0 3:28
## 243 0 3:27
## 244 0 3:26
## 245 0 3:25
## 246 0 3:24
## 247 0 3:23
## 248 0 3:22
## 249 0 3:21
## 250 0 3:20
## 251 0 3:19
## 252 0 3:18
## 253 0 3:17
## 254 0 3:16
## 255 0 3:15
## 256 0 3:14
## 257 0 3:13
## 258 0 3:12
## 259 0 3:11
## 260 0 3:10
## 261 0 3:09
## 262 0 3:08
## 263 0 3:07
## 264 0 3:06
## 265 0 3:05
## 266 0 3:04
## 267 0 3:03
## 268 0 3:02
## 269 0 3:01
## 270 0 3:00
## 271 0 2:59
## 272 0 2:58
## 273 0 2:57
## 274 0 2:56
## 275 0 2:55
## 276 0 2:54
## 277 0 2:53
## 278 0 2:52
## 279 0 2:51
## 280 0 2:50
## 281 0 2:49
## 282 0 2:48
## 283 0 2:47
## 284 0 2:46
## 285 0 2:45
## 286 0 2:44
## 287 0 2:43
## 288 0 2:42
## 289 0 2:41
## 290 0 2:40
## 291 0 2:39
## 292 2 2:38
## 293 2 2:37
## 294 0 2:36
## 295 0 2:35
## 296 0 2:34
## 297 0 2:33
## 298 0 2:32
## 299 0 2:31
## 300 0 2:30
## 301 0 2:29
## 302 0 2:28
## 303 0 2:27
## 304 0 2:26
## 305 0 2:25
## 306 0 2:24
## 307 0 2:23
## 308 0 2:22
## 309 0 2:21
## 310 0 2:20
## 311 0 2:19
## 312 0 2:18
## 313 0 2:17
## 314 0 2:16
## 315 0 2:15
## 316 0 2:14
## 317 0 2:13
## 318 0 2:12
## 319 0 2:11
## 320 2 2:10
## 321 2 2:09
## 322 0 2:08
## 323 0 2:07
## 324 0 2:06
## 325 0 2:05
## 326 0 2:04
## 327 0 2:03
## 328 0 2:02
## 329 0 2:01
## 330 0 2:00
## 331 0 1:59
## 332 0 1:58
## 333 2 1:57
## 334 1 1:56
## 335 0 1:55
## 336 0 1:54
## 337 0 1:53
## 338 0 1:52
## 339 0 1:51
## 340 0 1:50
## 341 1 1:49
## 342 1 1:48
## 343 0 1:47
## 344 0 1:46
## 345 0 1:45
## 346 0 1:44
## 347 0 1:43
## 348 0 1:42
## 349 0 1:41
## 350 0 1:40
## 351 0 1:39
## 352 0 1:38
## 353 0 1:37
## FighterActionReactions.X1
## 170 <NA>
## 171 testing L push kick
## 172 testing L mt kick to low leg
## 173 misses L push kick
## 174 lands R cross
## 175 misses L mt kick to face, misses R cross
## 176 misses R push kick
## 177 misses upper body takedown and holding upper body hold starts
## 178 lands L knee to body holding upper body hold continues
## 179 holding upper body hold continues
## 180 holding upper body hold continues
## 181 holding upper body hold continues
## 182 holding upper body hold continues
## 183 holding upper body hold continues and caught in R arm hold
## 184 holding upper body hold continues and caught in R arm hold
## 185 holding upper body hold continues and caught in R arm hold
## 186 holding upper body hold continues and caught in R arm hold
## 187 pushes against cage holding upper body hold continues and caught in R arm hold
## 188 lands R knee to L leg holding upper body hold continues and caught in R arm hold, lands L knee to inner leg
## 189 holding upper body hold continues and caught in R arm hold
## 190 holding upper body hold continues and caught in R arm hold
## 191 holding upper body hold continues and caught in R arm hold
## 192 holding upper body hold continues and caught in R arm hold
## 193 holding upper body hold continues and caught in R arm hold
## 194 holding upper body hold continues and caught in R arm hold
## 195 holding upper body hold continues and caught in R arm hold
## 196 holding upper body hold continues and caught in R arm hold
## 197 holding upper body hold continues and caught in R arm hold
## 198 holding upper body hold continues and caught in R arm hold
## 199 lands R knee to inner leg holding upper body hold continues and caught in R arm hold, lands L knee to inner leg
## 200 holding upper body hold continues and caught in R arm hold
## 201 holding upper body hold continues and caught in R arm hold
## 202 holding upper body hold continues and caught in R arm hold
## 203 holding upper body hold continues and caught in R arm hold
## 204 holding upper body hold continues and caught in R arm hold
## 205 lands R knee to inner leg and holding upper body hold continues and caught in R arm hold
## 206 holding upper body hold continues and caught in R arm hold
## 207 holding upper body hold continues and caught in R arm hold
## 208 holding upper body hold continues and caught in R arm hold
## 209 holding upper body hold continues and caught in R arm hold
## 210 holding upper body hold continues and caught in R arm hold
## 211 holding upper body hold continues and caught in R arm hold
## 212 holding upper body hold continues and caught in R arm hold
## 213 holding upper body hold continues and caught in R arm hold
## 214 holding upper body hold continues and caught in R arm hold
## 215 holding upper body hold continues and caught in R arm hold
## 216 lands R knee to outer leg and holding upper body hold continues and caught in R arm hold
## 217 holding upper body hold continues and caught in R arm hold
## 218 holding upper body hold continues and caught in R arm hold
## 219 holding upper body hold continues and caught in R arm hold
## 220 holding upper body hold continues and caught in R arm hold
## 221 holding upper body hold continues and caught in R arm hold
## 222 holding upper body hold continues and caught in R arm hold
## 223 holding upper body hold continues and caught in R arm hold
## 224 holding upper body hold continues and caught in R arm hold
## 225 holding upper body hold continues and caught in R arm hold
## 226 holding upper body hold continues and caught in R arm hold
## 227 holding upper body hold continues and caught in R arm hold
## 228 breaks R arm hold and loses upper body hold
## 229 lands judo type takedown
## 230 <NA>
## 231 moves out attempting a ground takedown flip
## 232 holding under shoulders and upper body hold starts on ground
## 233 holding upper body continues
## 234 holding upper body continues
## 235 jumps on back losing upper body hold and holding back mount hold starts
## 236 sinks feet in thighs while holding back mount hold continues
## 237 holding back mount hold continues
## 238 holding back mount hold continues
## 239 holding back mount hold continues
## 240 holding back mount hold continues
## 241 holding back mount hold continues
## 242 holding back mount hold continues
## 243 holding back mount hold continues and holding L underarm choke hold starts
## 244 holding back mount hold continues
## 245 flips forward taking the back, lands on side holding back mount hold continues and holding L underarm choke hold continues
## 246 grabs under arms holding back mount hold continues and holding L underarm choke hold continues
## 247 holding back mount hold continues and holding L underarm choke hold continues
## 248 holding back mount hold continues and holding L underarm choke hold continues
## 249 holding back mount hold continues and holding L underarm choke hold continues
## 250 holding back mount hold continues and holding L underarm choke hold continues
## 251 holding back mount hold continues and holding L underarm choke hold continues
## 252 holding back mount hold continues and holding L underarm choke hold continues
## 253 holding back mount hold continues and holding L underarm choke hold continues
## 254 holding back mount hold continues and loses L underarm choke hold
## 255 holding back mount hold
## 256 holding back mount hold and holding R underarm choke hold starts
## 257 holding back mount hold and holding R underarm choke hold starts
## 258 holding back mount hold and holding R underarm choke hold starts
## 259 holding back mount hold and holding R underarm choke hold starts
## 260 holding back mount hold and holding R underarm choke hold starts
## 261 holding back mount hold and holding R underarm choke hold starts
## 262 holding back mount hold and holding R underarm choke hold starts
## 263 holding back mount hold and holding R underarm choke hold starts
## 264 holding back mount hold and holding R underarm choke hold starts
## 265 holding back mount hold and holding R underarm choke hold starts
## 266 holding back mount hold and holding R underarm choke hold starts
## 267 holding back mount hold and holding R underarm choke hold starts
## 268 holding back mount hold and holding R underarm choke hold starts
## 269 holding back mount hold and holding R underarm choke hold starts
## 270 holding back mount hold and holding R underarm choke hold starts
## 271 holding back mount hold and holding R underarm choke hold starts
## 272 holding back mount hold and holding R underarm choke hold starts
## 273 holding back mount hold and holding R underarm choke hold starts
## 274 holding back mount hold and holding R underarm choke hold starts
## 275 holding back mount hold and holding R underarm choke hold starts
## 276 holding back mount hold and holding R underarm choke hold starts
## 277 holding back mount hold and holding R underarm choke hold starts
## 278 holding back mount hold and holding R underarm choke hold starts
## 279 holding back mount hold and holding R underarm choke hold starts
## 280 holding back mount hold and holding R underarm choke hold starts
## 281 holding back mount hold and holding R underarm choke hold starts
## 282 holding back mount hold and holding R underarm choke hold starts
## 283 holding back mount continues on top from on back
## 284 holding back mount
## 285 loses back mount hold and holding side mount hold starts
## 286 holding side mount hold continues
## 287 misses elbow holding side mount hold continues
## 288 holding side mount hold continues
## 289 loses side mount hold and holding back mount hold starts
## 290 holding back mount hold continues
## 291 holding back mount hold continues
## 292 lands R hammer hit holding back mount hold continues, lands R hammer hit
## 293 lands R hammer hit holding back mount hold continues, lands R hammer hit
## 294 holding back mount hold
## 295 holding back mount hold
## 296 holding back mount hold
## 297 loses back mount hold and holding back hold starts
## 298 holding back hold continues
## 299 holding back hold continues
## 300 holding back hold continues
## 301 holding back hold continues
## 302 holding back hold continues
## 303 holding back hold continues
## 304 holding back hold continues
## 305 holding back hold continues
## 306 holding back hold continues
## 307 holding back hold continues
## 308 holding back hold continues
## 309 holding back hold continues
## 310 holding back hold continues
## 311 holding back hold continues
## 312 holding back hold continues
## 313 holding back hold continues
## 314 holding back hold continues
## 315 holding back hold continues
## 316 holding back hold continues
## 317 holding back hold continues
## 318 holding back hold continues
## 319 holding back hold continues
## 320 lands R hook holding back hold continues, lands R hook
## 321 lands R hook holding back hold continues, lands R hook
## 322 loses back hold and holding back mount hold starts
## 323 holding back mount hold continues
## 324 holding back mount hold continues
## 325 holding back mount hold continues
## 326 holding back mount hold continues
## 327 holding back mount hold continues
## 328 holding back mount hold continues
## 329 holding back mount hold continues
## 330 holding back mount hold continues
## 331 holding back mount hold continues
## 332 holding back mount hold continues
## 333 lands R hook holding back mount hold continues, lands R hook
## 334 lands R hook holding back mount hold continues
## 335 holding back mount hold continues
## 336 holding back mount hold continues
## 337 holding back mount hold continues
## 338 holding back mount hold continues
## 339 holding back mount hold continues
## 340 holding back mount hold continues and holding neck hold starts
## 341 lands R hook holding back mount hold continues and holding neck hold continues
## 342 lands R hook holding back mount hold continues and holding neck hold continues
## 343 holding back mount hold continues and holding neck hold continues
## 344 holding back mount hold continues and holding neck hold continues
## 345 holding back mount hold continues and holding neck hold continues
## 346 holding back mount hold continues and holding neck hold continues
## 347 holding neck choke hold starts while holding back mount hold continues and loses neck hold
## 348 holding neck choke hold starts while holding back mount hold continues
## 349 holding neck choke hold starts while holding back mount hold continues
## 350 holding neck choke hold starts while holding back mount hold continues
## 351 holding neck choke hold starts while holding back mount hold continues
## 352 holding neck choke hold starts while holding back mount hold continues
## 353 wins by submission
## FightersActionsReactions.X2
## 170 misses L jab, misses L jab
## 171 <NA>
## 172 <NA>
## 173 misses single leg takedown with L leg catch pulling up to throw off balance
## 174 misses L knee to body
## 175 <NA>
## 176 misses R cross, misses L cross
## 177 caught in upper body hold
## 178 caught in upper body hold
## 179 caught in upper body hold
## 180 caught in upper body hold
## 181 caught in upper body hold
## 182 caught in upper body hold
## 183 caught in upper body hold and holding R arm
## 184 caught in upper body hold and holding R arm
## 185 caught in upper body hold and holding R arm
## 186 caught in upper body hold and holding R arm
## 187 caught in upper body hold and holding R arm
## 188 caught in upper body hold and holding R arm
## 189 caught in upper body hold and holding R arm
## 190 caught in upper body hold and holding R arm
## 191 caught in upper body hold and holding R arm
## 192 caught in upper body hold and holding R arm
## 193 caught in upper body hold and holding R arm
## 194 caught in upper body hold and holding R arm
## 195 caught in upper body hold and holding R arm
## 196 caught in upper body hold and holding R arm
## 197 caught in upper body hold and holding R arm
## 198 caught in upper body hold and holding R arm
## 199 caught in upper body hold and holding R arm
## 200 caught in upper body hold and holding R arm
## 201 caught in upper body hold and holding R arm
## 202 caught in upper body hold and holding R arm
## 203 caught in upper body hold and holding R arm
## 204 caught in upper body hold and holding R arm
## 205 caught in upper body hold and holding R arm
## 206 caught in upper body hold and holding R arm
## 207 caught in upper body hold and holding R arm
## 208 caught in upper body hold and holding R arm
## 209 caught in upper body hold and holding R arm
## 210 caught in upper body hold and holding R arm
## 211 caught in upper body hold and holding R arm
## 212 caught in upper body hold and holding R arm
## 213 caught in upper body hold and holding R arm
## 214 caught in upper body hold and holding R arm
## 215 caught in upper body hold and holding R arm
## 216 caught in upper body hold and holding R arm
## 217 caught in upper body hold and holding R arm
## 218 caught in upper body hold and holding R arm
## 219 caught in upper body hold and holding R arm
## 220 caught in upper body hold and holding R arm
## 221 caught in upper body hold and holding R arm
## 222 caught in upper body hold and holding R arm
## 223 caught in upper body hold and holding R arm
## 224 caught in upper body hold and holding R arm
## 225 caught in upper body hold and holding R arm
## 226 caught in upper body hold and holding R arm
## 227 caught in upper body hold and holding R arm
## 228 breaks out of upper body hold and loses R arm hold
## 229 <NA>
## 230 on top shoulders
## 231 <NA>
## 232 caught in upper body hold
## 233 caught in upper body hold
## 234 caught in upper body hold
## 235 breaks upper body hold and caught in back mount hold
## 236 caught in back mount hold
## 237 caught in back mount hold
## 238 caught in back mount hold
## 239 caught in back mount hold
## 240 caught in back mount hold
## 241 caught in back mount hold
## 242 caught in back mount hold
## 243 caught in back mount hold and caught in L underarm choke hold
## 244 caught in back mount hold and caught in L underarm choke hold
## 245 protects L underarm from choke and caught in back mount hold and caught in L underarm choke hold
## 246 caught in back mount hold and caught in L underarm choke hold
## 247 caught in back mount hold and caught in L underarm choke hold
## 248 caught in back mount hold and caught in L underarm choke hold
## 249 caught in back mount hold and caught in L underarm choke hold
## 250 caught in back mount hold and caught in L underarm choke hold
## 251 caught in back mount hold and caught in L underarm choke hold
## 252 caught in back mount hold and caught in L underarm choke hold
## 253 caught in back mount hold and caught in L underarm choke hold
## 254 caught in back mount hold and breaks L underarm choke hold
## 255 caught in back mount hold
## 256 caught in back mount hold and caught in R underarm choke hold
## 257 caught in back mount hold and caught in R underarm choke hold
## 258 caught in back mount hold and caught in R underarm choke hold
## 259 caught in back mount hold and caught in R underarm choke hold
## 260 caught in back mount hold and caught in R underarm choke hold
## 261 caught in back mount hold and caught in R underarm choke hold
## 262 caught in back mount hold and caught in R underarm choke hold
## 263 caught in back mount hold and caught in R underarm choke hold
## 264 caught in back mount hold and caught in R underarm choke hold
## 265 caught in back mount hold and caught in R underarm choke hold
## 266 caught in back mount hold and caught in R underarm choke hold
## 267 caught in back mount hold and caught in R underarm choke hold
## 268 caught in back mount hold and caught in R underarm choke hold
## 269 caught in back mount hold and caught in R underarm choke hold
## 270 caught in back mount hold and caught in R underarm choke hold
## 271 caught in back mount hold and caught in R underarm choke hold
## 272 caught in back mount hold and caught in R underarm choke hold
## 273 caught in back mount hold and caught in R underarm choke hold
## 274 caught in back mount hold and caught in R underarm choke hold
## 275 caught in back mount hold and caught in R underarm choke hold
## 276 caught in back mount hold and caught in R underarm choke hold
## 277 caught in back mount hold and caught in R underarm choke hold
## 278 caught in back mount hold and caught in R underarm choke hold
## 279 caught in back mount hold and caught in R underarm choke hold
## 280 caught in back mount hold and caught in R underarm choke hold
## 281 caught in back mount hold and caught in R underarm choke hold
## 282 caught in back mount hold and caught in R underarm choke hold
## 283 caught in back mount hold
## 284 caught in back mount hold
## 285 breaks back mount hold and caught in side mount hold
## 286 caught in side mount hold
## 287 caught in side mount hold
## 288 caught in side mount hold
## 289 breaks side mount hold and caught in back mount hold
## 290 caught in back mount hold
## 291 caught in back mount hold
## 292 caught in back mount hold
## 293 caught in back mount hold
## 294 caught in back mount hold
## 295 caught in back mount hold
## 296 caught in back mount hold
## 297 breaks back mount hold and caught in back hold getting up on knees pressing opponent's L thigh against cage
## 298 caught in back hold
## 299 caught in back hold
## 300 caught in back hold
## 301 caught in back hold
## 302 caught in back hold
## 303 caught in back hold
## 304 caught in back hold
## 305 caught in back hold
## 306 caught in back hold
## 307 caught in back hold
## 308 caught in back hold
## 309 caught in back hold
## 310 caught in back hold
## 311 caught in back hold
## 312 caught in back hold
## 313 caught in back hold
## 314 caught in back hold
## 315 caught in back hold
## 316 caught in back hold
## 317 caught in back hold
## 318 caught in back hold
## 319 caught in back hold
## 320 caught in back hold
## 321 caught in back hold
## 322 breaks back hold and caught inback mount hold
## 323 breaks back hold and caught inback mount hold
## 324 breaks back hold and caught inback mount hold
## 325 breaks back hold and caught inback mount hold
## 326 caught in back mount hold and slams opponent forward and down over her head while crouched with opponent holding her back
## 327 caught in back mount hold
## 328 caught in back mount hold
## 329 caught in back mount hold
## 330 caught in back mount hold
## 331 caught in back mount hold
## 332 caught in back mount hold
## 333 caught in back mount hold
## 334 caught in back mount hold
## 335 caught in back mount hold
## 336 caught in back mount hold
## 337 caught in back mount hold
## 338 caught in back mount hold
## 339 caught in back mount hold
## 340 caught in back mount hold and caught in neck hold
## 341 caught in back mount hold and caught in neck hold
## 342 caught in back mount hold and caught in neck hold
## 343 caught in back mount hold and caught in neck hold
## 344 caught in back mount hold and caught in neck hold
## 345 caught in back mount hold and caught in neck hold
## 346 caught in back mount hold and caught in neck hold
## 347 caught in back mount hold and caught in neck choke hold and breaks neck hold
## 348 caught in back mount hold and caught in neck choke hold
## 349 caught in back mount hold and caught in neck choke hold
## 350 caught in back mount hold and caught in neck choke hold
## 351 caught in back mount hold and caught in neck choke hold
## 352 caught in back mount hold and caught in neck choke hold
## 353 taps out
## Notes Crossl.X1 Kneel.X1 Elbowl.X1 Hookl.X1 Jabl.X1 Kickl.X1 Crossl.X2
## 170 Anderson 0 0 0 0 0 0 0
## 171 Anderson 0 0 0 0 0 0 0
## 172 Anderson 0 0 0 0 0 0 0
## 173 Anderson 0 0 0 0 0 0 0
## 174 Anderson 1 0 0 0 0 0 0
## 175 Anderson 0 0 0 0 0 0 0
## 176 Anderson 0 0 0 0 0 0 0
## 177 Anderson 0 0 0 0 0 0 0
## 178 Anderson 0 1 0 0 0 0 0
## 179 Anderson 0 0 0 0 0 0 0
## 180 Anderson 0 0 0 0 0 0 0
## 181 Anderson 0 0 0 0 0 0 0
## 182 Anderson 0 0 0 0 0 0 0
## 183 Anderson 0 0 0 0 0 0 0
## 184 Anderson 0 0 0 0 0 0 0
## 185 Anderson 0 0 0 0 0 0 0
## 186 Anderson 0 0 0 0 0 0 0
## 187 Anderson 0 0 0 0 0 0 0
## 188 Anderson 0 1 0 0 0 0 0
## 189 Anderson 0 0 0 0 0 0 0
## 190 Anderson 0 0 0 0 0 0 0
## 191 Anderson 0 0 0 0 0 0 0
## 192 Anderson 0 0 0 0 0 0 0
## 193 Anderson 0 0 0 0 0 0 0
## 194 Anderson 0 0 0 0 0 0 0
## 195 Anderson 0 0 0 0 0 0 0
## 196 Anderson 0 0 0 0 0 0 0
## 197 Anderson 0 0 0 0 0 0 0
## 198 Anderson 0 0 0 0 0 0 0
## 199 Anderson 0 1 0 0 0 0 0
## 200 Anderson 0 0 0 0 0 0 0
## 201 Anderson 0 0 0 0 0 0 0
## 202 Anderson 0 0 0 0 0 0 0
## 203 Anderson 0 0 0 0 0 0 0
## 204 Anderson 0 0 0 0 0 0 0
## 205 Anderson 0 1 0 0 0 0 0
## 206 Anderson 0 0 0 0 0 0 0
## 207 Anderson 0 0 0 0 0 0 0
## 208 Anderson 0 0 0 0 0 0 0
## 209 Anderson 0 0 0 0 0 0 0
## 210 Anderson 0 0 0 0 0 0 0
## 211 Anderson 0 0 0 0 0 0 0
## 212 Anderson 0 0 0 0 0 0 0
## 213 Anderson 0 0 0 0 0 0 0
## 214 Anderson 0 0 0 0 0 0 0
## 215 Anderson 0 0 0 0 0 0 0
## 216 Anderson 0 1 0 0 0 0 0
## 217 Anderson 0 0 0 0 0 0 0
## 218 Anderson 0 0 0 0 0 0 0
## 219 Anderson 0 0 0 0 0 0 0
## 220 Anderson 0 0 0 0 0 0 0
## 221 Anderson 0 0 0 0 0 0 0
## 222 Anderson 0 0 0 0 0 0 0
## 223 Anderson 0 0 0 0 0 0 0
## 224 Anderson 0 0 0 0 0 0 0
## 225 Anderson 0 0 0 0 0 0 0
## 226 Anderson 0 0 0 0 0 0 0
## 227 Anderson 0 0 0 0 0 0 0
## 228 Anderson 0 0 0 0 0 0 0
## 229 Anderson 0 0 0 0 0 0 0
## 230 Anderson 0 0 0 0 0 0 0
## 231 Anderson 0 0 0 0 0 0 0
## 232 Anderson 0 0 0 0 0 0 0
## 233 Anderson 0 0 0 0 0 0 0
## 234 Anderson 0 0 0 0 0 0 0
## 235 Anderson 0 0 0 0 0 0 0
## 236 Anderson 0 0 0 0 0 0 0
## 237 Anderson 0 0 0 0 0 0 0
## 238 Anderson 0 0 0 0 0 0 0
## 239 Anderson 0 0 0 0 0 0 0
## 240 Anderson 0 0 0 0 0 0 0
## 241 Anderson 0 0 0 0 0 0 0
## 242 Anderson 0 0 0 0 0 0 0
## 243 Anderson 0 0 0 0 0 0 0
## 244 Anderson 0 0 0 0 0 0 0
## 245 Anderson 0 0 0 0 0 0 0
## 246 Anderson 0 0 0 0 0 0 0
## 247 Anderson 0 0 0 0 0 0 0
## 248 Anderson 0 0 0 0 0 0 0
## 249 Anderson 0 0 0 0 0 0 0
## 250 Anderson 0 0 0 0 0 0 0
## 251 Anderson 0 0 0 0 0 0 0
## 252 Anderson 0 0 0 0 0 0 0
## 253 Anderson 0 0 0 0 0 0 0
## 254 Anderson 0 0 0 0 0 0 0
## 255 Anderson 0 0 0 0 0 0 0
## 256 Anderson 0 0 0 0 0 0 0
## 257 Anderson 0 0 0 0 0 0 0
## 258 Anderson 0 0 0 0 0 0 0
## 259 Anderson 0 0 0 0 0 0 0
## 260 Anderson 0 0 0 0 0 0 0
## 261 Anderson 0 0 0 0 0 0 0
## 262 Anderson 0 0 0 0 0 0 0
## 263 Anderson 0 0 0 0 0 0 0
## 264 Anderson 0 0 0 0 0 0 0
## 265 Anderson 0 0 0 0 0 0 0
## 266 Anderson 0 0 0 0 0 0 0
## 267 Anderson 0 0 0 0 0 0 0
## 268 Anderson 0 0 0 0 0 0 0
## 269 Anderson 0 0 0 0 0 0 0
## 270 Anderson 0 0 0 0 0 0 0
## 271 Anderson 0 0 0 0 0 0 0
## 272 Anderson 0 0 0 0 0 0 0
## 273 Anderson 0 0 0 0 0 0 0
## 274 Anderson 0 0 0 0 0 0 0
## 275 Anderson 0 0 0 0 0 0 0
## 276 Anderson 0 0 0 0 0 0 0
## 277 Anderson 0 0 0 0 0 0 0
## 278 Anderson 0 0 0 0 0 0 0
## 279 Anderson 0 0 0 0 0 0 0
## 280 Anderson 0 0 0 0 0 0 0
## 281 Anderson 0 0 0 0 0 0 0
## 282 Anderson 0 0 0 0 0 0 0
## 283 Anderson 0 0 0 0 0 0 0
## 284 Anderson 0 0 0 0 0 0 0
## 285 Anderson 0 0 0 0 0 0 0
## 286 Anderson 0 0 0 0 0 0 0
## 287 Anderson 0 0 0 0 0 0 0
## 288 Anderson 0 0 0 0 0 0 0
## 289 Anderson 0 0 0 0 0 0 0
## 290 Anderson 0 0 0 0 0 0 0
## 291 Anderson 0 0 0 0 0 0 0
## 292 Anderson 0 0 0 0 0 0 0
## 293 Anderson 0 0 0 0 0 0 0
## 294 Anderson 0 0 0 0 0 0 0
## 295 Anderson 0 0 0 0 0 0 0
## 296 Anderson 0 0 0 0 0 0 0
## 297 Anderson 0 0 0 0 0 0 0
## 298 Anderson 0 0 0 0 0 0 0
## 299 Anderson 0 0 0 0 0 0 0
## 300 Anderson 0 0 0 0 0 0 0
## 301 Anderson 0 0 0 0 0 0 0
## 302 Anderson 0 0 0 0 0 0 0
## 303 Anderson 0 0 0 0 0 0 0
## 304 Anderson 0 0 0 0 0 0 0
## 305 Anderson 0 0 0 0 0 0 0
## 306 Anderson 0 0 0 0 0 0 0
## 307 Anderson 0 0 0 0 0 0 0
## 308 Anderson 0 0 0 0 0 0 0
## 309 Anderson 0 0 0 0 0 0 0
## 310 Anderson 0 0 0 0 0 0 0
## 311 Anderson 0 0 0 0 0 0 0
## 312 Anderson 0 0 0 0 0 0 0
## 313 Anderson 0 0 0 0 0 0 0
## 314 Anderson 0 0 0 0 0 0 0
## 315 Anderson 0 0 0 0 0 0 0
## 316 Anderson 0 0 0 0 0 0 0
## 317 Anderson 0 0 0 0 0 0 0
## 318 Anderson 0 0 0 0 0 0 0
## 319 Anderson 0 0 0 0 0 0 0
## 320 Anderson 0 0 0 1 0 0 0
## 321 Anderson 0 0 0 1 0 0 0
## 322 Anderson 0 0 0 0 0 0 0
## 323 Anderson 0 0 0 0 0 0 0
## 324 Anderson 0 0 0 0 0 0 0
## 325 Anderson 0 0 0 0 0 0 0
## 326 Anderson 0 0 0 0 0 0 0
## 327 Anderson 0 0 0 0 0 0 0
## 328 Anderson 0 0 0 0 0 0 0
## 329 Anderson 0 0 0 0 0 0 0
## 330 Anderson 0 0 0 0 0 0 0
## 331 Anderson 0 0 0 0 0 0 0
## 332 Anderson 0 0 0 0 0 0 0
## 333 Anderson 0 0 0 1 0 0 0
## 334 Anderson 0 0 0 1 0 0 0
## 335 Anderson 0 0 0 0 0 0 0
## 336 Anderson 0 0 0 0 0 0 0
## 337 Anderson 0 0 0 0 0 0 0
## 338 Anderson 0 0 0 0 0 0 0
## 339 Anderson 0 0 0 0 0 0 0
## 340 Anderson 0 0 0 0 0 0 0
## 341 Anderson 0 0 0 1 0 0 0
## 342 Anderson 0 0 0 1 0 0 0
## 343 Anderson 0 0 0 0 0 0 0
## 344 Anderson 0 0 0 0 0 0 0
## 345 Anderson 0 0 0 0 0 0 0
## 346 Anderson 0 0 0 0 0 0 0
## 347 Anderson 0 0 0 0 0 0 0
## 348 Anderson 0 0 0 0 0 0 0
## 349 Anderson 0 0 0 0 0 0 0
## 350 Anderson 0 0 0 0 0 0 0
## 351 Anderson 0 0 0 0 0 0 0
## 352 Anderson 0 0 0 0 0 0 0
## 353 Anderson 0 0 0 0 0 0 0
## Kneel.X2 Elbowl.X2 Hookl.X2 Jabl.X2 Kickl.X2 upperl.X1 upperl.X2
## 170 0 0 0 0 0 0 0
## 171 0 0 0 0 0 0 0
## 172 0 0 0 0 0 0 0
## 173 0 0 0 0 0 0 0
## 174 0 0 0 0 0 0 0
## 175 0 0 0 0 0 0 0
## 176 0 0 0 0 0 0 0
## 177 0 0 0 0 0 0 0
## 178 0 0 0 0 0 0 0
## 179 0 0 0 0 0 0 0
## 180 0 0 0 0 0 0 0
## 181 0 0 0 0 0 0 0
## 182 0 0 0 0 0 0 0
## 183 0 0 0 0 0 0 0
## 184 0 0 0 0 0 0 0
## 185 0 0 0 0 0 0 0
## 186 0 0 0 0 0 0 0
## 187 0 0 0 0 0 0 0
## 188 0 0 0 0 0 0 0
## 189 0 0 0 0 0 0 0
## 190 0 0 0 0 0 0 0
## 191 0 0 0 0 0 0 0
## 192 0 0 0 0 0 0 0
## 193 0 0 0 0 0 0 0
## 194 0 0 0 0 0 0 0
## 195 0 0 0 0 0 0 0
## 196 0 0 0 0 0 0 0
## 197 0 0 0 0 0 0 0
## 198 0 0 0 0 0 0 0
## 199 0 0 0 0 0 0 0
## 200 0 0 0 0 0 0 0
## 201 0 0 0 0 0 0 0
## 202 0 0 0 0 0 0 0
## 203 0 0 0 0 0 0 0
## 204 0 0 0 0 0 0 0
## 205 0 0 0 0 0 0 0
## 206 0 0 0 0 0 0 0
## 207 0 0 0 0 0 0 0
## 208 0 0 0 0 0 0 0
## 209 0 0 0 0 0 0 0
## 210 0 0 0 0 0 0 0
## 211 0 0 0 0 0 0 0
## 212 0 0 0 0 0 0 0
## 213 0 0 0 0 0 0 0
## 214 0 0 0 0 0 0 0
## 215 0 0 0 0 0 0 0
## 216 0 0 0 0 0 0 0
## 217 0 0 0 0 0 0 0
## 218 0 0 0 0 0 0 0
## 219 0 0 0 0 0 0 0
## 220 0 0 0 0 0 0 0
## 221 0 0 0 0 0 0 0
## 222 0 0 0 0 0 0 0
## 223 0 0 0 0 0 0 0
## 224 0 0 0 0 0 0 0
## 225 0 0 0 0 0 0 0
## 226 0 0 0 0 0 0 0
## 227 0 0 0 0 0 0 0
## 228 0 0 0 0 0 0 0
## 229 0 0 0 0 0 0 0
## 230 0 0 0 0 0 0 0
## 231 0 0 0 0 0 0 0
## 232 0 0 0 0 0 0 0
## 233 0 0 0 0 0 0 0
## 234 0 0 0 0 0 0 0
## 235 0 0 0 0 0 0 0
## 236 0 0 0 0 0 0 0
## 237 0 0 0 0 0 0 0
## 238 0 0 0 0 0 0 0
## 239 0 0 0 0 0 0 0
## 240 0 0 0 0 0 0 0
## 241 0 0 0 0 0 0 0
## 242 0 0 0 0 0 0 0
## 243 0 0 0 0 0 0 0
## 244 0 0 0 0 0 0 0
## 245 0 0 0 0 0 0 0
## 246 0 0 0 0 0 0 0
## 247 0 0 0 0 0 0 0
## 248 0 0 0 0 0 0 0
## 249 0 0 0 0 0 0 0
## 250 0 0 0 0 0 0 0
## 251 0 0 0 0 0 0 0
## 252 0 0 0 0 0 0 0
## 253 0 0 0 0 0 0 0
## 254 0 0 0 0 0 0 0
## 255 0 0 0 0 0 0 0
## 256 0 0 0 0 0 0 0
## 257 0 0 0 0 0 0 0
## 258 0 0 0 0 0 0 0
## 259 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0
## 265 0 0 0 0 0 0 0
## 266 0 0 0 0 0 0 0
## 267 0 0 0 0 0 0 0
## 268 0 0 0 0 0 0 0
## 269 0 0 0 0 0 0 0
## 270 0 0 0 0 0 0 0
## 271 0 0 0 0 0 0 0
## 272 0 0 0 0 0 0 0
## 273 0 0 0 0 0 0 0
## 274 0 0 0 0 0 0 0
## 275 0 0 0 0 0 0 0
## 276 0 0 0 0 0 0 0
## 277 0 0 0 0 0 0 0
## 278 0 0 0 0 0 0 0
## 279 0 0 0 0 0 0 0
## 280 0 0 0 0 0 0 0
## 281 0 0 0 0 0 0 0
## 282 0 0 0 0 0 0 0
## 283 0 0 0 0 0 0 0
## 284 0 0 0 0 0 0 0
## 285 0 0 0 0 0 0 0
## 286 0 0 0 0 0 0 0
## 287 0 0 0 0 0 0 0
## 288 0 0 0 0 0 0 0
## 289 0 0 0 0 0 0 0
## 290 0 0 0 0 0 0 0
## 291 0 0 0 0 0 0 0
## 292 0 0 0 0 0 0 0
## 293 0 0 0 0 0 0 0
## 294 0 0 0 0 0 0 0
## 295 0 0 0 0 0 0 0
## 296 0 0 0 0 0 0 0
## 297 0 0 0 0 0 0 0
## 298 0 0 0 0 0 0 0
## 299 0 0 0 0 0 0 0
## 300 0 0 0 0 0 0 0
## 301 0 0 0 0 0 0 0
## 302 0 0 0 0 0 0 0
## 303 0 0 0 0 0 0 0
## 304 0 0 0 0 0 0 0
## 305 0 0 0 0 0 0 0
## 306 0 0 0 0 0 0 0
## 307 0 0 0 0 0 0 0
## 308 0 0 0 0 0 0 0
## 309 0 0 0 0 0 0 0
## 310 0 0 0 0 0 0 0
## 311 0 0 0 0 0 0 0
## 312 0 0 0 0 0 0 0
## 313 0 0 0 0 0 0 0
## 314 0 0 0 0 0 0 0
## 315 0 0 0 0 0 0 0
## 316 0 0 0 0 0 0 0
## 317 0 0 0 0 0 0 0
## 318 0 0 0 0 0 0 0
## 319 0 0 0 0 0 0 0
## 320 0 0 0 0 0 0 0
## 321 0 0 0 0 0 0 0
## 322 0 0 0 0 0 0 0
## 323 0 0 0 0 0 0 0
## 324 0 0 0 0 0 0 0
## 325 0 0 0 0 0 0 0
## 326 0 0 0 0 0 0 0
## 327 0 0 0 0 0 0 0
## 328 0 0 0 0 0 0 0
## 329 0 0 0 0 0 0 0
## 330 0 0 0 0 0 0 0
## 331 0 0 0 0 0 0 0
## 332 0 0 0 0 0 0 0
## 333 0 0 0 0 0 0 0
## 334 0 0 0 0 0 0 0
## 335 0 0 0 0 0 0 0
## 336 0 0 0 0 0 0 0
## 337 0 0 0 0 0 0 0
## 338 0 0 0 0 0 0 0
## 339 0 0 0 0 0 0 0
## 340 0 0 0 0 0 0 0
## 341 0 0 0 0 0 0 0
## 342 0 0 0 0 0 0 0
## 343 0 0 0 0 0 0 0
## 344 0 0 0 0 0 0 0
## 345 0 0 0 0 0 0 0
## 346 0 0 0 0 0 0 0
## 347 0 0 0 0 0 0 0
## 348 0 0 0 0 0 0 0
## 349 0 0 0 0 0 0 0
## 350 0 0 0 0 0 0 0
## 351 0 0 0 0 0 0 0
## 352 0 0 0 0 0 0 0
## 353 0 0 0 0 0 0 0
## takedownl.X1 takedownl.X2 hammerl.X1 hammerl.X2 Cross2l.X1 Knee2l.X1
## 170 0 0 0 0 0 0
## 171 0 0 0 0 0 0
## 172 0 0 0 0 0 0
## 173 0 0 0 0 0 0
## 174 0 0 0 0 0 0
## 175 0 0 0 0 0 0
## 176 0 0 0 0 0 0
## 177 0 0 0 0 0 0
## 178 0 0 0 0 0 0
## 179 0 0 0 0 0 0
## 180 0 0 0 0 0 0
## 181 0 0 0 0 0 0
## 182 0 0 0 0 0 0
## 183 0 0 0 0 0 0
## 184 0 0 0 0 0 0
## 185 0 0 0 0 0 0
## 186 0 0 0 0 0 0
## 187 0 0 0 0 0 0
## 188 0 0 0 0 0 1
## 189 0 0 0 0 0 0
## 190 0 0 0 0 0 0
## 191 0 0 0 0 0 0
## 192 0 0 0 0 0 0
## 193 0 0 0 0 0 0
## 194 0 0 0 0 0 0
## 195 0 0 0 0 0 0
## 196 0 0 0 0 0 0
## 197 0 0 0 0 0 0
## 198 0 0 0 0 0 0
## 199 0 0 0 0 0 1
## 200 0 0 0 0 0 0
## 201 0 0 0 0 0 0
## 202 0 0 0 0 0 0
## 203 0 0 0 0 0 0
## 204 0 0 0 0 0 0
## 205 0 0 0 0 0 0
## 206 0 0 0 0 0 0
## 207 0 0 0 0 0 0
## 208 0 0 0 0 0 0
## 209 0 0 0 0 0 0
## 210 0 0 0 0 0 0
## 211 0 0 0 0 0 0
## 212 0 0 0 0 0 0
## 213 0 0 0 0 0 0
## 214 0 0 0 0 0 0
## 215 0 0 0 0 0 0
## 216 0 0 0 0 0 0
## 217 0 0 0 0 0 0
## 218 0 0 0 0 0 0
## 219 0 0 0 0 0 0
## 220 0 0 0 0 0 0
## 221 0 0 0 0 0 0
## 222 0 0 0 0 0 0
## 223 0 0 0 0 0 0
## 224 0 0 0 0 0 0
## 225 0 0 0 0 0 0
## 226 0 0 0 0 0 0
## 227 0 0 0 0 0 0
## 228 0 0 0 0 0 0
## 229 1 0 0 0 0 0
## 230 0 0 0 0 0 0
## 231 0 0 0 0 0 0
## 232 0 0 0 0 0 0
## 233 0 0 0 0 0 0
## 234 0 0 0 0 0 0
## 235 0 0 0 0 0 0
## 236 0 0 0 0 0 0
## 237 0 0 0 0 0 0
## 238 0 0 0 0 0 0
## 239 0 0 0 0 0 0
## 240 0 0 0 0 0 0
## 241 0 0 0 0 0 0
## 242 0 0 0 0 0 0
## 243 0 0 0 0 0 0
## 244 0 0 0 0 0 0
## 245 0 0 0 0 0 0
## 246 0 0 0 0 0 0
## 247 0 0 0 0 0 0
## 248 0 0 0 0 0 0
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## 310 0 0 0 0 0 0 0
## 311 0 0 0 0 0 0 0
## 312 0 0 0 0 0 0 0
## 313 0 0 0 0 0 0 0
## 314 0 0 0 0 0 0 0
## 315 0 0 0 0 0 0 0
## 316 0 0 0 0 0 0 0
## 317 0 0 0 0 0 0 0
## 318 0 0 0 0 0 0 0
## 319 0 0 0 0 0 0 0
## 320 0 0 0 0 0 0 0
## 321 0 0 0 0 0 0 0
## 322 0 0 0 0 0 0 0
## 323 0 0 0 0 0 0 0
## 324 0 0 0 0 0 0 0
## 325 0 0 0 0 0 0 0
## 326 0 0 0 0 0 0 0
## 327 0 0 0 0 0 0 0
## 328 0 0 0 0 0 0 0
## 329 0 0 0 0 0 0 0
## 330 0 0 0 0 0 0 0
## 331 0 0 0 0 0 0 0
## 332 0 0 0 0 0 0 0
## 333 0 0 0 0 0 0 0
## 334 0 0 0 0 0 0 0
## 335 0 0 0 0 0 0 0
## 336 0 0 0 0 0 0 0
## 337 0 0 0 0 0 0 0
## 338 0 0 0 0 0 0 0
## 339 0 0 0 0 0 0 0
## 340 0 0 0 0 0 0 0
## 341 0 0 0 0 0 0 0
## 342 0 0 0 0 0 0 0
## 343 0 0 0 0 0 0 0
## 344 0 0 0 0 0 0 0
## 345 0 0 0 0 0 0 0
## 346 0 0 0 0 0 0 0
## 347 0 0 0 0 0 0 0
## 348 0 0 0 0 0 0 0
## 349 0 0 0 0 0 0 0
## 350 0 0 0 0 0 0 0
## 351 0 0 0 0 0 0 0
## 352 0 0 0 0 0 0 0
## 353 0 0 0 0 0 0 0
## Jab3r.X2 Kick3r.X2 upper3r.X1 upper3r.X2 takedown3r.X1 takedown3r.X2
## 170 0 0 0 0 0 0
## 171 0 0 0 0 0 0
## 172 0 0 0 0 0 0
## 173 0 0 0 0 0 0
## 174 0 0 0 0 0 0
## 175 0 0 0 0 0 0
## 176 0 0 0 0 0 0
## 177 0 0 0 0 0 0
## 178 0 0 0 0 0 0
## 179 0 0 0 0 0 0
## 180 0 0 0 0 0 0
## 181 0 0 0 0 0 0
## 182 0 0 0 0 0 0
## 183 0 0 0 0 0 0
## 184 0 0 0 0 0 0
## 185 0 0 0 0 0 0
## 186 0 0 0 0 0 0
## 187 0 0 0 0 0 0
## 188 0 0 0 0 0 0
## 189 0 0 0 0 0 0
## 190 0 0 0 0 0 0
## 191 0 0 0 0 0 0
## 192 0 0 0 0 0 0
## 193 0 0 0 0 0 0
## 194 0 0 0 0 0 0
## 195 0 0 0 0 0 0
## 196 0 0 0 0 0 0
## 197 0 0 0 0 0 0
## 198 0 0 0 0 0 0
## 199 0 0 0 0 0 0
## 200 0 0 0 0 0 0
## 201 0 0 0 0 0 0
## 202 0 0 0 0 0 0
## 203 0 0 0 0 0 0
## 204 0 0 0 0 0 0
## 205 0 0 0 0 0 0
## 206 0 0 0 0 0 0
## 207 0 0 0 0 0 0
## 208 0 0 0 0 0 0
## 209 0 0 0 0 0 0
## 210 0 0 0 0 0 0
## 211 0 0 0 0 0 0
## 212 0 0 0 0 0 0
## 213 0 0 0 0 0 0
## 214 0 0 0 0 0 0
## 215 0 0 0 0 0 0
## 216 0 0 0 0 0 0
## 217 0 0 0 0 0 0
## 218 0 0 0 0 0 0
## 219 0 0 0 0 0 0
## 220 0 0 0 0 0 0
## 221 0 0 0 0 0 0
## 222 0 0 0 0 0 0
## 223 0 0 0 0 0 0
## 224 0 0 0 0 0 0
## 225 0 0 0 0 0 0
## 226 0 0 0 0 0 0
## 227 0 0 0 0 0 0
## 228 0 0 0 0 0 0
## 229 0 0 0 0 0 0
## 230 0 0 0 0 0 0
## 231 0 0 0 0 0 0
## 232 0 0 0 0 0 0
## 233 0 0 0 0 0 0
## 234 0 0 0 0 0 0
## 235 0 0 0 0 0 0
## 236 0 0 0 0 0 0
## 237 0 0 0 0 0 0
## 238 0 0 0 0 0 0
## 239 0 0 0 0 0 0
## 240 0 0 0 0 0 0
## 241 0 0 0 0 0 0
## 242 0 0 0 0 0 0
## 243 0 0 0 0 0 0
## 244 0 0 0 0 0 0
## 245 0 0 0 0 0 0
## 246 0 0 0 0 0 0
## 247 0 0 0 0 0 0
## 248 0 0 0 0 0 0
## 249 0 0 0 0 0 0
## 250 0 0 0 0 0 0
## 251 0 0 0 0 0 0
## 252 0 0 0 0 0 0
## 253 0 0 0 0 0 0
## 254 0 0 0 0 0 0
## 255 0 0 0 0 0 0
## 256 0 0 0 0 0 0
## 257 0 0 0 0 0 0
## 258 0 0 0 0 0 0
## 259 0 0 0 0 0 0
## 260 0 0 0 0 0 0
## 261 0 0 0 0 0 0
## 262 0 0 0 0 0 0
## 263 0 0 0 0 0 0
## 264 0 0 0 0 0 0
## 265 0 0 0 0 0 0
## 266 0 0 0 0 0 0
## 267 0 0 0 0 0 0
## 268 0 0 0 0 0 0
## 269 0 0 0 0 0 0
## 270 0 0 0 0 0 0
## 271 0 0 0 0 0 0
## 272 0 0 0 0 0 0
## 273 0 0 0 0 0 0
## 274 0 0 0 0 0 0
## 275 0 0 0 0 0 0
## 276 0 0 0 0 0 0
## 277 0 0 0 0 0 0
## 278 0 0 0 0 0 0
## 279 0 0 0 0 0 0
## 280 0 0 0 0 0 0
## 281 0 0 0 0 0 0
## 282 0 0 0 0 0 0
## 283 0 0 0 0 0 0
## 284 0 0 0 0 0 0
## 285 0 0 0 0 0 0
## 286 0 0 0 0 0 0
## 287 0 0 0 0 0 0
## 288 0 0 0 0 0 0
## 289 0 0 0 0 0 0
## 290 0 0 0 0 0 0
## 291 0 0 0 0 0 0
## 292 0 0 0 0 0 0
## 293 0 0 0 0 0 0
## 294 0 0 0 0 0 0
## 295 0 0 0 0 0 0
## 296 0 0 0 0 0 0
## 297 0 0 0 0 0 0
## 298 0 0 0 0 0 0
## 299 0 0 0 0 0 0
## 300 0 0 0 0 0 0
## 301 0 0 0 0 0 0
## 302 0 0 0 0 0 0
## 303 0 0 0 0 0 0
## 304 0 0 0 0 0 0
## 305 0 0 0 0 0 0
## 306 0 0 0 0 0 0
## 307 0 0 0 0 0 0
## 308 0 0 0 0 0 0
## 309 0 0 0 0 0 0
## 310 0 0 0 0 0 0
## 311 0 0 0 0 0 0
## 312 0 0 0 0 0 0
## 313 0 0 0 0 0 0
## 314 0 0 0 0 0 0
## 315 0 0 0 0 0 0
## 316 0 0 0 0 0 0
## 317 0 0 0 0 0 0
## 318 0 0 0 0 0 0
## 319 0 0 0 0 0 0
## 320 0 0 0 0 0 0
## 321 0 0 0 0 0 0
## 322 0 0 0 0 0 0
## 323 0 0 0 0 0 0
## 324 0 0 0 0 0 0
## 325 0 0 0 0 0 0
## 326 0 0 0 0 0 0
## 327 0 0 0 0 0 0
## 328 0 0 0 0 0 0
## 329 0 0 0 0 0 0
## 330 0 0 0 0 0 0
## 331 0 0 0 0 0 0
## 332 0 0 0 0 0 0
## 333 0 0 0 0 0 0
## 334 0 0 0 0 0 0
## 335 0 0 0 0 0 0
## 336 0 0 0 0 0 0
## 337 0 0 0 0 0 0
## 338 0 0 0 0 0 0
## 339 0 0 0 0 0 0
## 340 0 0 0 0 0 0
## 341 0 0 0 0 0 0
## 342 0 0 0 0 0 0
## 343 0 0 0 0 0 0
## 344 0 0 0 0 0 0
## 345 0 0 0 0 0 0
## 346 0 0 0 0 0 0
## 347 0 0 0 0 0 0
## 348 0 0 0 0 0 0
## 349 0 0 0 0 0 0
## 350 0 0 0 0 0 0
## 351 0 0 0 0 0 0
## 352 0 0 0 0 0 0
## 353 0 0 0 0 0 0
## hammer3r.X1 hammer3r.X2
## 170 0 0
## 171 0 0
## 172 0 0
## 173 0 0
## 174 0 0
## 175 0 0
## 176 0 0
## 177 0 0
## 178 0 0
## 179 0 0
## 180 0 0
## 181 0 0
## 182 0 0
## 183 0 0
## 184 0 0
## 185 0 0
## 186 0 0
## 187 0 0
## 188 0 0
## 189 0 0
## 190 0 0
## 191 0 0
## 192 0 0
## 193 0 0
## 194 0 0
## 195 0 0
## 196 0 0
## 197 0 0
## 198 0 0
## 199 0 0
## 200 0 0
## 201 0 0
## 202 0 0
## 203 0 0
## 204 0 0
## 205 0 0
## 206 0 0
## 207 0 0
## 208 0 0
## 209 0 0
## 210 0 0
## 211 0 0
## 212 0 0
## 213 0 0
## 214 0 0
## 215 0 0
## 216 0 0
## 217 0 0
## 218 0 0
## 219 0 0
## 220 0 0
## 221 0 0
## 222 0 0
## 223 0 0
## 224 0 0
## 225 0 0
## 226 0 0
## 227 0 0
## 228 0 0
## 229 0 0
## 230 0 0
## 231 0 0
## 232 0 0
## 233 0 0
## 234 0 0
## 235 0 0
## 236 0 0
## 237 0 0
## 238 0 0
## 239 0 0
## 240 0 0
## 241 0 0
## 242 0 0
## 243 0 0
## 244 0 0
## 245 0 0
## 246 0 0
## 247 0 0
## 248 0 0
## 249 0 0
## 250 0 0
## 251 0 0
## 252 0 0
## 253 0 0
## 254 0 0
## 255 0 0
## 256 0 0
## 257 0 0
## 258 0 0
## 259 0 0
## 260 0 0
## 261 0 0
## 262 0 0
## 263 0 0
## 264 0 0
## 265 0 0
## 266 0 0
## 267 0 0
## 268 0 0
## 269 0 0
## 270 0 0
## 271 0 0
## 272 0 0
## 273 0 0
## 274 0 0
## 275 0 0
## 276 0 0
## 277 0 0
## 278 0 0
## 279 0 0
## 280 0 0
## 281 0 0
## 282 0 0
## 283 0 0
## 284 0 0
## 285 0 0
## 286 0 0
## 287 0 0
## 288 0 0
## 289 0 0
## 290 0 0
## 291 0 0
## 292 0 0
## 293 0 0
## 294 0 0
## 295 0 0
## 296 0 0
## 297 0 0
## 298 0 0
## 299 0 0
## 300 0 0
## 301 0 0
## 302 0 0
## 303 0 0
## 304 0 0
## 305 0 0
## 306 0 0
## 307 0 0
## 308 0 0
## 309 0 0
## 310 0 0
## 311 0 0
## 312 0 0
## 313 0 0
## 314 0 0
## 315 0 0
## 316 0 0
## 317 0 0
## 318 0 0
## 319 0 0
## 320 0 0
## 321 0 0
## 322 0 0
## 323 0 0
## 324 0 0
## 325 0 0
## 326 0 0
## 327 0 0
## 328 0 0
## 329 0 0
## 330 0 0
## 331 0 0
## 332 0 0
## 333 0 0
## 334 0 0
## 335 0 0
## 336 0 0
## 337 0 0
## 338 0 0
## 339 0 0
## 340 0 0
## 341 0 0
## 342 0 0
## 343 0 0
## 344 0 0
## 345 0 0
## 346 0 0
## 347 0 0
## 348 0 0
## 349 0 0
## 350 0 0
## 351 0 0
## 352 0 0
## 353 0 0Third opponent:
Table3 <- Added3[split2:(length(Added3$Round)),]
Table3## Round SecondsIntoRound lastAction SecondsLastRoundAction cmTotHitsR.X1
## 354 1 7 202 7 NA
## 355 1 8 7 1 NA
## 356 1 9 8 1 NA
## 357 1 10 9 1 NA
## 358 1 17 10 7 NA
## 359 1 18 17 1 NA
## 360 1 19 18 1 NA
## 361 1 20 19 1 NA
## 362 1 22 20 2 NA
## 363 1 23 22 1 NA
## 364 1 24 23 1 NA
## 365 1 31 24 7 NA
## 366 1 32 31 1 NA
## 367 1 34 32 2 NA
## 368 1 36 34 2 NA
## 369 1 37 36 1 NA
## 370 1 38 37 1 NA
## 371 1 40 38 2 NA
## 372 1 41 40 1 NA
## 373 1 45 41 4 NA
## 374 1 46 45 1 NA
## 375 1 47 46 1 NA
## 376 1 48 47 1 NA
## 377 1 49 48 1 NA
## 378 1 50 49 1 NA
## 379 1 51 50 1 NA
## 380 1 52 51 1 NA
## 381 1 53 52 1 NA
## 382 1 54 53 1 NA
## 383 1 55 54 1 NA
## 384 1 56 55 1 NA
## 385 1 57 56 1 NA
## 386 1 58 57 1 NA
## 387 1 59 58 1 NA
## 388 1 60 59 1 NA
## 389 1 61 60 1 NA
## 390 1 62 61 1 NA
## 391 1 63 62 1 NA
## 392 1 64 63 1 NA
## 393 1 65 64 1 NA
## 394 1 66 65 1 NA
## 395 1 67 66 1 NA
## 396 1 68 67 1 NA
## 397 1 69 68 1 NA
## 398 1 70 69 1 NA
## 399 1 71 70 1 NA
## 400 1 72 71 1 NA
## 401 1 73 72 1 NA
## 402 1 74 73 1 NA
## 403 1 75 74 1 NA
## 404 1 76 75 1 NA
## 405 1 77 76 1 NA
## 406 1 78 77 1 NA
## 407 1 79 78 1 NA
## 408 1 80 79 1 NA
## 409 1 81 80 1 NA
## 410 1 82 81 1 NA
## 411 1 83 82 1 NA
## 412 1 84 83 1 NA
## 413 1 85 84 1 NA
## 414 1 86 85 1 NA
## 415 1 87 86 1 NA
## 416 1 88 87 1 NA
## 417 1 89 88 1 NA
## 418 1 90 89 1 NA
## 419 1 91 90 1 NA
## 420 1 92 91 1 NA
## 421 1 93 92 1 NA
## 422 1 94 93 1 NA
## 423 1 95 94 1 NA
## 424 1 96 95 1 NA
## 425 1 97 96 1 NA
## 426 1 98 97 1 NA
## 427 1 99 98 1 NA
## 428 1 100 99 1 NA
## 429 1 101 100 1 NA
## 430 1 102 101 1 NA
## 431 1 103 102 1 NA
## 432 1 104 103 1 NA
## 433 1 105 104 1 NA
## 434 1 117 105 12 NA
## 435 1 119 117 2 NA
## 436 1 127 119 8 NA
## 437 1 128 127 1 NA
## 438 1 129 128 1 NA
## 439 1 130 129 1 NA
## 440 1 139 130 9 NA
## 441 1 142 139 3 NA
## 442 1 145 142 3 NA
## 443 1 157 145 12 NA
## 444 1 158 157 1 NA
## 445 1 160 158 2 NA
## 446 1 164 160 4 NA
## 447 1 166 164 2 NA
## 448 1 171 166 5 NA
## 449 1 176 171 5 NA
## 450 1 177 176 1 NA
## 451 1 178 177 1 NA
## 452 1 179 178 1 NA
## 453 1 187 179 8 NA
## 454 1 196 187 9 NA
## 455 1 209 196 13 NA
## 456 1 213 209 4 NA
## 457 1 214 213 1 NA
## 458 1 227 214 13 NA
## 459 1 228 227 1 NA
## 460 1 229 228 1 NA
## 461 1 230 229 1 NA
## 462 1 231 230 1 NA
## 463 1 232 231 1 NA
## 464 1 234 232 2 NA
## 465 1 235 234 1 NA
## 466 1 236 235 1 NA
## 467 1 237 236 1 NA
## 468 1 238 237 1 NA
## 469 1 239 238 1 NA
## 470 1 240 239 1 NA
## 471 1 241 240 1 NA
## 472 1 242 241 1 NA
## 473 1 243 242 1 NA
## 474 1 244 243 1 NA
## 475 1 245 244 1 NA
## 476 1 246 245 1 NA
## 477 1 247 246 1 NA
## 478 1 248 247 1 NA
## 479 1 249 248 1 NA
## 480 1 250 249 1 NA
## 481 1 251 250 1 NA
## 482 1 252 251 1 NA
## 483 1 253 252 1 NA
## 484 1 254 253 1 NA
## 485 1 255 254 1 NA
## 486 1 256 255 1 NA
## 487 1 257 256 1 NA
## 488 1 258 257 1 NA
## 489 1 259 258 1 NA
## 490 1 260 259 1 NA
## 491 1 261 260 1 NA
## 492 1 262 261 1 NA
## 493 1 263 262 1 NA
## 494 1 264 263 1 NA
## 495 1 265 264 1 NA
## 496 1 266 265 1 NA
## 497 1 267 266 1 NA
## 498 1 268 267 1 NA
## 499 1 269 268 1 NA
## 500 1 270 269 1 NA
## 501 1 271 270 1 NA
## 502 1 272 271 1 NA
## 503 1 273 272 1 NA
## 504 1 274 273 1 NA
## 505 1 275 274 1 NA
## 506 1 276 275 1 NA
## 507 1 277 276 1 NA
## 508 1 278 277 1 NA
## 509 1 279 278 1 NA
## 510 1 280 279 1 NA
## 511 1 281 280 1 NA
## 512 1 282 281 1 NA
## 513 1 283 282 1 NA
## 514 1 284 283 1 NA
## 515 1 285 284 1 NA
## 516 1 286 285 1 NA
## 517 1 287 286 1 NA
## 518 1 288 287 1 NA
## 519 1 289 288 1 NA
## 520 1 290 289 1 NA
## 521 1 291 290 1 NA
## 522 1 292 291 1 NA
## 523 1 293 292 1 NA
## 524 1 294 293 1 NA
## 525 1 295 294 1 NA
## 526 1 296 295 1 NA
## 527 1 297 296 1 NA
## 528 1 298 297 1 NA
## 529 1 299 298 1 NA
## cmTotHitsL.X1 cmTotHitsM.X1 TotLandsX1 TotMissedX1 TotReceivedX1
## 354 NA NA 0 1 0
## 355 NA NA 0 0 0
## 356 NA NA 0 1 0
## 357 NA NA 1 0 0
## 358 NA NA 0 0 0
## 359 NA NA 0 1 0
## 360 NA NA 0 0 1
## 361 NA NA 0 1 0
## 362 NA NA 0 0 0
## 363 NA NA 0 0 0
## 364 NA NA 0 1 0
## 365 NA NA 1 0 1
## 366 NA NA 0 1 0
## 367 NA NA 0 0 0
## 368 NA NA 0 0 1
## 369 NA NA 1 0 0
## 370 NA NA 0 0 0
## 371 NA NA 0 0 0
## 372 NA NA 0 0 0
## 373 NA NA 0 2 1
## 374 NA NA 0 0 0
## 375 NA NA 0 0 0
## 376 NA NA 1 0 0
## 377 NA NA 0 0 0
## 378 NA NA 0 0 0
## 379 NA NA 0 0 0
## 380 NA NA 0 0 0
## 381 NA NA 0 0 0
## 382 NA NA 0 0 0
## 383 NA NA 1 0 0
## 384 NA NA 1 0 0
## 385 NA NA 0 0 0
## 386 NA NA 0 0 0
## 387 NA NA 0 0 0
## 388 NA NA 1 0 0
## 389 NA NA 0 0 0
## 390 NA NA 0 0 0
## 391 NA NA 0 0 0
## 392 NA NA 0 0 0
## 393 NA NA 1 0 0
## 394 NA NA 0 0 2
## 395 NA NA 0 0 0
## 396 NA NA 0 0 0
## 397 NA NA 0 0 0
## 398 NA NA 1 0 0
## 399 NA NA 0 0 0
## 400 NA NA 0 0 0
## 401 NA NA 0 0 0
## 402 NA NA 0 0 0
## 403 NA NA 0 0 0
## 404 NA NA 0 0 0
## 405 NA NA 1 0 0
## 406 NA NA 0 0 0
## 407 NA NA 0 0 2
## 408 NA NA 0 0 2
## 409 NA NA 0 0 0
## 410 NA NA 0 0 0
## 411 NA NA 0 0 0
## 412 NA NA 0 0 2
## 413 NA NA 0 0 0
## 414 NA NA 0 0 0
## 415 NA NA 0 0 0
## 416 NA NA 0 0 0
## 417 NA NA 0 0 0
## 418 NA NA 0 0 0
## 419 NA NA 0 0 0
## 420 NA NA 0 0 0
## 421 NA NA 0 0 0
## 422 NA NA 0 0 0
## 423 NA NA 0 0 0
## 424 NA NA 0 0 0
## 425 NA NA 0 0 0
## 426 NA NA 0 0 0
## 427 NA NA 1 0 0
## 428 NA NA 1 0 0
## 429 NA NA 0 0 0
## 430 NA NA 0 0 2
## 431 NA NA 0 0 2
## 432 NA NA 0 0 2
## 433 NA NA 0 0 1
## 434 NA NA 0 1 0
## 435 NA NA 0 1 2
## 436 NA NA 0 0 1
## 437 NA NA 0 0 0
## 438 NA NA 0 0 0
## 439 NA NA 0 0 0
## 440 NA NA 0 0 1
## 441 NA NA 0 1 0
## 442 NA NA 0 2 0
## 443 NA NA 0 0 0
## 444 NA NA 0 0 0
## 445 NA NA 0 1 0
## 446 NA NA 0 1 0
## 447 NA NA 0 1 0
## 448 NA NA 0 0 0
## 449 NA NA 0 0 0
## 450 NA NA 0 1 0
## 451 NA NA 0 1 0
## 452 NA NA 0 0 1
## 453 NA NA 0 1 0
## 454 NA NA 0 0 0
## 455 NA NA 0 1 1
## 456 NA NA 0 0 1
## 457 NA NA 0 0 0
## 458 NA NA 1 0 0
## 459 NA NA 1 0 0
## 460 NA NA 0 1 0
## 461 NA NA 0 0 1
## 462 NA NA 0 1 1
## 463 NA NA 0 1 0
## 464 NA NA 0 0 0
## 465 NA NA 0 0 0
## 466 NA NA 0 0 0
## 467 NA NA 0 0 0
## 468 NA NA 0 0 0
## 469 NA NA 0 0 0
## 470 NA NA 0 0 0
## 471 NA NA 0 0 0
## 472 NA NA 1 0 0
## 473 NA NA 0 0 0
## 474 NA NA 0 0 0
## 475 NA NA 0 0 0
## 476 NA NA 0 0 0
## 477 NA NA 0 0 0
## 478 NA NA 0 0 0
## 479 NA NA 0 2 0
## 480 NA NA 0 0 0
## 481 NA NA 0 0 0
## 482 NA NA 0 0 0
## 483 NA NA 0 0 0
## 484 NA NA 1 0 0
## 485 NA NA 0 2 0
## 486 NA NA 0 0 0
## 487 NA NA 0 0 0
## 488 NA NA 0 0 0
## 489 NA NA 0 0 0
## 490 NA NA 0 0 0
## 491 NA NA 0 0 0
## 492 NA NA 0 0 2
## 493 NA NA 0 0 0
## 494 NA NA 0 0 0
## 495 NA NA 0 0 0
## 496 NA NA 0 0 0
## 497 NA NA 0 0 0
## 498 NA NA 0 0 0
## 499 NA NA 0 0 0
## 500 NA NA 0 0 0
## 501 NA NA 0 0 0
## 502 NA NA 0 0 0
## 503 NA NA 0 0 0
## 504 NA NA 0 0 0
## 505 NA NA 0 0 0
## 506 NA NA 1 0 0
## 507 NA NA 0 0 0
## 508 NA NA 0 0 0
## 509 NA NA 0 0 0
## 510 NA NA 0 0 0
## 511 NA NA 0 0 0
## 512 NA NA 0 0 0
## 513 NA NA 0 0 0
## 514 NA NA 0 0 0
## 515 NA NA 0 0 0
## 516 NA NA 0 0 0
## 517 NA NA 0 0 0
## 518 NA NA 0 0 0
## 519 NA NA 0 0 0
## 520 NA NA 0 0 0
## 521 NA NA 0 0 0
## 522 NA NA 0 0 2
## 523 NA NA 0 0 0
## 524 NA NA 0 0 0
## 525 NA NA 0 0 0
## 526 NA NA 0 0 0
## 527 NA NA 0 0 0
## 528 NA NA 0 0 0
## 529 NA NA 0 0 0
## cmTotHitsR.X2 cmTotHitsL.X2 cmTotHitsM.X2 TotLandsX2 TotMissedX2
## 354 NA NA NA 0 1
## 355 NA NA NA 0 2
## 356 NA NA NA 0 1
## 357 NA NA NA 0 1
## 358 NA NA NA 0 1
## 359 NA NA NA 0 0
## 360 NA NA NA 1 0
## 361 NA NA NA 0 0
## 362 NA NA NA 0 1
## 363 NA NA NA 0 2
## 364 NA NA NA 0 0
## 365 NA NA NA 1 0
## 366 NA NA NA 0 0
## 367 NA NA NA 0 1
## 368 NA NA NA 1 1
## 369 NA NA NA 0 1
## 370 NA NA NA 0 0
## 371 NA NA NA 0 0
## 372 NA NA NA 0 1
## 373 NA NA NA 1 0
## 374 NA NA NA 0 0
## 375 NA NA NA 0 0
## 376 NA NA NA 0 0
## 377 NA NA NA 0 0
## 378 NA NA NA 0 0
## 379 NA NA NA 0 0
## 380 NA NA NA 0 0
## 381 NA NA NA 0 0
## 382 NA NA NA 0 0
## 383 NA NA NA 0 0
## 384 NA NA NA 0 0
## 385 NA NA NA 0 0
## 386 NA NA NA 0 0
## 387 NA NA NA 0 0
## 388 NA NA NA 0 0
## 389 NA NA NA 0 0
## 390 NA NA NA 0 0
## 391 NA NA NA 0 0
## 392 NA NA NA 0 0
## 393 NA NA NA 0 0
## 394 NA NA NA 2 0
## 395 NA NA NA 0 0
## 396 NA NA NA 0 0
## 397 NA NA NA 0 0
## 398 NA NA NA 0 0
## 399 NA NA NA 0 0
## 400 NA NA NA 0 0
## 401 NA NA NA 0 0
## 402 NA NA NA 0 0
## 403 NA NA NA 0 0
## 404 NA NA NA 0 0
## 405 NA NA NA 0 0
## 406 NA NA NA 0 0
## 407 NA NA NA 2 0
## 408 NA NA NA 2 0
## 409 NA NA NA 0 0
## 410 NA NA NA 0 0
## 411 NA NA NA 0 0
## 412 NA NA NA 2 0
## 413 NA NA NA 0 0
## 414 NA NA NA 0 0
## 415 NA NA NA 0 0
## 416 NA NA NA 0 0
## 417 NA NA NA 0 0
## 418 NA NA NA 0 0
## 419 NA NA NA 0 0
## 420 NA NA NA 0 0
## 421 NA NA NA 0 0
## 422 NA NA NA 0 0
## 423 NA NA NA 0 0
## 424 NA NA NA 0 0
## 425 NA NA NA 0 0
## 426 NA NA NA 0 0
## 427 NA NA NA 0 0
## 428 NA NA NA 0 0
## 429 NA NA NA 0 0
## 430 NA NA NA 2 0
## 431 NA NA NA 2 0
## 432 NA NA NA 2 0
## 433 NA NA NA 1 0
## 434 NA NA NA 0 0
## 435 NA NA NA 2 1
## 436 NA NA NA 1 0
## 437 NA NA NA 0 0
## 438 NA NA NA 0 2
## 439 NA NA NA 0 0
## 440 NA NA NA 1 0
## 441 NA NA NA 0 1
## 442 NA NA NA 0 0
## 443 NA NA NA 0 2
## 444 NA NA NA 0 0
## 445 NA NA NA 0 0
## 446 NA NA NA 0 0
## 447 NA NA NA 0 0
## 448 NA NA NA 0 1
## 449 NA NA NA 0 1
## 450 NA NA NA 0 0
## 451 NA NA NA 0 1
## 452 NA NA NA 1 0
## 453 NA NA NA 0 0
## 454 NA NA NA 0 1
## 455 NA NA NA 1 0
## 456 NA NA NA 1 0
## 457 NA NA NA 0 2
## 458 NA NA NA 0 0
## 459 NA NA NA 0 0
## 460 NA NA NA 0 2
## 461 NA NA NA 1 0
## 462 NA NA NA 1 0
## 463 NA NA NA 0 1
## 464 NA NA NA 0 0
## 465 NA NA NA 0 0
## 466 NA NA NA 0 0
## 467 NA NA NA 0 0
## 468 NA NA NA 0 0
## 469 NA NA NA 0 2
## 470 NA NA NA 0 0
## 471 NA NA NA 0 0
## 472 NA NA NA 0 0
## 473 NA NA NA 0 0
## 474 NA NA NA 0 0
## 475 NA NA NA 0 0
## 476 NA NA NA 0 0
## 477 NA NA NA 0 0
## 478 NA NA NA 0 0
## 479 NA NA NA 0 0
## 480 NA NA NA 0 0
## 481 NA NA NA 0 0
## 482 NA NA NA 0 0
## 483 NA NA NA 0 0
## 484 NA NA NA 0 0
## 485 NA NA NA 0 0
## 486 NA NA NA 0 0
## 487 NA NA NA 0 0
## 488 NA NA NA 0 0
## 489 NA NA NA 0 0
## 490 NA NA NA 0 0
## 491 NA NA NA 0 0
## 492 NA NA NA 2 0
## 493 NA NA NA 0 0
## 494 NA NA NA 0 0
## 495 NA NA NA 0 0
## 496 NA NA NA 0 0
## 497 NA NA NA 0 0
## 498 NA NA NA 0 0
## 499 NA NA NA 0 0
## 500 NA NA NA 0 0
## 501 NA NA NA 0 0
## 502 NA NA NA 0 0
## 503 NA NA NA 0 0
## 504 NA NA NA 0 0
## 505 NA NA NA 0 0
## 506 NA NA NA 0 0
## 507 NA NA NA 0 0
## 508 NA NA NA 0 0
## 509 NA NA NA 0 0
## 510 NA NA NA 0 0
## 511 NA NA NA 0 0
## 512 NA NA NA 0 0
## 513 NA NA NA 0 0
## 514 NA NA NA 0 0
## 515 NA NA NA 0 0
## 516 NA NA NA 0 0
## 517 NA NA NA 0 0
## 518 NA NA NA 0 0
## 519 NA NA NA 0 0
## 520 NA NA NA 0 0
## 521 NA NA NA 0 0
## 522 NA NA NA 2 0
## 523 NA NA NA 0 0
## 524 NA NA NA 0 0
## 525 NA NA NA 0 0
## 526 NA NA NA 0 0
## 527 NA NA NA 0 0
## 528 NA NA NA 0 0
## 529 NA NA NA 0 0
## TotReceivedX2 Time
## 354 0 4:52
## 355 0 4:51
## 356 0 4:50
## 357 1 4:49
## 358 0 4:42
## 359 0 4:41
## 360 0 4:40
## 361 0 4:39
## 362 0 4:37
## 363 0 4:36
## 364 0 4:35
## 365 1 4:28
## 366 0 4:27
## 367 0 4:25
## 368 0 4:23
## 369 1 4:22
## 370 0 4:21
## 371 0 4:19
## 372 0 4:18
## 373 0 4:14
## 374 0 4:13
## 375 0 4:12
## 376 2 4:11
## 377 0 4:10
## 378 0 4:09
## 379 0 4:08
## 380 0 4:07
## 381 0 4:06
## 382 0 4:05
## 383 2 4:04
## 384 2 4:03
## 385 0 4:02
## 386 0 4:01
## 387 0 4:00
## 388 2 3:59
## 389 0 3:58
## 390 0 3:57
## 391 0 3:56
## 392 0 3:55
## 393 2 3:54
## 394 0 3:53
## 395 0 3:52
## 396 0 3:51
## 397 0 3:50
## 398 2 3:49
## 399 0 3:48
## 400 0 3:47
## 401 0 3:46
## 402 0 3:45
## 403 0 3:44
## 404 0 3:43
## 405 2 3:42
## 406 0 3:41
## 407 0 3:40
## 408 0 3:39
## 409 0 3:38
## 410 0 3:37
## 411 0 3:36
## 412 0 3:35
## 413 0 3:34
## 414 0 3:33
## 415 0 3:32
## 416 0 3:31
## 417 0 3:30
## 418 0 3:29
## 419 0 3:28
## 420 0 3:27
## 421 0 3:26
## 422 0 3:25
## 423 0 3:24
## 424 0 3:23
## 425 0 3:22
## 426 0 3:21
## 427 2 3:20
## 428 2 3:19
## 429 0 3:18
## 430 0 3:17
## 431 0 3:16
## 432 0 3:15
## 433 0 3:14
## 434 0 3:02
## 435 0 3:00
## 436 0 2:52
## 437 0 2:51
## 438 0 2:50
## 439 0 2:49
## 440 0 2:40
## 441 0 2:37
## 442 0 2:34
## 443 0 2:22
## 444 0 2:21
## 445 0 2:19
## 446 0 2:15
## 447 0 2:13
## 448 0 2:08
## 449 0 2:03
## 450 0 2:02
## 451 0 2:01
## 452 0 2:00
## 453 0 1:52
## 454 0 1:43
## 455 0 1:30
## 456 0 1:26
## 457 0 1:25
## 458 1 1:12
## 459 1 1:11
## 460 0 1:10
## 461 0 1:09
## 462 0 1:08
## 463 0 1:07
## 464 0 1:05
## 465 0 1:04
## 466 0 1:03
## 467 0 1:02
## 468 0 1:01
## 469 0 1:00
## 470 0 0:59
## 471 0 0:58
## 472 2 0:57
## 473 0 0:56
## 474 0 0:55
## 475 0 0:54
## 476 0 0:53
## 477 0 0:52
## 478 0 0:51
## 479 0 0:50
## 480 0 0:49
## 481 0 0:48
## 482 0 0:47
## 483 0 0:46
## 484 2 0:45
## 485 0 0:44
## 486 0 0:43
## 487 0 0:42
## 488 0 0:41
## 489 0 0:40
## 490 0 0:39
## 491 0 0:38
## 492 0 0:37
## 493 0 0:36
## 494 0 0:35
## 495 0 0:34
## 496 0 0:33
## 497 0 0:32
## 498 0 0:31
## 499 0 0:30
## 500 0 0:29
## 501 0 0:28
## 502 0 0:27
## 503 0 0:26
## 504 0 0:25
## 505 0 0:24
## 506 2 0:23
## 507 0 0:22
## 508 0 0:21
## 509 0 0:20
## 510 0 0:19
## 511 0 0:18
## 512 0 0:17
## 513 0 0:16
## 514 0 0:15
## 515 0 0:14
## 516 0 0:13
## 517 0 0:12
## 518 0 0:11
## 519 0 0:10
## 520 0 0:09
## 521 0 0:08
## 522 0 0:07
## 523 0 0:06
## 524 0 0:05
## 525 0 0:04
## 526 0 0:03
## 527 0 0:02
## 528 0 0:01
## 529 0 0:00
## FighterActionReactions.X1
## 354 missed R cross
## 355 <NA>
## 356 missed R cross
## 357 lands R mt kick
## 358 <NA>
## 359 grabs leg, misses single leg takedown
## 360 <NA>
## 361 missed L knee to body, missed L to face
## 362 <NA>
## 363 <NA>
## 364 missed R cross
## 365 lands flying elbow to face
## 366 missed R cross to face
## 367 <NA>
## 368 <NA>
## 369 lands R push kick
## 370 grabs head against cage
## 371 pushes against cage
## 372 <NA>
## 373 misses takedown holding upper bodyhold starts and caught in R arm hold
## 374 caught in R arm hold and holding upper body hold continues
## 375 caught in R arm hold and holding upper body hold continues
## 376 lands L knee to inner R upper leg while caught in R arm hold and holding upper body hold continues
## 377 caught in R arm hold and holding upper body hold continues
## 378 caught in R arm hold and holding upper body hold continues
## 379 caught in R arm hold and holding upper body hold continues
## 380 caught in R arm hold and holding upper body hold continues
## 381 caught in R arm hold and holding upper body hold continues
## 382 caught in R arm hold and holding upper body hold continues
## 383 lands R knee to inner R leg caught in R arm hold and holding upper body hold continues
## 384 lands R knee to inner R leg caught in R arm hold and holding upper body hold continues
## 385 caught in R arm hold and holding upper body hold continues
## 386 caught in R arm hold and holding upper body hold continues
## 387 caught in R arm hold and holding upper body hold continues
## 388 lands R knee to inner R leg caught in R arm hold and holding upper body hold continues
## 389 caught in R arm hold and holding upper body hold continues
## 390 caught in R arm hold and holding upper body hold continues
## 391 caught in R arm hold and holding upper body hold continues
## 392 caught in R arm hold and holding upper body hold continues
## 393 lands R knee to inner R leg caught in R arm hold and holding upper body hold continues
## 394 caught in R arm hold and holding upper body hold continues
## 395 caught in R arm hold and holding upper body hold continues
## 396 caught in R arm hold and holding upper body hold continues
## 397 caught in R arm hold and holding upper body hold continues
## 398 lands R knee to outer L leg caught in R arm hold and holding upper body hold continues
## 399 caught in R arm hold and holding upper body hold continues
## 400 caught in R arm hold and holding upper body hold continues
## 401 caught in R arm hold and holding upper body hold continues
## 402 caught in R arm hold and holding upper body hold continues
## 403 caught in R arm hold and holding upper body hold continues
## 404 caught in R arm hold and holding upper body hold continues
## 405 lands R knee to inner R leg caught in R arm hold and holding upper body hold continues
## 406 caught in R arm hold and holding upper body hold continues
## 407 caught in R arm hold and holding upper body hold continues
## 408 caught in R arm hold and holding upper body hold continues
## 409 gets up holding upper body hold continues and caught in R arm hold
## 410 caught in R arm hold and holding upper body hold continues
## 411 caught in R arm hold and holding upper body hold continues
## 412 caught in R arm hold and holding upper body hold continues
## 413 caught in R arm hold and holding upper body hold continues
## 414 caught in R arm hold and holding upper body hold continues
## 415 caught in R arm hold and holding upper body hold continues
## 416 breaks R arm hold caught in L arm hold and holding upper body hold continues
## 417 caught in L arm hold and holding upper body hold continues
## 418 caught in L arm hold and holding upper body hold continues
## 419 caught in L arm hold and holding upper body hold continues
## 420 caught in L arm hold and holding upper body hold continues
## 421 caught in L arm hold and holding upper body hold continues
## 422 caught in L arm hold and holding upper body hold continues
## 423 caught in L arm hold and holding upper body hold continues
## 424 caught in L arm hold and holding upper body hold continues
## 425 breaks L arm hold and caught in R arm hold while holding upper body hold continues
## 426 caught in R arm hold and holding upper body hold continues
## 427 lands R knee to inner R leg and caught in R arm hold and holding upper body hold continues
## 428 lands L hook to head and caught in R arm hold and holding upper body hold continues
## 429 breaks R arm hold and loses upper body hold
## 430 <NA>
## 431 <NA>
## 432 <NA>
## 433 <NA>
## 434 misses L hook to face
## 435 misses R cross
## 436 <NA>
## 437 falls
## 438 falls
## 439 gets up
## 440 <NA>
## 441 misses L jab
## 442 misses flying elbow to face, misses R uppercut to face
## 443 <NA>
## 444 misses attempted clinch
## 445 misses R push kick
## 446 misses R jab
## 447 misses L jab
## 448 <NA>
## 449 <NA>
## 450 misses attempted takedown
## 451 misses L hook to face
## 452 <NA>
## 453 misses takedown
## 454 <NA>
## 455 misses attempted takedown
## 456 <NA>
## 457 <NA>
## 458 lands L flying elbow to face
## 459 lands R elbow to face
## 460 misses R cross
## 461 <NA>
## 462 misses R cross
## 463 misses attempted takedown
## 464 holding upper body hold starts
## 465 holding upper body hold continues
## 466 holding upper body hold continues
## 467 holding upper body hold continues
## 468 holding upper body hold continues and caught in R arm hold
## 469 holding upper body hold continues and caught in R arm hold
## 470 holding upper body hold continues and caught in R arm hold
## 471 holding upper body hold continues and caught in R arm hold
## 472 lands R knee to inner R leg and caught in R arm hold and holding upper body hold continues
## 473 holding upper body hold continues and caught in R arm hold
## 474 holding upper body hold continues and caught in R arm hold
## 475 holding upper body hold continues and caught in R arm hold
## 476 holding upper body hold continues and caught in R arm hold
## 477 holding upper body hold continues and caught in R arm hold
## 478 holding upper body hold continues and caught in R arm hold
## 479 misses backwards upward elbow to face while holding upper body hold continues and caught in R arm hold
## 480 holding upper body hold continues and caught in R arm hold
## 481 holding upper body hold continues and caught in R arm hold
## 482 holding upper body hold continues and caught in R arm hold
## 483 holding upper body hold continues and caught in R arm hold
## 484 lands R knee to inner R leg holding upper body hold continues and caught in R arm hold
## 485 misses single leg takedown attempt while holding upper body hold continues and caught in R arm hold
## 486 holding upper body hold continues and caught in R arm hold
## 487 holding upper body hold continues and caught in R arm hold
## 488 holding upper body hold continues and caught in R arm hold
## 489 holding upper body hold continues and caught in R arm hold
## 490 holding upper body hold continues and caught in R arm hold
## 491 holding upper body hold continues and caught in R arm hold
## 492 holding upper body hold continues and caught in R arm hold
## 493 holding upper body hold continues and caught in R arm hold
## 494 holding upper body hold continues and caught in R arm hold
## 495 holding upper body hold continues and caught in R arm hold
## 496 holding upper body hold continues and caught in R arm hold
## 497 holding upper body hold continues and caught in R arm hold
## 498 holding upper body hold continues and caught in R arm hold
## 499 holding upper body hold continues and caught in R arm hold
## 500 holding upper body hold continues and caught in R arm hold
## 501 holding upper body hold continues and caught in R arm hold
## 502 holding upper body hold continues and caught in R arm hold
## 503 holding upper body hold continues and caught in R arm hold
## 504 holding upper body hold continues and caught in R arm hold
## 505 holding upper body hold continues and caught in R arm hold
## 506 lands R knee to inner R leg holding upper body hold continues and caught in R arm hold
## 507 pulls off opponent's R hand blocking airway with L hand while holding upper body hold continues and caught in R arm hold
## 508 holding upper body hold continues and caught in R arm hold
## 509 holding upper body hold continues and caught in R arm hold
## 510 holding upper body hold continues and caught in R arm hold
## 511 holding upper body hold continues and caught in R arm hold
## 512 holding upper body hold continues and caught in R arm hold
## 513 holding upper body hold continues and caught in R arm hold
## 514 holding upper body hold continues and caught in R arm hold
## 515 holding upper body hold continues and caught in R arm hold
## 516 holding upper body hold continues and caught in R arm hold
## 517 holding upper body hold continues and caught in R arm hold
## 518 holding upper body hold continues and caught in R arm hold
## 519 holding upper body hold continues and caught in R arm hold
## 520 holding upper body hold continues and caught in R arm hold
## 521 holding upper body hold continues and caught in R arm hold
## 522 holding upper body hold continues and caught in R arm hold
## 523 holding upper body hold continues and caught in R arm hold
## 524 holding upper body hold continues and caught in R arm hold
## 525 holding upper body hold continues and caught in R arm hold
## 526 holding upper body hold continues and caught in R arm hold
## 527 holding upper body hold continues and caught in R arm hold
## 528 holding upper body hold continues and caught in R arm hold
## 529 holding upper body hold continues and caught in R arm hold
## FightersActionsReactions.X2
## 354 missed jab
## 355 missed jab, missed jab
## 356 misses R cross
## 357 misses L mt kick to body
## 358 misses L mt kick to body
## 359 <NA>
## 360 lands R cross to face
## 361 <NA>
## 362 missed R mt kick to head
## 363 missed L jab to face, missed R cross to face
## 364 <NA>
## 365 lands L hook to face
## 366 <NA>
## 367 missed L jab to face
## 368 misses R cross, lands L jab
## 369 misses R cross
## 370 <NA>
## 371 <NA>
## 372 misses R hook to head
## 373 holding R arm hold starts and caught in upper body hold lands R uppercut to face
## 374 holding R arm hold continues and caught in upper body hold
## 375 holding R arm hold continues and caught in upper body hold
## 376 holding R arm hold continues and caught in upper body hold
## 377 holding R arm hold continues and caught in upper body hold
## 378 holding R arm hold continues and caught in upper body hold
## 379 holding R arm hold continues and caught in upper body hold
## 380 holding R arm hold continues and caught in upper body hold
## 381 holding R arm hold continues and caught in upper body hold
## 382 holding R arm hold continues and caught in upper body hold
## 383 holding R arm hold continues and caught in upper body hold
## 384 holding R arm hold continues and caught in upper body hold
## 385 holding R arm hold continues and caught in upper body hold
## 386 holding R arm hold continues and caught in upper body hold
## 387 holding R arm hold continues and caught in upper body hold
## 388 holding R arm hold continues and caught in upper body hold
## 389 holding R arm hold continues and caught in upper body hold
## 390 holding R arm hold continues and caught in upper body hold
## 391 holding R arm hold continues and caught in upper body hold
## 392 holding R arm hold continues and caught in upper body hold
## 393 holding R arm hold continues and caught in upper body hold
## 394 lands R hook to head holding R arm hold continues and caught in upper body hold
## 395 holding R arm hold continues and caught in upper body hold
## 396 holding R arm hold continues and caught in upper body hold
## 397 holding R arm hold continues and caught in upper body hold
## 398 holding R arm hold continues and caught in upper body hold
## 399 holding R arm hold continues and caught in upper body hold
## 400 holding R arm hold continues and caught in upper body hold
## 401 holding R arm hold continues and caught in upper body hold
## 402 holding R arm hold continues and caught in upper body hold
## 403 holding R arm hold continues and caught in upper body hold
## 404 holding R arm hold continues and caught in upper body hold
## 405 holding R arm hold continues and caught in upper body hold
## 406 holding R arm hold continues and caught in upper body hold
## 407 lands R knee to L body holding R arm hold continues and caught in upper body hold
## 408 lands takedown holding R arm hold and caught in upper body hold
## 409 holding R arm hold continues and caught in upper body hold
## 410 holding R arm hold continues and caught in upper body hold
## 411 holding R arm hold continues and caught in upper body hold
## 412 lands L knee to body while holding R arm hold and caught in upper body hold
## 413 holding R arm hold continues and caught in upper body hold
## 414 holding R arm hold continues and caught in upper body hold
## 415 holding R arm hold continues and caught in upper body hold
## 416 loses R arm hold holding L arm starts
## 417 holding L arm hold continues and caught in upper body hold
## 418 holding L arm hold continues and caught in upper body hold
## 419 holding L arm hold continues and caught in upper body hold
## 420 holding L arm hold continues and caught in upper body hold
## 421 holding L arm hold continues and caught in upper body hold
## 422 holding L arm hold continues and caught in upper body hold
## 423 holding L arm hold continues and caught in upper body hold
## 424 holding L arm hold continues and caught in upper body hold
## 425 caught in upper body hold and loses L arm hold , holding R arm hold starts
## 426 holding R arm hold and caught in upper body hold
## 427 holding R arm hold and caught in upper body hold
## 428 holding R arm hold and caught in upper body hold
## 429 loses R arm hold and breaks upper body hold
## 430 lands R knee to body, lands L jab to face
## 431 lands R cross to face, lands L jab to face
## 432 lands R cross to face, lands L jab to face
## 433 lands L hook to head
## 434 <NA>
## 435 lands L jab, lands R cross, misses R cross
## 436 lands L mt kick to low L leg
## 437 <NA>
## 438 misses R cross, misses L cross
## 439 <NA>
## 440 lands R mt kick to leg
## 441 misses L jab
## 442 <NA>
## 443 misses L cross, misses R cross
## 444 <NA>
## 445 <NA>
## 446 <NA>
## 447 <NA>
## 448 misses L mt kick
## 449 misses L jab
## 450 <NA>
## 451 misses R mt kick to body
## 452 lands L jab to face
## 453 <NA>
## 454 misses L cross
## 455 lands R cross to face
## 456 lands L mt kick to inner low leg
## 457 misses R cross, misses L cross
## 458 <NA>
## 459 <NA>
## 460 misses L cross, misses R cross
## 461 lands R mt kick to leg
## 462 lands L cross
## 463 misses L cross
## 464 caught in upper body hold
## 465 caught in upper body hold
## 466 caught in upper body hold
## 467 caught in upper body hold
## 468 holding R arm hold starts and caught in upper body hold
## 469 misses L knee to body and holding R arm hold continues and caught in upper body hold
## 470 holding R arm hold continues and caught in upper body hold
## 471 holding R arm hold continues and caught in upper body hold
## 472 holding R arm hold continues and caught in upper body hold
## 473 holding R arm hold continues and caught in upper body hold
## 474 holding R arm hold continues and caught in upper body hold
## 475 holding R arm hold continues and caught in upper body hold
## 476 holding R arm hold continues and caught in upper body hold
## 477 holding R arm hold continues and caught in upper body hold
## 478 holding R arm hold continues and caught in upper body hold
## 479 holding R arm hold continues and caught in upper body hold
## 480 holding R arm hold continues and caught in upper body hold
## 481 holding R arm hold continues and caught in upper body hold
## 482 holding R arm hold continues and caught in upper body hold
## 483 holding R arm hold continues and caught in upper body hold
## 484 holding R arm hold continues and caught in upper body hold
## 485 holding R arm hold continues and caught in upper body hold
## 486 holding R arm hold continues and caught in upper body hold
## 487 holding R arm hold continues and caught in upper body hold
## 488 holding R arm hold continues and caught in upper body hold
## 489 holding R arm hold continues and caught in upper body hold
## 490 holding R arm hold continues and caught in upper body hold
## 491 holding R arm hold continues and caught in upper body hold
## 492 lands R knee to body holding R arm hold continues and caught in upper body hold
## 493 holding R arm hold continues and caught in upper body hold
## 494 holding R arm hold continues and caught in upper body hold
## 495 holding R arm hold continues and caught in upper body hold
## 496 holding R arm hold continues and caught in upper body hold
## 497 holding R arm hold continues and caught in upper body hold
## 498 holding R arm hold continues and caught in upper body hold
## 499 holding R arm hold continues and caught in upper body hold
## 500 holding R arm hold continues and caught in upper body hold
## 501 holding R arm hold continues and caught in upper body hold
## 502 blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold
## 503 blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold
## 504 blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold
## 505 blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold
## 506 blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold
## 507 blocks nose and mouth with R hand while holding R arm hold continues and caught in upper body hold
## 508 holding R arm hold continues and caught in upper body hold
## 509 holding R arm hold continues and caught in upper body hold
## 510 holding R arm hold continues and caught in upper body hold
## 511 holding R arm hold continues and caught in upper body hold
## 512 holding R arm hold continues and caught in upper body hold
## 513 holding R arm hold continues and caught in upper body hold
## 514 holding R arm hold continues and caught in upper body hold
## 515 holding R arm hold continues and caught in upper body hold
## 516 holding R arm hold continues and caught in upper body hold
## 517 holding R arm hold continues and caught in upper body hold
## 518 holding R arm hold continues and caught in upper body hold
## 519 holding R arm hold continues and caught in upper body hold
## 520 holding R arm hold continues and caught in upper body hold
## 521 holding R arm hold continues and caught in upper body hold
## 522 lands R knee to body holding R arm hold continues and caught in upper body hold
## 523 holding R arm hold continues and caught in upper body hold
## 524 holding R arm hold continues and caught in upper body hold
## 525 holding R arm hold continues and caught in upper body hold
## 526 holding R arm hold continues and caught in upper body hold
## 527 holding R arm hold continues and caught in upper body hold
## 528 holding R arm hold continues and caught in upper body hold
## 529 holding R arm hold continues and caught in upper body hold
## Notes Crossl.X1 Kneel.X1 Elbowl.X1 Hookl.X1 Jabl.X1 Kickl.X1 Crossl.X2
## 354 Cyborg 0 0 0 0 0 0 0
## 355 Cyborg 0 0 0 0 0 0 0
## 356 Cyborg 0 0 0 0 0 0 0
## 357 Cyborg 0 0 0 0 0 1 0
## 358 Cyborg 0 0 0 0 0 0 0
## 359 Cyborg 0 0 0 0 0 0 0
## 360 Cyborg 0 0 0 0 0 0 1
## 361 Cyborg 0 0 0 0 0 0 0
## 362 Cyborg 0 0 0 0 0 0 0
## 363 Cyborg 0 0 0 0 0 0 0
## 364 Cyborg 0 0 0 0 0 0 0
## 365 Cyborg 0 0 1 0 0 0 0
## 366 Cyborg 0 0 0 0 0 0 0
## 367 Cyborg 0 0 0 0 0 0 0
## 368 Cyborg 0 0 0 0 0 0 0
## 369 Cyborg 0 0 0 0 0 1 0
## 370 Cyborg 0 0 0 0 0 0 0
## 371 Cyborg 0 0 0 0 0 0 0
## 372 Cyborg 0 0 0 0 0 0 0
## 373 Cyborg 0 0 0 0 0 0 0
## 374 Cyborg 0 0 0 0 0 0 0
## 375 Cyborg 0 0 0 0 0 0 0
## 376 Cyborg 0 1 0 0 0 0 0
## 377 Cyborg 0 0 0 0 0 0 0
## 378 Cyborg 0 0 0 0 0 0 0
## 379 Cyborg 0 0 0 0 0 0 0
## 380 Cyborg 0 0 0 0 0 0 0
## 381 Cyborg 0 0 0 0 0 0 0
## 382 Cyborg 0 0 0 0 0 0 0
## 383 Cyborg 0 1 0 0 0 0 0
## 384 Cyborg 0 1 0 0 0 0 0
## 385 Cyborg 0 0 0 0 0 0 0
## 386 Cyborg 0 0 0 0 0 0 0
## 387 Cyborg 0 0 0 0 0 0 0
## 388 Cyborg 0 1 0 0 0 0 0
## 389 Cyborg 0 0 0 0 0 0 0
## 390 Cyborg 0 0 0 0 0 0 0
## 391 Cyborg 0 0 0 0 0 0 0
## 392 Cyborg 0 0 0 0 0 0 0
## 393 Cyborg 0 1 0 0 0 0 0
## 394 Cyborg 0 0 0 0 0 0 0
## 395 Cyborg 0 0 0 0 0 0 0
## 396 Cyborg 0 0 0 0 0 0 0
## 397 Cyborg 0 0 0 0 0 0 0
## 398 Cyborg 0 1 0 0 0 0 0
## 399 Cyborg 0 0 0 0 0 0 0
## 400 Cyborg 0 0 0 0 0 0 0
## 401 Cyborg 0 0 0 0 0 0 0
## 402 Cyborg 0 0 0 0 0 0 0
## 403 Cyborg 0 0 0 0 0 0 0
## 404 Cyborg 0 0 0 0 0 0 0
## 405 Cyborg 0 1 0 0 0 0 0
## 406 Cyborg 0 0 0 0 0 0 0
## 407 Cyborg 0 0 0 0 0 0 0
## 408 Cyborg 0 0 0 0 0 0 0
## 409 Cyborg 0 0 0 0 0 0 0
## 410 Cyborg 0 0 0 0 0 0 0
## 411 Cyborg 0 0 0 0 0 0 0
## 412 Cyborg 0 0 0 0 0 0 0
## 413 Cyborg 0 0 0 0 0 0 0
## 414 Cyborg 0 0 0 0 0 0 0
## 415 Cyborg 0 0 0 0 0 0 0
## 416 Cyborg 0 0 0 0 0 0 0
## 417 Cyborg 0 0 0 0 0 0 0
## 418 Cyborg 0 0 0 0 0 0 0
## 419 Cyborg 0 0 0 0 0 0 0
## 420 Cyborg 0 0 0 0 0 0 0
## 421 Cyborg 0 0 0 0 0 0 0
## 422 Cyborg 0 0 0 0 0 0 0
## 423 Cyborg 0 0 0 0 0 0 0
## 424 Cyborg 0 0 0 0 0 0 0
## 425 Cyborg 0 0 0 0 0 0 0
## 426 Cyborg 0 0 0 0 0 0 0
## 427 Cyborg 0 1 0 0 0 0 0
## 428 Cyborg 0 0 0 1 0 0 0
## 429 Cyborg 0 0 0 0 0 0 0
## 430 Cyborg 0 0 0 0 0 0 0
## 431 Cyborg 0 0 0 0 0 0 1
## 432 Cyborg 0 0 0 0 0 0 1
## 433 Cyborg 0 0 0 0 0 0 0
## 434 Cyborg 0 0 0 0 0 0 0
## 435 Cyborg 0 0 0 0 0 0 0
## 436 Cyborg 0 0 0 0 0 0 0
## 437 Cyborg 0 0 0 0 0 0 0
## 438 Cyborg 0 0 0 0 0 0 0
## 439 Cyborg 0 0 0 0 0 0 0
## 440 Cyborg 0 0 0 0 0 0 0
## 441 Cyborg 0 0 0 0 0 0 0
## 442 Cyborg 0 0 0 0 0 0 0
## 443 Cyborg 0 0 0 0 0 0 0
## 444 Cyborg 0 0 0 0 0 0 0
## 445 Cyborg 0 0 0 0 0 0 0
## 446 Cyborg 0 0 0 0 0 0 0
## 447 Cyborg 0 0 0 0 0 0 0
## 448 Cyborg 0 0 0 0 0 0 0
## 449 Cyborg 0 0 0 0 0 0 0
## 450 Cyborg 0 0 0 0 0 0 0
## 451 Cyborg 0 0 0 0 0 0 0
## 452 Cyborg 0 0 0 0 0 0 0
## 453 Cyborg 0 0 0 0 0 0 0
## 454 Cyborg 0 0 0 0 0 0 0
## 455 Cyborg 0 0 0 0 0 0 1
## 456 Cyborg 0 0 0 0 0 0 0
## 457 Cyborg 0 0 0 0 0 0 0
## 458 Cyborg 0 0 1 0 0 0 0
## 459 Cyborg 0 0 1 0 0 0 0
## 460 Cyborg 0 0 0 0 0 0 0
## 461 Cyborg 0 0 0 0 0 0 0
## 462 Cyborg 0 0 0 0 0 0 1
## 463 Cyborg 0 0 0 0 0 0 0
## 464 Cyborg 0 0 0 0 0 0 0
## 465 Cyborg 0 0 0 0 0 0 0
## 466 Cyborg 0 0 0 0 0 0 0
## 467 Cyborg 0 0 0 0 0 0 0
## 468 Cyborg 0 0 0 0 0 0 0
## 469 Cyborg 0 0 0 0 0 0 0
## 470 Cyborg 0 0 0 0 0 0 0
## 471 Cyborg 0 0 0 0 0 0 0
## 472 Cyborg 0 1 0 0 0 0 0
## 473 Cyborg 0 0 0 0 0 0 0
## 474 Cyborg 0 0 0 0 0 0 0
## 475 Cyborg 0 0 0 0 0 0 0
## 476 Cyborg 0 0 0 0 0 0 0
## 477 Cyborg 0 0 0 0 0 0 0
## 478 Cyborg 0 0 0 0 0 0 0
## 479 Cyborg 0 0 0 0 0 0 0
## 480 Cyborg 0 0 0 0 0 0 0
## 481 Cyborg 0 0 0 0 0 0 0
## 482 Cyborg 0 0 0 0 0 0 0
## 483 Cyborg 0 0 0 0 0 0 0
## 484 Cyborg 0 1 0 0 0 0 0
## 485 Cyborg 0 0 0 0 0 0 0
## 486 Cyborg 0 0 0 0 0 0 0
## 487 Cyborg 0 0 0 0 0 0 0
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## Hook3r.X1 Jab3r.X1 Kick3r.X1 Cross3r.X2 Knee3r.X2 Elbow3r.X2 Hook3r.X2
## 354 0 0 0 0 0 0 0
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## Jab3r.X2 Kick3r.X2 upper3r.X1 upper3r.X2 takedown3r.X1 takedown3r.X2
## 354 0 0 0 0 0 0
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## hammer3r.X1 hammer3r.X2
## 354 0 0
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## 529 0 0This next section creates the cumulative actions of hits landed,missed, or received for each second into the round for each of the three table splits by opponent, since only first round of various fights extracted and just created above.
Table4 <- mutate(Table1, cmTotHitsR.X1=cumsum(TotReceivedX1),
cmTotHitsL.X1=cumsum(TotLandsX1),
cmTotHitsM.X1=cumsum(TotMissedX1),
cmTotHitsR.X2=cumsum(TotReceivedX2),
cmTotHitsL.X2=cumsum(TotLandsX2),
cmTotHitsM.X2=cumsum(TotMissedX2))
Table5 <- mutate(Table2, cmTotHitsR.X1=cumsum(TotReceivedX1),
cmTotHitsL.X1=cumsum(TotLandsX1),
cmTotHitsM.X1=cumsum(TotMissedX1),
cmTotHitsR.X2=cumsum(TotReceivedX2),
cmTotHitsL.X2=cumsum(TotLandsX2),
cmTotHitsM.X2=cumsum(TotMissedX2))
Table6 <- mutate(Table3, cmTotHitsR.X1=cumsum(TotReceivedX1),
cmTotHitsL.X1=cumsum(TotLandsX1),
cmTotHitsM.X1=cumsum(TotMissedX1),
cmTotHitsR.X2=cumsum(TotReceivedX2),
cmTotHitsL.X2=cumsum(TotLandsX2),
cmTotHitsM.X2=cumsum(TotMissedX2))This combines the three table splits with cumulative sum of actions for each unique round or fighter.
Table7 <- rbind(Table4,Table5,Table6)
colnames(Table7)## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X1" "cmTotHitsL.X1"
## [7] "cmTotHitsM.X1" "TotLandsX1"
## [9] "TotMissedX1" "TotReceivedX1"
## [11] "cmTotHitsR.X2" "cmTotHitsL.X2"
## [13] "cmTotHitsM.X2" "TotLandsX2"
## [15] "TotMissedX2" "TotReceivedX2"
## [17] "Time" "FighterActionReactions.X1"
## [19] "FightersActionsReactions.X2" "Notes"
## [21] "Crossl.X1" "Kneel.X1"
## [23] "Elbowl.X1" "Hookl.X1"
## [25] "Jabl.X1" "Kickl.X1"
## [27] "Crossl.X2" "Kneel.X2"
## [29] "Elbowl.X2" "Hookl.X2"
## [31] "Jabl.X2" "Kickl.X2"
## [33] "upperl.X1" "upperl.X2"
## [35] "takedownl.X1" "takedownl.X2"
## [37] "hammerl.X1" "hammerl.X2"
## [39] "Cross2l.X1" "Knee2l.X1"
## [41] "Elbow2l.X1" "Hook2l.X1"
## [43] "Jab2l.X1" "Kick2l.X1"
## [45] "Cross2l.X2" "Knee2l.X2"
## [47] "Elbow2l.X2" "Hook2l.X2"
## [49] "Jab2l.X2" "Kick2l.X2"
## [51] "upper2l.X1" "upper2l.X2"
## [53] "takedown2l.X1" "takedown2l.X2"
## [55] "hammer2l.X1" "hammer2l.X2"
## [57] "Cross3l.X1" "Knee3l.X1"
## [59] "Elbow3l.X1" "Hook3l.X1"
## [61] "Jab3l.X1" "Kick3l.X1"
## [63] "Cross3l.X2" "Knee3l.X2"
## [65] "Elbow3l.X2" "Hook3l.X2"
## [67] "Jab3l.X2" "Kick3l.X2"
## [69] "upper3l.X1" "upper3l.X2"
## [71] "takedown3l.X1" "takedown3l.X2"
## [73] "hammer3l.X1" "hammer3l.X2"
## [75] "Crossm.X1" "Kneem.X1"
## [77] "Elbowm.X1" "Hookm.X1"
## [79] "Jabm.X1" "Kickm.X1"
## [81] "Crossm.X2" "Kneem.X2"
## [83] "Elbowm.X2" "Hookm.X2"
## [85] "Jabm.X2" "Kickm.X2"
## [87] "upperm.X1" "upperm.X2"
## [89] "takedownm.X1" "takedownm.X2"
## [91] "hammerm.X1" "hammerm.X2"
## [93] "Cross2m.X1" "Knee2m.X1"
## [95] "Elbow2m.X1" "Hook2m.X1"
## [97] "Jab2m.X1" "Kick2m.X1"
## [99] "Cross2m.X2" "Knee2m.X2"
## [101] "Elbow2m.X2" "Hook2m.X2"
## [103] "Jab2m.X2" "Kick2m.X2"
## [105] "upper2m.X1" "upper2m.X2"
## [107] "takedown2m.X1" "takedown2m.X2"
## [109] "hammer2m.X1" "hammer2m.X2"
## [111] "Cross3m.X1" "Knee3m.X1"
## [113] "Elbow3m.X1" "Hook3m.X1"
## [115] "Jab3m.X1" "Kick3m.X1"
## [117] "Cross3m.X2" "Knee3m.X2"
## [119] "Elbow3m.X2" "Hook3m.X2"
## [121] "Jab3m.X2" "Kick3m.X2"
## [123] "upper3m.X1" "upper3m.X2"
## [125] "takedown3m.X1" "takedown3m.X2"
## [127] "hammer3m.X1" "hammer3m.X2"
## [129] "Crossr.X1" "Kneer.X1"
## [131] "Elbowr.X1" "Hookr.X1"
## [133] "Jabr.X1" "Kickr.X1"
## [135] "Crossr.X2" "Kneer.X2"
## [137] "Elbowr.X2" "Hookr.X2"
## [139] "Jabr.X2" "Kickr.X2"
## [141] "upperr.X1" "upperr.X2"
## [143] "takedownr.X1" "takedownr.X2"
## [145] "hammerr.X1" "hammerr.X2"
## [147] "Cross2r.X1" "Knee2r.X1"
## [149] "Elbow2r.X1" "Hook2r.X1"
## [151] "Jab2r.X1" "Kick2r.X1"
## [153] "Cross2r.X2" "Knee2r.X2"
## [155] "Elbow2r.X2" "Hook2r.X2"
## [157] "Jab2r.X2" "Kick2r.X2"
## [159] "upper2r.X1" "upper2r.X2"
## [161] "takedown2r.X1" "takedown2r.X2"
## [163] "hammer2r.X1" "hammer2r.X2"
## [165] "Cross3r.X1" "Knee3r.X1"
## [167] "Elbow3r.X1" "Hook3r.X1"
## [169] "Jab3r.X1" "Kick3r.X1"
## [171] "Cross3r.X2" "Knee3r.X2"
## [173] "Elbow3r.X2" "Hook3r.X2"
## [175] "Jab3r.X2" "Kick3r.X2"
## [177] "upper3r.X1" "upper3r.X2"
## [179] "takedown3r.X1" "takedown3r.X2"
## [181] "hammer3r.X1" "hammer3r.X2"The following rearranges the table columns by actions landed, missed, and received into a new table.
landX1 <- c(21:26,33,35,37,39:44,51,53,55,57:62,69,71,73)
landX2 <- c(27:32,34,36,38,45:50,52,54,56,63:68,70,72,74)
missX1 <- c(75:80,87,89,91,93:98,105,107,109,111:116,123,125,127)
missX2 <- c(81:86,88,90,92,99:104,106,108,110,117:122,124,126,128)
recvX1 <- c(129:134,141,143,145,147:152,159,161,163,165:170,177,179,181)
recvX2 <- c(135:140,142,144,146,153:158,160,162,164,171:176,178,180,182)
Table8 <- Table7[,c(1:20,landX1,landX2,missX1,missX2,recvX1,recvX2)]
colnames(Table8)## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X1" "cmTotHitsL.X1"
## [7] "cmTotHitsM.X1" "TotLandsX1"
## [9] "TotMissedX1" "TotReceivedX1"
## [11] "cmTotHitsR.X2" "cmTotHitsL.X2"
## [13] "cmTotHitsM.X2" "TotLandsX2"
## [15] "TotMissedX2" "TotReceivedX2"
## [17] "Time" "FighterActionReactions.X1"
## [19] "FightersActionsReactions.X2" "Notes"
## [21] "Crossl.X1" "Kneel.X1"
## [23] "Elbowl.X1" "Hookl.X1"
## [25] "Jabl.X1" "Kickl.X1"
## [27] "upperl.X1" "takedownl.X1"
## [29] "hammerl.X1" "Cross2l.X1"
## [31] "Knee2l.X1" "Elbow2l.X1"
## [33] "Hook2l.X1" "Jab2l.X1"
## [35] "Kick2l.X1" "upper2l.X1"
## [37] "takedown2l.X1" "hammer2l.X1"
## [39] "Cross3l.X1" "Knee3l.X1"
## [41] "Elbow3l.X1" "Hook3l.X1"
## [43] "Jab3l.X1" "Kick3l.X1"
## [45] "upper3l.X1" "takedown3l.X1"
## [47] "hammer3l.X1" "Crossl.X2"
## [49] "Kneel.X2" "Elbowl.X2"
## [51] "Hookl.X2" "Jabl.X2"
## [53] "Kickl.X2" "upperl.X2"
## [55] "takedownl.X2" "hammerl.X2"
## [57] "Cross2l.X2" "Knee2l.X2"
## [59] "Elbow2l.X2" "Hook2l.X2"
## [61] "Jab2l.X2" "Kick2l.X2"
## [63] "upper2l.X2" "takedown2l.X2"
## [65] "hammer2l.X2" "Cross3l.X2"
## [67] "Knee3l.X2" "Elbow3l.X2"
## [69] "Hook3l.X2" "Jab3l.X2"
## [71] "Kick3l.X2" "upper3l.X2"
## [73] "takedown3l.X2" "hammer3l.X2"
## [75] "Crossm.X1" "Kneem.X1"
## [77] "Elbowm.X1" "Hookm.X1"
## [79] "Jabm.X1" "Kickm.X1"
## [81] "upperm.X1" "takedownm.X1"
## [83] "hammerm.X1" "Cross2m.X1"
## [85] "Knee2m.X1" "Elbow2m.X1"
## [87] "Hook2m.X1" "Jab2m.X1"
## [89] "Kick2m.X1" "upper2m.X1"
## [91] "takedown2m.X1" "hammer2m.X1"
## [93] "Cross3m.X1" "Knee3m.X1"
## [95] "Elbow3m.X1" "Hook3m.X1"
## [97] "Jab3m.X1" "Kick3m.X1"
## [99] "upper3m.X1" "takedown3m.X1"
## [101] "hammer3m.X1" "Crossm.X2"
## [103] "Kneem.X2" "Elbowm.X2"
## [105] "Hookm.X2" "Jabm.X2"
## [107] "Kickm.X2" "upperm.X2"
## [109] "takedownm.X2" "hammerm.X2"
## [111] "Cross2m.X2" "Knee2m.X2"
## [113] "Elbow2m.X2" "Hook2m.X2"
## [115] "Jab2m.X2" "Kick2m.X2"
## [117] "upper2m.X2" "takedown2m.X2"
## [119] "hammer2m.X2" "Cross3m.X2"
## [121] "Knee3m.X2" "Elbow3m.X2"
## [123] "Hook3m.X2" "Jab3m.X2"
## [125] "Kick3m.X2" "upper3m.X2"
## [127] "takedown3m.X2" "hammer3m.X2"
## [129] "Crossr.X1" "Kneer.X1"
## [131] "Elbowr.X1" "Hookr.X1"
## [133] "Jabr.X1" "Kickr.X1"
## [135] "upperr.X1" "takedownr.X1"
## [137] "hammerr.X1" "Cross2r.X1"
## [139] "Knee2r.X1" "Elbow2r.X1"
## [141] "Hook2r.X1" "Jab2r.X1"
## [143] "Kick2r.X1" "upper2r.X1"
## [145] "takedown2r.X1" "hammer2r.X1"
## [147] "Cross3r.X1" "Knee3r.X1"
## [149] "Elbow3r.X1" "Hook3r.X1"
## [151] "Jab3r.X1" "Kick3r.X1"
## [153] "upper3r.X1" "takedown3r.X1"
## [155] "hammer3r.X1" "Crossr.X2"
## [157] "Kneer.X2" "Elbowr.X2"
## [159] "Hookr.X2" "Jabr.X2"
## [161] "Kickr.X2" "upperr.X2"
## [163] "takedownr.X2" "hammerr.X2"
## [165] "Cross2r.X2" "Knee2r.X2"
## [167] "Elbow2r.X2" "Hook2r.X2"
## [169] "Jab2r.X2" "Kick2r.X2"
## [171] "upper2r.X2" "takedown2r.X2"
## [173] "hammer2r.X2" "Cross3r.X2"
## [175] "Knee3r.X2" "Elbow3r.X2"
## [177] "Hook3r.X2" "Jab3r.X2"
## [179] "Kick3r.X2" "upper3r.X2"
## [181] "takedown3r.X2" "hammer3r.X2"We should now add in the wrestling holds,caught in, breaks hold, and lost hold features for each fighter of X1 or X2 in each instance, not sequence. We already made these fields at the beginning of this script. Lets also add in the muay thai and push kicks for each sequence noting that these are already counted in the ‘kicks’ feature. We will just count them all as attempted action or reaction regardless if it landed or missed opponent. The target was blocked if not landed or ducked. We can account for these changes later in a different script if needed or you could do it yourself and not be critical of my work donations. Aside: mutalate misogynist monsters as a stepping stone instead of small creatures as a message to anybody who gets this nerdy break-down of violent and graphic content. Leave the squirrels, bunnys, guneau pigs, and females alone.
The ‘breaks’,‘caught’,‘hold’, and ‘lost’ are for X1, and the ‘breaksX2’,‘holdX2’,‘caughtX2’, and ‘lostX2’ are for X2 ground or submit type wrestling actions or reactions. For the kicks, ‘mtKicks’ and ‘pushKicks’ are for X1 and ‘mtKicksX2’ and ‘pushKicksX2’ are for X2 actions or reactions. Those will be taken from only the 1st sequence for this fighter because none of the 2nd and 3rd sequences had any push or muay thai kicks from either fighter. The lists to pull from are sq1 for X1 and sq1b for X2.
Lets make a new table from Table8.
Table9 <- Table8Create the placeholders for each action feature added.
Table9$holdingX1 <- 0
Table9$holdingX2 <- 0
Table9$breaksHoldX1 <- 0
Table9$breaksHoldX2 <- 0
Table9$caughtHoldX1 <- 0
Table9$caughtHoldX2 <- 0
Table9$lostHoldX1 <- 0
Table9$lostHoldX2 <- 0 colnames(Table9)[183:190]## [1] "holdingX1" "holdingX2" "breaksHoldX1" "breaksHoldX2" "caughtHoldX1"
## [6] "caughtHoldX2" "lostHoldX1" "lostHoldX2"Table9[hold,"holdingX1"] <- 1
Table9[holdX2,"holdingX2"] <- 1
Table9[breaks,"breaksHoldX1"] <- 1
Table9[breaksX2,"breaksHoldX2"] <- 1
Table9[caught,"caughtHoldX1"] <- 1
Table9[caughtX2,"caughtHoldX2"] <- 1
Table9[lost,"lostHoldX1"] <- 1
Table9[lostX2,"lostHoldX2"] <- 1 Now add in the muay thai and push kicks to this table.
Table9$muayThaiKickX1 <- 0
Table9$muayThaiKickX2 <- 0
Table9$pushKickX1 <- 0
Table9$pushKickX2 <- 0Table9[mtKicks,"muayThaiKickX1"] <- 1
Table9[mtKicksX2,"muayThaiKickX2"] <- 1
Table9[pushKicks,"pushKickX1"] <- 1
Table9[pushKicksX2,"pushKickX2"] <- 1We should also add in the cumulative count of each instance of holding, lost holds, broken holds, and instances caught in a hold as well as the cumulative counts of muay thai and push kicks attempted by each fighter. Since X2 has three different fighters, we need to split the Table 9 into a table of each opponent as we did earlier, get the cumulative counts, then recombine the table splits.
break_1 <- Table9$lastAction > Table9$SecondsIntoRound
break_1## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSEbreaks_3 <- row.names(Table9)[break_1]
breaks_3## [1] "170" "354"breaks_split <- as.numeric(paste(breaks_3))
breaks_split## [1] 170 354We had these values already stored in bk2, but the above was a refresher on how we obtained the X2 fighter splits.
Create the three tables of the separate fighters of X2.
table1_a <- Table9[1:(breaks_split[1]-1),]
table1_b <- Table9[breaks_split[1]:(breaks_split[2]-1),]
table1_c <- Table9[breaks_split[2]:length(Table9$Round),]
dim(table1_a)[1]+dim(table1_b)[1]+dim(table1_c)[1]==dim(Table9)[1]## [1] TRUEAll observations are accounted for as the number of observations in all three subset tables of Table 9 add up to the total observations in Table 9.
Now lets create those cumulative fields for each table. The cumulative sum of holds and caught holds is for seconds in a hold as caught or holding a hold, while the cumulative sums of the broken holds and lost holds are for number of times in that round and this fighter the hold was broken if caught in a hold or lost if holding the hold.
The first fighter as X2 table.
table1_a$totalHoldsX1 <- cumsum(table1_a$holdingX1)
table1_a$totalHoldsX2 <- cumsum(table1_a$holdingX2)
table1_a$totalLostHoldsX1 <- cumsum(table1_a$lostHoldX1)
table1_a$totalLostHoldsX2 <- cumsum(table1_a$lostHoldX2)
table1_a$totalCaughtHoldsX1 <- cumsum(table1_a$caughtHoldX1)
table1_a$totalCaughtHoldsX2 <- cumsum(table1_a$caughtHoldX2)
table1_a$totalBreakOutHoldsX1 <- cumsum(table1_a$breaksHoldX1)
table1_a$totalBreakOutHoldsX2 <- cumsum(table1_a$breaksHoldX2)
table1_a$totalMuayThaiKicksX1 <- cumsum(table1_a$muayThaiKickX1)
table1_a$totalMuayThaiKicksX2 <- cumsum(table1_a$muayThaiKickX2)
table1_a$totalPushKicksX1 <- cumsum(table1_a$pushKickX1)
table1_a$totalPushKicksX2 <- cumsum(table1_a$pushKickX2)
tail(table1_a[,195:206],10)## totalHoldsX1 totalHoldsX2 totalLostHoldsX1 totalLostHoldsX2
## 160 132 73 5 6
## 161 133 73 5 6
## 162 134 73 5 6
## 163 135 73 5 6
## 164 136 73 5 6
## 165 137 73 5 6
## 166 138 73 5 6
## 167 139 73 5 6
## 168 140 73 5 6
## 169 140 73 5 6
## totalCaughtHoldsX1 totalCaughtHoldsX2 totalBreakOutHoldsX1
## 160 73 133 6
## 161 73 134 6
## 162 73 135 6
## 163 73 136 6
## 164 73 137 6
## 165 73 138 6
## 166 73 139 6
## 167 73 140 6
## 168 73 141 6
## 169 73 142 6
## totalBreakOutHoldsX2 totalMuayThaiKicksX1 totalMuayThaiKicksX2
## 160 4 3 0
## 161 4 3 0
## 162 4 3 0
## 163 4 3 0
## 164 4 3 0
## 165 4 3 0
## 166 4 3 0
## 167 4 3 0
## 168 4 3 0
## 169 4 3 0
## totalPushKicksX1 totalPushKicksX2
## 160 1 0
## 161 1 0
## 162 1 0
## 163 1 0
## 164 1 0
## 165 1 0
## 166 1 0
## 167 1 0
## 168 1 0
## 169 1 0The 2nd fighter as x2 table.
table1_b$totalHoldsX1 <- cumsum(table1_b$holdingX1)
table1_b$totalHoldsX2 <- cumsum(table1_b$holdingX2)
table1_b$totalLostHoldsX1 <- cumsum(table1_b$lostHoldX1)
table1_b$totalLostHoldsX2 <- cumsum(table1_b$lostHoldX2)
table1_b$totalCaughtHoldsX1 <- cumsum(table1_b$caughtHoldX1)
table1_b$totalCaughtHoldsX2 <- cumsum(table1_b$caughtHoldX2)
table1_b$totalBreakOutHoldsX1 <- cumsum(table1_b$breaksHoldX1)
table1_b$totalBreakOutHoldsX2 <- cumsum(table1_b$breaksHoldX2)
table1_b$totalMuayThaiKicksX1 <- cumsum(table1_b$muayThaiKickX1)
table1_b$totalMuayThaiKicksX2 <- cumsum(table1_b$muayThaiKickX2)
table1_b$totalPushKicksX1 <- cumsum(table1_b$pushKickX1)
table1_b$totalPushKicksX2 <- cumsum(table1_b$pushKickX2)
tail(table1_b[,195:206],10)## totalHoldsX1 totalHoldsX2 totalLostHoldsX1 totalLostHoldsX2
## 344 159 0 6 1
## 345 160 0 6 1
## 346 161 0 6 1
## 347 162 0 7 1
## 348 163 0 7 1
## 349 164 0 7 1
## 350 165 0 7 1
## 351 166 0 7 1
## 352 167 0 7 1
## 353 167 0 7 1
## totalCaughtHoldsX1 totalCaughtHoldsX2 totalBreakOutHoldsX1
## 344 45 164 1
## 345 45 165 1
## 346 45 166 1
## 347 45 167 1
## 348 45 168 1
## 349 45 169 1
## 350 45 170 1
## 351 45 171 1
## 352 45 172 1
## 353 45 172 1
## totalBreakOutHoldsX2 totalMuayThaiKicksX1 totalMuayThaiKicksX2
## 344 10 2 0
## 345 10 2 0
## 346 10 2 0
## 347 11 2 0
## 348 11 2 0
## 349 11 2 0
## 350 11 2 0
## 351 11 2 0
## 352 11 2 0
## 353 11 2 0
## totalPushKicksX1 totalPushKicksX2
## 344 3 0
## 345 3 0
## 346 3 0
## 347 3 0
## 348 3 0
## 349 3 0
## 350 3 0
## 351 3 0
## 352 3 0
## 353 3 0The 3rd fighter as X2 table.
table1_c$totalHoldsX1 <- cumsum(table1_c$holdingX1)
table1_c$totalHoldsX2 <- cumsum(table1_c$holdingX2)
table1_c$totalLostHoldsX1 <- cumsum(table1_c$lostHoldX1)
table1_c$totalLostHoldsX2 <- cumsum(table1_c$lostHoldX2)
table1_c$totalCaughtHoldsX1 <- cumsum(table1_c$caughtHoldX1)
table1_c$totalCaughtHoldsX2 <- cumsum(table1_c$caughtHoldX2)
table1_c$totalBreakOutHoldsX1 <- cumsum(table1_c$breaksHoldX1)
table1_c$totalBreakOutHoldsX2 <- cumsum(table1_c$breaksHoldX2)
table1_c$totalMuayThaiKicksX1 <- cumsum(table1_c$muayThaiKickX1)
table1_c$totalMuayThaiKicksX2 <- cumsum(table1_c$muayThaiKickX2)
table1_c$totalPushKicksX1 <- cumsum(table1_c$pushKickX1)
table1_c$totalPushKicksX2 <- cumsum(table1_c$pushKickX2)
tail(table1_c[,195:206],10)## totalHoldsX1 totalHoldsX2 totalLostHoldsX1 totalLostHoldsX2
## 520 113 107 1 3
## 521 114 108 1 3
## 522 115 109 1 3
## 523 116 110 1 3
## 524 117 111 1 3
## 525 118 112 1 3
## 526 119 113 1 3
## 527 120 114 1 3
## 528 121 115 1 3
## 529 122 116 1 3
## totalCaughtHoldsX1 totalCaughtHoldsX2 totalBreakOutHoldsX1
## 520 109 112 3
## 521 110 113 3
## 522 111 114 3
## 523 112 115 3
## 524 113 116 3
## 525 114 117 3
## 526 115 118 3
## 527 116 119 3
## 528 117 120 3
## 529 118 121 3
## totalBreakOutHoldsX2 totalMuayThaiKicksX1 totalMuayThaiKicksX2
## 520 1 1 9
## 521 1 1 9
## 522 1 1 9
## 523 1 1 9
## 524 1 1 9
## 525 1 1 9
## 526 1 1 9
## 527 1 1 9
## 528 1 1 9
## 529 1 1 9
## totalPushKicksX1 totalPushKicksX2
## 520 2 0
## 521 2 0
## 522 2 0
## 523 2 0
## 524 2 0
## 525 2 0
## 526 2 0
## 527 2 0
## 528 2 0
## 529 2 0Now we need to create a new table of these combined tables of cumulative sums.
Table10 <- rbind(table1_a,table1_b,table1_c)
dim(Table10)## [1] 529 206dim(Table9)## [1] 529 194head(Table10)## Round SecondsIntoRound lastAction SecondsLastRoundAction cmTotHitsR.X1
## 1 1 4 0 4 0
## 2 1 5 4 1 0
## 3 1 9 5 4 0
## 4 1 11 9 2 0
## 5 1 13 11 2 0
## 6 1 19 13 6 0
## cmTotHitsL.X1 cmTotHitsM.X1 TotLandsX1 TotMissedX1 TotReceivedX1
## 1 0 1 0 1 0
## 2 0 1 0 0 0
## 3 0 1 0 0 0
## 4 0 3 0 2 0
## 5 0 4 0 1 0
## 6 0 5 0 1 0
## cmTotHitsR.X2 cmTotHitsL.X2 cmTotHitsM.X2 TotLandsX2 TotMissedX2
## 1 0 0 0 0 0
## 2 0 0 1 0 1
## 3 0 0 2 0 1
## 4 0 0 2 0 0
## 5 0 0 2 0 0
## 6 0 0 3 0 1
## TotReceivedX2 Time FighterActionReactions.X1 FightersActionsReactions.X2
## 1 0 4:55 missed L jab <NA>
## 2 0 4:54 <NA> missed R cross
## 3 0 4:50 <NA> missed R cross
## 4 0 4:48 missed R cross, missed L jab <NA>
## 5 0 4:46 missed L jab <NA>
## 6 0 4:40 missed R cross missed L cross
## Notes Crossl.X1 Kneel.X1 Elbowl.X1 Hookl.X1 Jabl.X1 Kickl.X1 upperl.X1
## 1 Zarah 0 0 0 0 0 0 0
## 2 Zarah 0 0 0 0 0 0 0
## 3 Zarah 0 0 0 0 0 0 0
## 4 Zarah 0 0 0 0 0 0 0
## 5 Zarah 0 0 0 0 0 0 0
## 6 Zarah 0 0 0 0 0 0 0
## takedownl.X1 hammerl.X1 Cross2l.X1 Knee2l.X1 Elbow2l.X1 Hook2l.X1 Jab2l.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## Kick2l.X1 upper2l.X1 takedown2l.X1 hammer2l.X1 Cross3l.X1 Knee3l.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Elbow3l.X1 Hook3l.X1 Jab3l.X1 Kick3l.X1 upper3l.X1 takedown3l.X1 hammer3l.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## Crossl.X2 Kneel.X2 Elbowl.X2 Hookl.X2 Jabl.X2 Kickl.X2 upperl.X2 takedownl.X2
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## hammerl.X2 Cross2l.X2 Knee2l.X2 Elbow2l.X2 Hook2l.X2 Jab2l.X2 Kick2l.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## upper2l.X2 takedown2l.X2 hammer2l.X2 Cross3l.X2 Knee3l.X2 Elbow3l.X2
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Hook3l.X2 Jab3l.X2 Kick3l.X2 upper3l.X2 takedown3l.X2 hammer3l.X2 Crossm.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 1
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 1
## Kneem.X1 Elbowm.X1 Hookm.X1 Jabm.X1 Kickm.X1 upperm.X1 takedownm.X1
## 1 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 1 0 0 0
## 6 0 0 0 0 0 0 0
## hammerm.X1 Cross2m.X1 Knee2m.X1 Elbow2m.X1 Hook2m.X1 Jab2m.X1 Kick2m.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 1 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## upper2m.X1 takedown2m.X1 hammer2m.X1 Cross3m.X1 Knee3m.X1 Elbow3m.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Hook3m.X1 Jab3m.X1 Kick3m.X1 upper3m.X1 takedown3m.X1 hammer3m.X1 Crossm.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 1
## 3 0 0 0 0 0 0 1
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 1
## Kneem.X2 Elbowm.X2 Hookm.X2 Jabm.X2 Kickm.X2 upperm.X2 takedownm.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## hammerm.X2 Cross2m.X2 Knee2m.X2 Elbow2m.X2 Hook2m.X2 Jab2m.X2 Kick2m.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## upper2m.X2 takedown2m.X2 hammer2m.X2 Cross3m.X2 Knee3m.X2 Elbow3m.X2
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Hook3m.X2 Jab3m.X2 Kick3m.X2 upper3m.X2 takedown3m.X2 hammer3m.X2 Crossr.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## Kneer.X1 Elbowr.X1 Hookr.X1 Jabr.X1 Kickr.X1 upperr.X1 takedownr.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## hammerr.X1 Cross2r.X1 Knee2r.X1 Elbow2r.X1 Hook2r.X1 Jab2r.X1 Kick2r.X1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## upper2r.X1 takedown2r.X1 hammer2r.X1 Cross3r.X1 Knee3r.X1 Elbow3r.X1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Hook3r.X1 Jab3r.X1 Kick3r.X1 upper3r.X1 takedown3r.X1 hammer3r.X1 Crossr.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## Kneer.X2 Elbowr.X2 Hookr.X2 Jabr.X2 Kickr.X2 upperr.X2 takedownr.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## hammerr.X2 Cross2r.X2 Knee2r.X2 Elbow2r.X2 Hook2r.X2 Jab2r.X2 Kick2r.X2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## upper2r.X2 takedown2r.X2 hammer2r.X2 Cross3r.X2 Knee3r.X2 Elbow3r.X2
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Hook3r.X2 Jab3r.X2 Kick3r.X2 upper3r.X2 takedown3r.X2 hammer3r.X2 holdingX1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## holdingX2 breaksHoldX1 breaksHoldX2 caughtHoldX1 caughtHoldX2 lostHoldX1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## lostHoldX2 muayThaiKickX1 muayThaiKickX2 pushKickX1 pushKickX2 totalHoldsX1
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## totalHoldsX2 totalLostHoldsX1 totalLostHoldsX2 totalCaughtHoldsX1
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## totalCaughtHoldsX2 totalBreakOutHoldsX1 totalBreakOutHoldsX2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## totalMuayThaiKicksX1 totalMuayThaiKicksX2 totalPushKicksX1 totalPushKicksX2
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0Write this table of added action/reaction features to csv file.
write.csv(Table10, 'Felicia3Fights_addedFeatures.csv', row.names=F)So far, we have a great table of actions, reactions, and cumulative totals for all actions, for attempted muay thai kicks, attempted push kicks, cumulative seconds of holds and being caught in a hold for each fighter, and cumulative counts of holds broken or lost for each fighter in the first round of their fight with Felicia as either Zarah, Megan Anderson, or Cyborg. Next we will select some or all features to target the outcome as a hit landed for that instant as predicted with the features selected. We will see how far our machine learning can go as far as predicting if the main outcome for X1 is a hit landed based on all given features or a select few using a model built on the training subset of this data to test on the remaining samples in accuracy of prediction.
This section will test out these features in part to predict the target of X1 landing a hit or missing a hit. We have to exclude the features where X2 received a hit if the instance shows a hit landed, so the model learns well. Random forest, recursive partitioned trees, generalized linear models, knn, and gradient boosted models within the R packages of Caret will be used to see how well these features predict the accuracy in a hit landed. Note that we do have all wrestling features for this data set.
library(caret)## Loading required package: lattice## Loading required package: ggplot2Make a table to predict the hit landed for X1 using all features except for the time, notes, X1 landed, X1 missed, X1 received, and X2 received features.
colnames(Table10)## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X1" "cmTotHitsL.X1"
## [7] "cmTotHitsM.X1" "TotLandsX1"
## [9] "TotMissedX1" "TotReceivedX1"
## [11] "cmTotHitsR.X2" "cmTotHitsL.X2"
## [13] "cmTotHitsM.X2" "TotLandsX2"
## [15] "TotMissedX2" "TotReceivedX2"
## [17] "Time" "FighterActionReactions.X1"
## [19] "FightersActionsReactions.X2" "Notes"
## [21] "Crossl.X1" "Kneel.X1"
## [23] "Elbowl.X1" "Hookl.X1"
## [25] "Jabl.X1" "Kickl.X1"
## [27] "upperl.X1" "takedownl.X1"
## [29] "hammerl.X1" "Cross2l.X1"
## [31] "Knee2l.X1" "Elbow2l.X1"
## [33] "Hook2l.X1" "Jab2l.X1"
## [35] "Kick2l.X1" "upper2l.X1"
## [37] "takedown2l.X1" "hammer2l.X1"
## [39] "Cross3l.X1" "Knee3l.X1"
## [41] "Elbow3l.X1" "Hook3l.X1"
## [43] "Jab3l.X1" "Kick3l.X1"
## [45] "upper3l.X1" "takedown3l.X1"
## [47] "hammer3l.X1" "Crossl.X2"
## [49] "Kneel.X2" "Elbowl.X2"
## [51] "Hookl.X2" "Jabl.X2"
## [53] "Kickl.X2" "upperl.X2"
## [55] "takedownl.X2" "hammerl.X2"
## [57] "Cross2l.X2" "Knee2l.X2"
## [59] "Elbow2l.X2" "Hook2l.X2"
## [61] "Jab2l.X2" "Kick2l.X2"
## [63] "upper2l.X2" "takedown2l.X2"
## [65] "hammer2l.X2" "Cross3l.X2"
## [67] "Knee3l.X2" "Elbow3l.X2"
## [69] "Hook3l.X2" "Jab3l.X2"
## [71] "Kick3l.X2" "upper3l.X2"
## [73] "takedown3l.X2" "hammer3l.X2"
## [75] "Crossm.X1" "Kneem.X1"
## [77] "Elbowm.X1" "Hookm.X1"
## [79] "Jabm.X1" "Kickm.X1"
## [81] "upperm.X1" "takedownm.X1"
## [83] "hammerm.X1" "Cross2m.X1"
## [85] "Knee2m.X1" "Elbow2m.X1"
## [87] "Hook2m.X1" "Jab2m.X1"
## [89] "Kick2m.X1" "upper2m.X1"
## [91] "takedown2m.X1" "hammer2m.X1"
## [93] "Cross3m.X1" "Knee3m.X1"
## [95] "Elbow3m.X1" "Hook3m.X1"
## [97] "Jab3m.X1" "Kick3m.X1"
## [99] "upper3m.X1" "takedown3m.X1"
## [101] "hammer3m.X1" "Crossm.X2"
## [103] "Kneem.X2" "Elbowm.X2"
## [105] "Hookm.X2" "Jabm.X2"
## [107] "Kickm.X2" "upperm.X2"
## [109] "takedownm.X2" "hammerm.X2"
## [111] "Cross2m.X2" "Knee2m.X2"
## [113] "Elbow2m.X2" "Hook2m.X2"
## [115] "Jab2m.X2" "Kick2m.X2"
## [117] "upper2m.X2" "takedown2m.X2"
## [119] "hammer2m.X2" "Cross3m.X2"
## [121] "Knee3m.X2" "Elbow3m.X2"
## [123] "Hook3m.X2" "Jab3m.X2"
## [125] "Kick3m.X2" "upper3m.X2"
## [127] "takedown3m.X2" "hammer3m.X2"
## [129] "Crossr.X1" "Kneer.X1"
## [131] "Elbowr.X1" "Hookr.X1"
## [133] "Jabr.X1" "Kickr.X1"
## [135] "upperr.X1" "takedownr.X1"
## [137] "hammerr.X1" "Cross2r.X1"
## [139] "Knee2r.X1" "Elbow2r.X1"
## [141] "Hook2r.X1" "Jab2r.X1"
## [143] "Kick2r.X1" "upper2r.X1"
## [145] "takedown2r.X1" "hammer2r.X1"
## [147] "Cross3r.X1" "Knee3r.X1"
## [149] "Elbow3r.X1" "Hook3r.X1"
## [151] "Jab3r.X1" "Kick3r.X1"
## [153] "upper3r.X1" "takedown3r.X1"
## [155] "hammer3r.X1" "Crossr.X2"
## [157] "Kneer.X2" "Elbowr.X2"
## [159] "Hookr.X2" "Jabr.X2"
## [161] "Kickr.X2" "upperr.X2"
## [163] "takedownr.X2" "hammerr.X2"
## [165] "Cross2r.X2" "Knee2r.X2"
## [167] "Elbow2r.X2" "Hook2r.X2"
## [169] "Jab2r.X2" "Kick2r.X2"
## [171] "upper2r.X2" "takedown2r.X2"
## [173] "hammer2r.X2" "Cross3r.X2"
## [175] "Knee3r.X2" "Elbow3r.X2"
## [177] "Hook3r.X2" "Jab3r.X2"
## [179] "Kick3r.X2" "upper3r.X2"
## [181] "takedown3r.X2" "hammer3r.X2"
## [183] "holdingX1" "holdingX2"
## [185] "breaksHoldX1" "breaksHoldX2"
## [187] "caughtHoldX1" "caughtHoldX2"
## [189] "lostHoldX1" "lostHoldX2"
## [191] "muayThaiKickX1" "muayThaiKickX2"
## [193] "pushKickX1" "pushKickX2"
## [195] "totalHoldsX1" "totalHoldsX2"
## [197] "totalLostHoldsX1" "totalLostHoldsX2"
## [199] "totalCaughtHoldsX1" "totalCaughtHoldsX2"
## [201] "totalBreakOutHoldsX1" "totalBreakOutHoldsX2"
## [203] "totalMuayThaiKicksX1" "totalMuayThaiKicksX2"
## [205] "totalPushKicksX1" "totalPushKicksX2"ML_table <- Table10[,c(2:16,48:74,102:128,183:206)]
str(ML_table)## 'data.frame': 529 obs. of 93 variables:
## $ SecondsIntoRound : num 4 5 9 11 13 19 20 23 27 28 ...
## $ lastAction : num 0 4 5 9 11 13 19 20 23 27 ...
## $ SecondsLastRoundAction: num 4 1 4 2 2 6 1 3 4 1 ...
## $ cmTotHitsR.X1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ cmTotHitsL.X1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ cmTotHitsM.X1 : num 1 1 1 3 4 5 5 6 6 7 ...
## $ TotLandsX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ TotMissedX1 : num 1 0 0 2 1 1 0 1 0 1 ...
## $ TotReceivedX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ cmTotHitsR.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ cmTotHitsL.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ cmTotHitsM.X2 : num 0 1 2 2 2 3 4 4 5 5 ...
## $ TotLandsX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ TotMissedX2 : num 0 1 1 0 0 1 1 0 1 0 ...
## $ TotReceivedX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Crossl.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Kneel.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Elbowl.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Hookl.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Jabl.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Kickl.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ upperl.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ takedownl.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hammerl.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Cross2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Knee2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Elbow2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Hook2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Jab2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Kick2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ upper2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ takedown2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hammer2l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Cross3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Knee3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Elbow3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Hook3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Jab3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Kick3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ upper3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ takedown3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hammer3l.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Crossm.X2 : num 0 1 1 0 0 1 0 0 0 0 ...
## $ Kneem.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Elbowm.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Hookm.X2 : num 0 0 0 0 0 0 1 0 0 0 ...
## $ Jabm.X2 : num 0 0 0 0 0 0 0 0 1 0 ...
## $ Kickm.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ upperm.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ takedownm.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hammerm.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Cross2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Knee2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Elbow2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Hook2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Jab2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Kick2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ upper2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ takedown2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hammer2m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Cross3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Knee3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Elbow3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Hook3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Jab3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Kick3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ upper3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ takedown3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hammer3m.X2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ holdingX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ holdingX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ breaksHoldX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ breaksHoldX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ caughtHoldX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ caughtHoldX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ lostHoldX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ lostHoldX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ muayThaiKickX1 : num 0 0 0 0 0 0 0 1 0 0 ...
## $ muayThaiKickX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ pushKickX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ pushKickX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalHoldsX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalHoldsX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalLostHoldsX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalLostHoldsX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalCaughtHoldsX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalCaughtHoldsX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalBreakOutHoldsX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalBreakOutHoldsX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalMuayThaiKicksX1 : num 0 0 0 0 0 0 0 1 1 1 ...
## $ totalMuayThaiKicksX2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalPushKicksX1 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ totalPushKicksX2 : num 0 0 0 0 0 0 0 0 0 0 ...Write this table out to use in Python later. Its easier than selecting the columns in Python.
write.csv(ML_table, 'Felicia_ml_ready.csv', row.names=FALSE)Our target variable is TotLandsX1 which will be a hit landed for X1 based on the other features that include the wrestling features of both X1 and X2, and actions of X2 for hits landed and missed to see how this can predict if X1 will land more hits based on the number of attempts on X1, the seconds holding and being held, the number of holds broken or lost, the seconds since last action by either X1 or X2, the seconds into the round, and cumulative totals for all landed, missed, and received hits for X1 and X2. Since all values are numeric, this will make it easier to classify the hit with linear regression as closer to zero or up to the max hits landed in an observation. We should see what that value is for the max number of hits landed in one second.
max(ML_table$TotLandsX1)## [1] 2This fighter, X1 or Felicia in this case, landed two hits at most in any second observed in the first round with one of three different fighters: Zarah, Megan, or Christiane.
Lets split the data into a 70% training set and a 30% testing set.
set.seed(12345)
inTrain <- createDataPartition(y=ML_table$TotLandsX1, p=0.7, list=FALSE)
trainingSet <- ML_table[inTrain,]
testingSet <- ML_table[-inTrain,]Lets get the dimensions of the testing and training set to see how many observations are in each subset of our data.
dim(testingSet)## [1] 158 93dim(trainingSet)## [1] 371 93We could normalize these features to subtract the mean from each feature and divide by the number of samples or observations in each, but I won’t do that here. And actually this preprocessing can be done within each model using the parameters from the Caret R module. We will use python with the reticulate R package later to do this as well and compare results. We see that the training set has 371 samples and the testing set has 158 samples. Lets see how many of each total landed hits by X1 are in each subset.
library(dplyr)summaryLandedHitsX1 <- trainingSet %>% group_by(TotLandsX1) %>% count(TotLandsX1)
summaryLandedHitsX1## # A tibble: 3 x 2
## # Groups: TotLandsX1 [3]
## TotLandsX1 n
## <dbl> <int>
## 1 0 330
## 2 1 39
## 3 2 2There are 330 instances where no hits were landed by X1, 39 instances where 1 hit was landed by X1, and 2 instances where there were 2 hits landed by X1 in our training set that will build our varioud models to predict on the testing set.
summaryLandedHitsX1b <- testingSet %>% group_by(TotLandsX1) %>% count(TotLandsX1)
summaryLandedHitsX1b## # A tibble: 3 x 2
## # Groups: TotLandsX1 [3]
## TotLandsX1 n
## <dbl> <int>
## 1 0 143
## 2 1 9
## 3 2 6In our testing set, we will predict the hits landed by the model built on our training set to predict 143 instances that 0 hits landed, 9 instances where 1 hit was landed by X1, and 6 instances where 2 hits were landed by X1. We will probably lose precision on the instances where 2 hits were landed, because our model is only using 2 instances that actually had 2 hits landed when building it. The zeros will probably be accurate, and the 1s possibly as well. We will have to see which of our ML models based on the R Carets module perform the best as far as accuracy is concerned.
Lets start with the random forest model using the boot method and also will be centering and scaling the features in our model. We will use the cv or cross validation which tests on subsets when training to build the model, and set classProbs to TRUE so that regression isn’t done on our numeric values, but classification for classes: 0,1,or 2.
rf_cv15 <- train(TotLandsX1~., method='rf',
na.action=na.pass,
data=(trainingSet), preProc = c("center", "scale"),
trControl=trainControl(method='cv', classProbs = T), number=15)predRF_cv15 <- predict(rf_cv15, testingSet)
DF_cv15 <- data.frame(predRF_cv15, ActualHitsLanded=testingSet$TotLandsX1)
length_cv15 <- length(DF_cv15$ActualHitsLanded)
sum_cv15 <- sum(DF_cv15$predRF_boot==DF_cv15$ActualHitsLanded)
accRF_cv15 <- (sum_cv15/length_cv15)
accRF_cv15## [1] 0head(DF_cv15,30)## predRF_cv15 ActualHitsLanded
## 2 -9.464651e-18 0
## 4 -9.880985e-18 0
## 11 -9.409140e-18 0
## 19 -1.232348e-17 0
## 20 -9.159340e-18 0
## 22 4.000000e-03 0
## 26 2.000000e-03 0
## 28 -3.050338e-17 0
## 29 -3.080869e-17 0
## 30 -3.136380e-17 0
## 33 -3.202993e-17 0
## 39 -4.135581e-17 0
## 43 -4.543588e-17 0
## 45 -4.776735e-17 0
## 48 -4.835021e-17 0
## 50 -4.865552e-17 0
## 54 -4.807266e-17 0
## 64 -4.751755e-17 0
## 69 -4.732326e-17 0
## 70 -4.735101e-17 0
## 84 -4.751755e-17 0
## 85 -4.718448e-17 0
## 87 2.000000e-03 0
## 89 -4.493628e-17 0
## 95 -4.474199e-17 0
## 96 -4.463097e-17 0
## 97 1.000000e+00 1
## 99 -4.352074e-17 0
## 100 -3.982925e-17 0
## 102 -4.554690e-17 0The values predicted are outside their class probabilities, probably due to the centered scaling or normalisaion.
Lets try this again without preprocessing which will
rf_cv15b <- train(TotLandsX1~., method='rf',
na.action=na.pass,
data=(trainingSet), #preProc = c("center", "scale"),
trControl=trainControl(method='cv', classProbs = T), number=15)predRF_cv15b <- predict(rf_cv15b, testingSet)
DF_cv15b <- data.frame(predRF_cv15b, ActualHitsLanded=testingSet$TotLandsX1)
length_cv15b <- length(DF_cv15b$ActualHitsLanded)
sum_cv15b <- sum(DF_cv15b$predRF_boot==DF_cv15b$ActualHitsLanded)
accRF_cv15b <- (sum_cv15b/length_cv15b)
accRF_cv15b## [1] 0head(DF_cv15b,30)## predRF_cv15b ActualHitsLanded
## 2 -7.271961e-18 0
## 4 -1.049161e-17 0
## 11 4.000000e-03 0
## 19 -1.124101e-17 0
## 20 2.000000e-03 0
## 22 4.000000e-03 0
## 26 8.000000e-03 0
## 28 -3.946843e-17 0
## 29 -3.977374e-17 0
## 30 2.000000e-03 0
## 33 -4.238276e-17 0
## 39 -5.886958e-17 0
## 43 -6.572520e-17 0
## 45 -6.802892e-17 0
## 48 -7.077672e-17 0
## 50 -7.199796e-17 0
## 54 -7.255307e-17 0
## 64 -7.560619e-17 0
## 69 -7.557843e-17 0
## 70 -7.530088e-17 0
## 84 -7.505108e-17 0
## 85 -7.494005e-17 0
## 87 -7.463474e-17 0
## 89 -7.444045e-17 0
## 95 -7.124856e-17 0
## 96 -7.124856e-17 0
## 97 1.004500e+00 1
## 99 -7.269185e-17 0
## 100 -6.017409e-17 0
## 102 -7.158163e-17 0Lets add in an extra feature that will round these values to 0, 1, or 2 on both random forest models to get a realistic output. Even when not normalizing and using classProbs set to True, the classification is producing probability classes instead. Could be due to the numeric type of the target. Lets see if we change the numeric type to a factor if it classifies better. Or the same as our rounded features of the random forest models normalized and not normalized.
trainingSetFactor <- trainingSet
testingSetFactor <- testingSet
trainingSetFactor$TotLandsX1 <- as.factor(paste(trainingSetFactor$TotLandsX1))
testingSetFactor$TotLandsX1 <- as.factor(paste(testingSetFactor$TotLandsX1))rf_cv15c <- train(TotLandsX1~., method='rf',
na.action=na.pass,
data=(trainingSetFactor), #preProc = c("center", "scale"),
trControl=trainControl(method='cv'), number=15)predRF_cv15c <- predict(rf_cv15c, testingSetFactor)
DF_cv15c <- data.frame(predRF_cv15c, ActualHitsLanded=testingSet$TotLandsX1)
length_cv15c <- length(DF_cv15c$ActualHitsLanded)
sum_cv15c <- sum(DF_cv15c$predRF_cv15c==DF_cv15c$ActualHitsLanded)
accRF_cv15c <- (sum_cv15c/length_cv15c)
accRF_cv15c## [1] 0.9620253head(DF_cv15c,30)## predRF_cv15c ActualHitsLanded
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## 27 1 1
## 28 0 0
## 29 0 0
## 30 0 0It did work better, because the accuracy went from 0 to 96% accuracy in predicting the hits landed correctly. Lets still add in the features to the previous random forest models to compare.
DF_cv15$roundedPrediction <- ifelse(DF_cv15$predRF_cv15 < 0, 0,
ifelse(DF_cv15$predRF_cv15 > 2, 2,
round(DF_cv15$predRF_cv15)
)
)
DF_cv15$Correct <- ifelse(DF_cv15$ActualHitsLanded==DF_cv15$roundedPrediction,1,0)
accuracy1 <- sum(DF_cv15$Correct)/length(DF_cv15$Correct)
accuracy1## [1] 0.9683544head(DF_cv15)## predRF_cv15 ActualHitsLanded roundedPrediction Correct
## 2 -9.464651e-18 0 0 1
## 4 -9.880985e-18 0 0 1
## 11 -9.409140e-18 0 0 1
## 19 -1.232348e-17 0 0 1
## 20 -9.159340e-18 0 0 1
## 22 4.000000e-03 0 0 1The non normalized random forest model results when rounding to boundaries or closest class:
DF_cv15b$roundedPrediction <- ifelse(DF_cv15b$predRF_cv15 < 0, 0,
ifelse(DF_cv15b$predRF_cv15 > 2, 2,
round(DF_cv15b$predRF_cv15)
)
)
DF_cv15b$Correct <- ifelse(DF_cv15b$ActualHitsLanded==DF_cv15b$roundedPrediction,1,0)
accuracy2 <- sum(DF_cv15b$Correct)/length(DF_cv15b$Correct)
accuracy2## [1] 0.9683544head(DF_cv15b)## predRF_cv15b ActualHitsLanded roundedPrediction Correct
## 2 -7.271961e-18 0 0 1
## 4 -1.049161e-17 0 0 1
## 11 4.000000e-03 0 0 1
## 19 -1.124101e-17 0 0 1
## 20 2.000000e-03 0 0 1
## 22 4.000000e-03 0 0 1When classifying with random forest on numeric classes then rounding to the closest boundaries and closest class after closest min or max boundary, the results were the same for normalized or non normalized random forest classification. And the results were better than using the random forest model to classify on numeric values that are actually integers to factors by .06%. Because the former models scored 96.8% accuracy and the latter model scored 96.2% accuracy on the test set.
Next, we will use the k-nearest neighbor algorithm with the cv method to train.
knn_cv <- train(TotLandsX1 ~ .,
method='knn',# preProcess=c('center','scale'),
tuneLength=10, trControl=trainControl(method='cv'),
data=trainingSet)predKNN_cv <- predict(knn_cv, testingSet)
DF_KNN_cv <- data.frame(predKNN_cv, ActualHitsLanded=testingSet$TotLandsX1)
length_KNN_cv <- length(DF_KNN_cv$ActualHitsLanded)
sum_KNN_cv <- sum(DF_KNN_cv$predKNN_cv==DF_KNN_cv$ActualHitsLanded)
accKNN_cv <- (sum_KNN_cv/length_KNN_cv)
accKNN_cv## [1] 0.1329114head(DF_KNN_cv)## predKNN_cv ActualHitsLanded
## 1 0.1304348 0
## 2 0.1304348 0
## 3 0.1304348 0
## 4 0.2173913 0
## 5 0.2608696 0
## 6 0.1739130 0Lets add in the same features of rounded and correct fields to this table as well. As the predicted values are not integers.
DF_KNN_cv$roundedPrediction <- ifelse(DF_KNN_cv$predKNN_cv<0,0,
ifelse(DF_KNN_cv$predKNN_cv>2,2,
round(DF_KNN_cv$predKNN_cv,0)))
DF_KNN_cv$Correct <- ifelse(DF_KNN_cv$ActualHitsLanded==DF_KNN_cv$roundedPrediction,1,0)
accuracy3 <- sum(DF_KNN_cv$Correct)/length(DF_KNN_cv$Correct)
accuracy3## [1] 0.9050633Next, we will use the k-nearest neighbor algorithm with the cv method to train on the target as a factor instead of numeric.
knn_cvb <- train(TotLandsX1 ~ .,
method='knn',# preProcess=c('center','scale'),
tuneLength=10, trControl=trainControl(method='cv'),
data=trainingSetFactor)predKNN_cvb <- predict(knn_cvb, testingSetFactor)
DF_KNN_cvb <- data.frame(predKNN_cvb, ActualHitsLanded=testingSet$TotLandsX1)
length_KNN_cvb <- length(DF_KNN_cvb$ActualHitsLanded)
sum_KNN_cvb <- sum(DF_KNN_cvb$predKNN_cvb==DF_KNN_cvb$ActualHitsLanded)
accKNN_cvb <- (sum_KNN_cvb/length_KNN_cvb)
accKNN_cvb## [1] 0.9050633The KNN algorithm didn’t score as high in accuracy for classifying the total hits landed by X1 in our testing set. But using the KNN to classify on the numeric data then rounding the results to the closest min or max boundaries if out of bounds and then rounding to the nearest number in our 3 classes of 0, 1, or 2 did score exactly the same at 90.5% accuracy.
head(DF_KNN_cvb)## predKNN_cvb ActualHitsLanded
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0Now lets see how well this data will do in a few other algorithms: glm, rpart, and gbm. We have a factor version target and a numeric target with which to compare results.
glmMod2 <- train(TotLandsX1 ~ .,
method='glm', data=trainingSet)predglm2 <- predict(glmMod2, testingSet)
DF_glm2 <- data.frame(predglm2, ActualHitsLanded=testingSet$TotLandsX1)
length_glm2 <- length(DF_glm2$ActualHitsLanded)
sum_glm2 <- sum(DF_glm2$predglm2==DF_glm2$ActualHitsLanded)
accglm2 <- (sum_glm2/length_glm2)
accglm2## [1] 0head(DF_glm2)## predglm2 ActualHitsLanded
## 2 0.006779364 0
## 4 -0.050934403 0
## 11 -0.006285359 0
## 19 0.004485202 0
## 20 0.012384217 0
## 22 0.114243060 0DF_glm2$roundedPrediction <- ifelse(DF_glm2$predglm2<0,0,
ifelse(DF_glm2$predglm2>2,2,
round(DF_glm2$predglm2,0)))
DF_glm2$Correct <- ifelse(DF_glm2$ActualHitsLanded==DF_glm2$roundedPrediction,1,0)
accuracy4 <- sum(DF_glm2$Correct)/length(DF_glm2$Correct)
accuracy4## [1] 0.9683544head(DF_glm2)## predglm2 ActualHitsLanded roundedPrediction Correct
## 2 0.006779364 0 0 1
## 4 -0.050934403 0 0 1
## 11 -0.006285359 0 0 1
## 19 0.004485202 0 0 1
## 20 0.012384217 0 0 1
## 22 0.114243060 0 0 1The glm or generalized linear model algorithm when correcting for min/max boundaries and rounding the predicted result with the actual result scored 96.8% accuracy. This makes it in a tie with the two random forest models using the same boundary and rounded modifications on numeric classes when classifying the hits landed for each instance as a 0, 1, or 2.
rpartMod <- train(TotLandsX1 ~ .,
method='rpart', data=trainingSet)predrpart <- predict(rpartMod, testingSet)
DF_rpart <- data.frame(predrpart, ActualHitsLanded=testingSet$TotLandsX1)
length_rpart <- length(DF_rpart$ActualHitsLanded)
sum_rpart <- sum(DF_rpart$predrpart==DF_rpart$ActualHitsLanded)
accrpart <- (sum_rpart/length_rpart)
accrpart## [1] 0.9050633head(DF_rpart)## predrpart ActualHitsLanded
## 2 0 0
## 4 0 0
## 11 0 0
## 19 0 0
## 20 0 0
## 22 0 0The rpart algorithm scored as well as the KNN at 90.5% accuracy in prediction.
DF_rpart$roundedPrediction <- ifelse(DF_rpart$predrpart<0,0,
ifelse(DF_rpart$predrpart>2,2,
round(DF_rpart$predrpart,0)))
DF_rpart$Correct <- ifelse(DF_rpart$ActualHitsLanded==DF_rpart$roundedPrediction,1,0)
accuracy5 <- sum(DF_rpart$Correct)/length(DF_rpart$Correct)
accuracy5## [1] 0.9620253When correcting for min/max boundaries and rounding as a modification used on the other models the rpart model scored near 6% better in accuracy at 96.2%. But still not as good as our best models of glm and random forest classifications on numeric data.
head(DF_rpart)## predrpart ActualHitsLanded roundedPrediction Correct
## 2 0 0 0 1
## 4 0 0 0 1
## 11 0 0 0 1
## 19 0 0 0 1
## 20 0 0 0 1
## 22 0 0 0 1Lets look at the gradient boosted models or gbm model that uses the previous output to improve on a gradient and weights adjusted by the previous iteration. You will see a lengthly output of the process, but it shows how with each iteration the model tries to improve.
gbmMod <- train(TotLandsX1 ~ .,
method='gbm', data=trainingSet)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0745 nan 0.1000 0.0199
## 2 0.0603 nan 0.1000 0.0115
## 3 0.0489 nan 0.1000 0.0120
## 4 0.0396 nan 0.1000 0.0084
## 5 0.0321 nan 0.1000 0.0072
## 6 0.0260 nan 0.1000 0.0055
## 7 0.0210 nan 0.1000 0.0031
## 8 0.0170 nan 0.1000 0.0044
## 9 0.0138 nan 0.1000 0.0035
## 10 0.0112 nan 0.1000 0.0029
## 20 0.0014 nan 0.1000 0.0003
## 40 0.0000 nan 0.1000 0.0000
## 60 0.0000 nan 0.1000 0.0000
## 80 0.0000 nan 0.1000 0.0000
## 100 0.0000 nan 0.1000 0.0000
## 120 0.0000 nan 0.1000 0.0000
## 140 0.0000 nan 0.1000 0.0000
## 150 0.0000 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0745 nan 0.1000 0.0207
## 2 0.0603 nan 0.1000 0.0141
## 3 0.0489 nan 0.1000 0.0125
## 4 0.0396 nan 0.1000 0.0093
## 5 0.0321 nan 0.1000 0.0072
## 6 0.0260 nan 0.1000 0.0047
## 7 0.0210 nan 0.1000 0.0047
## 8 0.0170 nan 0.1000 0.0042
## 9 0.0138 nan 0.1000 0.0038
## 10 0.0112 nan 0.1000 0.0031
## 20 0.0014 nan 0.1000 0.0003
## 40 0.0000 nan 0.1000 0.0000
## 60 0.0000 nan 0.1000 0.0000
## 80 0.0000 nan 0.1000 0.0000
## 100 0.0000 nan 0.1000 0.0000
## 120 0.0000 nan 0.1000 0.0000
## 140 0.0000 nan 0.1000 0.0000
## 150 0.0000 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0745 nan 0.1000 0.0134
## 2 0.0603 nan 0.1000 0.0135
## 3 0.0489 nan 0.1000 0.0141
## 4 0.0396 nan 0.1000 0.0093
## 5 0.0321 nan 0.1000 0.0075
## 6 0.0260 nan 0.1000 0.0064
## 7 0.0210 nan 0.1000 0.0068
## 8 0.0170 nan 0.1000 0.0045
## 9 0.0138 nan 0.1000 0.0034
## 10 0.0112 nan 0.1000 0.0031
## 20 0.0014 nan 0.1000 0.0003
## 40 0.0000 nan 0.1000 0.0000
## 60 0.0000 nan 0.1000 0.0000
## 80 0.0000 nan 0.1000 0.0000
## 100 0.0000 nan 0.1000 0.0000
## 120 0.0000 nan 0.1000 0.0000
## 140 0.0000 nan 0.1000 0.0000
## 150 0.0000 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1007 nan 0.1000 0.0181
## 2 0.0826 nan 0.1000 0.0176
## 3 0.0684 nan 0.1000 0.0177
## 4 0.0567 nan 0.1000 0.0096
## 5 0.0470 nan 0.1000 0.0055
## 6 0.0390 nan 0.1000 0.0085
## 7 0.0328 nan 0.1000 0.0062
## 8 0.0282 nan 0.1000 0.0045
## 9 0.0249 nan 0.1000 0.0043
## 10 0.0218 nan 0.1000 0.0032
## 20 0.0091 nan 0.1000 0.0004
## 40 0.0068 nan 0.1000 -0.0001
## 60 0.0066 nan 0.1000 0.0000
## 80 0.0065 nan 0.1000 -0.0001
## 100 0.0065 nan 0.1000 -0.0000
## 120 0.0063 nan 0.1000 -0.0000
## 140 0.0063 nan 0.1000 0.0000
## 150 0.0062 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1022 nan 0.1000 0.0211
## 2 0.0846 nan 0.1000 0.0162
## 3 0.0697 nan 0.1000 0.0132
## 4 0.0578 nan 0.1000 0.0148
## 5 0.0487 nan 0.1000 0.0093
## 6 0.0405 nan 0.1000 0.0066
## 7 0.0339 nan 0.1000 0.0059
## 8 0.0286 nan 0.1000 0.0049
## 9 0.0245 nan 0.1000 0.0051
## 10 0.0210 nan 0.1000 0.0036
## 20 0.0082 nan 0.1000 0.0003
## 40 0.0062 nan 0.1000 -0.0001
## 60 0.0056 nan 0.1000 0.0000
## 80 0.0050 nan 0.1000 -0.0001
## 100 0.0046 nan 0.1000 -0.0001
## 120 0.0044 nan 0.1000 -0.0000
## 140 0.0041 nan 0.1000 -0.0000
## 150 0.0040 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1004 nan 0.1000 0.0200
## 2 0.0814 nan 0.1000 0.0176
## 3 0.0676 nan 0.1000 0.0157
## 4 0.0557 nan 0.1000 0.0124
## 5 0.0467 nan 0.1000 0.0103
## 6 0.0390 nan 0.1000 0.0065
## 7 0.0337 nan 0.1000 0.0054
## 8 0.0282 nan 0.1000 0.0040
## 9 0.0239 nan 0.1000 0.0043
## 10 0.0209 nan 0.1000 0.0030
## 20 0.0083 nan 0.1000 0.0005
## 40 0.0060 nan 0.1000 -0.0002
## 60 0.0052 nan 0.1000 0.0000
## 80 0.0047 nan 0.1000 0.0000
## 100 0.0042 nan 0.1000 -0.0001
## 120 0.0037 nan 0.1000 -0.0000
## 140 0.0034 nan 0.1000 -0.0000
## 150 0.0033 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0928 nan 0.1000 0.0192
## 2 0.0769 nan 0.1000 0.0195
## 3 0.0633 nan 0.1000 0.0128
## 4 0.0523 nan 0.1000 0.0103
## 5 0.0425 nan 0.1000 0.0104
## 6 0.0354 nan 0.1000 0.0069
## 7 0.0297 nan 0.1000 0.0054
## 8 0.0251 nan 0.1000 0.0044
## 9 0.0211 nan 0.1000 0.0047
## 10 0.0181 nan 0.1000 0.0032
## 20 0.0064 nan 0.1000 0.0003
## 40 0.0051 nan 0.1000 -0.0000
## 60 0.0048 nan 0.1000 -0.0000
## 80 0.0046 nan 0.1000 -0.0001
## 100 0.0046 nan 0.1000 -0.0000
## 120 0.0045 nan 0.1000 -0.0000
## 140 0.0044 nan 0.1000 -0.0000
## 150 0.0044 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0928 nan 0.1000 0.0198
## 2 0.0763 nan 0.1000 0.0141
## 3 0.0628 nan 0.1000 0.0132
## 4 0.0525 nan 0.1000 0.0120
## 5 0.0440 nan 0.1000 0.0079
## 6 0.0364 nan 0.1000 0.0092
## 7 0.0304 nan 0.1000 0.0059
## 8 0.0256 nan 0.1000 0.0041
## 9 0.0216 nan 0.1000 0.0045
## 10 0.0188 nan 0.1000 0.0036
## 20 0.0061 nan 0.1000 0.0004
## 40 0.0044 nan 0.1000 -0.0001
## 60 0.0041 nan 0.1000 -0.0000
## 80 0.0039 nan 0.1000 -0.0000
## 100 0.0037 nan 0.1000 -0.0001
## 120 0.0034 nan 0.1000 -0.0000
## 140 0.0033 nan 0.1000 -0.0000
## 150 0.0032 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0929 nan 0.1000 0.0158
## 2 0.0761 nan 0.1000 0.0184
## 3 0.0621 nan 0.1000 0.0089
## 4 0.0505 nan 0.1000 0.0101
## 5 0.0419 nan 0.1000 0.0081
## 6 0.0347 nan 0.1000 0.0078
## 7 0.0291 nan 0.1000 0.0046
## 8 0.0249 nan 0.1000 0.0045
## 9 0.0215 nan 0.1000 0.0035
## 10 0.0183 nan 0.1000 0.0029
## 20 0.0060 nan 0.1000 0.0002
## 40 0.0044 nan 0.1000 -0.0001
## 60 0.0041 nan 0.1000 -0.0000
## 80 0.0038 nan 0.1000 -0.0000
## 100 0.0035 nan 0.1000 -0.0001
## 120 0.0034 nan 0.1000 -0.0000
## 140 0.0032 nan 0.1000 -0.0000
## 150 0.0032 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0958 nan 0.1000 0.0268
## 2 0.0794 nan 0.1000 0.0171
## 3 0.0644 nan 0.1000 0.0191
## 4 0.0535 nan 0.1000 0.0090
## 5 0.0444 nan 0.1000 0.0089
## 6 0.0382 nan 0.1000 0.0057
## 7 0.0326 nan 0.1000 0.0060
## 8 0.0273 nan 0.1000 0.0039
## 9 0.0241 nan 0.1000 0.0036
## 10 0.0206 nan 0.1000 0.0037
## 20 0.0090 nan 0.1000 0.0004
## 40 0.0069 nan 0.1000 0.0000
## 60 0.0065 nan 0.1000 -0.0000
## 80 0.0064 nan 0.1000 -0.0001
## 100 0.0063 nan 0.1000 -0.0000
## 120 0.0062 nan 0.1000 -0.0001
## 140 0.0062 nan 0.1000 -0.0000
## 150 0.0061 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0962 nan 0.1000 0.0221
## 2 0.0797 nan 0.1000 0.0179
## 3 0.0648 nan 0.1000 0.0138
## 4 0.0542 nan 0.1000 0.0099
## 5 0.0450 nan 0.1000 0.0086
## 6 0.0381 nan 0.1000 0.0076
## 7 0.0320 nan 0.1000 0.0059
## 8 0.0270 nan 0.1000 0.0052
## 9 0.0234 nan 0.1000 0.0039
## 10 0.0201 nan 0.1000 0.0034
## 20 0.0083 nan 0.1000 0.0005
## 40 0.0061 nan 0.1000 -0.0000
## 60 0.0055 nan 0.1000 -0.0000
## 80 0.0051 nan 0.1000 -0.0000
## 100 0.0048 nan 0.1000 -0.0001
## 120 0.0046 nan 0.1000 -0.0000
## 140 0.0043 nan 0.1000 -0.0000
## 150 0.0042 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0946 nan 0.1000 0.0263
## 2 0.0785 nan 0.1000 0.0169
## 3 0.0637 nan 0.1000 0.0120
## 4 0.0523 nan 0.1000 0.0125
## 5 0.0447 nan 0.1000 0.0092
## 6 0.0377 nan 0.1000 0.0088
## 7 0.0315 nan 0.1000 0.0056
## 8 0.0263 nan 0.1000 0.0035
## 9 0.0220 nan 0.1000 0.0036
## 10 0.0193 nan 0.1000 0.0028
## 20 0.0079 nan 0.1000 0.0000
## 40 0.0059 nan 0.1000 -0.0000
## 60 0.0053 nan 0.1000 0.0000
## 80 0.0047 nan 0.1000 -0.0001
## 100 0.0042 nan 0.1000 -0.0000
## 120 0.0039 nan 0.1000 -0.0000
## 140 0.0035 nan 0.1000 -0.0000
## 150 0.0034 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0842 nan 0.1000 0.0156
## 2 0.0691 nan 0.1000 0.0142
## 3 0.0562 nan 0.1000 0.0114
## 4 0.0458 nan 0.1000 0.0073
## 5 0.0379 nan 0.1000 0.0082
## 6 0.0310 nan 0.1000 0.0060
## 7 0.0258 nan 0.1000 0.0062
## 8 0.0216 nan 0.1000 0.0051
## 9 0.0178 nan 0.1000 0.0036
## 10 0.0148 nan 0.1000 0.0027
## 20 0.0041 nan 0.1000 0.0003
## 40 0.0026 nan 0.1000 -0.0000
## 60 0.0025 nan 0.1000 -0.0000
## 80 0.0025 nan 0.1000 -0.0000
## 100 0.0025 nan 0.1000 -0.0000
## 120 0.0025 nan 0.1000 -0.0000
## 140 0.0025 nan 0.1000 -0.0000
## 150 0.0025 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0849 nan 0.1000 0.0200
## 2 0.0697 nan 0.1000 0.0169
## 3 0.0567 nan 0.1000 0.0109
## 4 0.0467 nan 0.1000 0.0128
## 5 0.0381 nan 0.1000 0.0073
## 6 0.0309 nan 0.1000 0.0080
## 7 0.0258 nan 0.1000 0.0060
## 8 0.0216 nan 0.1000 0.0041
## 9 0.0177 nan 0.1000 0.0037
## 10 0.0146 nan 0.1000 0.0028
## 20 0.0037 nan 0.1000 0.0003
## 40 0.0024 nan 0.1000 -0.0000
## 60 0.0023 nan 0.1000 -0.0000
## 80 0.0021 nan 0.1000 -0.0000
## 100 0.0020 nan 0.1000 -0.0000
## 120 0.0020 nan 0.1000 -0.0000
## 140 0.0020 nan 0.1000 -0.0000
## 150 0.0019 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0849 nan 0.1000 0.0184
## 2 0.0697 nan 0.1000 0.0150
## 3 0.0565 nan 0.1000 0.0145
## 4 0.0456 nan 0.1000 0.0132
## 5 0.0371 nan 0.1000 0.0089
## 6 0.0308 nan 0.1000 0.0071
## 7 0.0257 nan 0.1000 0.0058
## 8 0.0211 nan 0.1000 0.0045
## 9 0.0178 nan 0.1000 0.0042
## 10 0.0147 nan 0.1000 0.0029
## 20 0.0040 nan 0.1000 0.0003
## 40 0.0025 nan 0.1000 -0.0000
## 60 0.0024 nan 0.1000 -0.0000
## 80 0.0022 nan 0.1000 -0.0000
## 100 0.0021 nan 0.1000 -0.0000
## 120 0.0020 nan 0.1000 -0.0000
## 140 0.0019 nan 0.1000 -0.0000
## 150 0.0019 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0993 nan 0.1000 0.0216
## 2 0.0823 nan 0.1000 0.0200
## 3 0.0683 nan 0.1000 0.0164
## 4 0.0565 nan 0.1000 0.0095
## 5 0.0467 nan 0.1000 0.0099
## 6 0.0393 nan 0.1000 0.0078
## 7 0.0329 nan 0.1000 0.0054
## 8 0.0276 nan 0.1000 0.0056
## 9 0.0234 nan 0.1000 0.0031
## 10 0.0198 nan 0.1000 0.0022
## 20 0.0068 nan 0.1000 0.0004
## 40 0.0049 nan 0.1000 -0.0001
## 60 0.0048 nan 0.1000 -0.0000
## 80 0.0047 nan 0.1000 -0.0000
## 100 0.0046 nan 0.1000 -0.0000
## 120 0.0046 nan 0.1000 0.0000
## 140 0.0045 nan 0.1000 -0.0000
## 150 0.0045 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0990 nan 0.1000 0.0268
## 2 0.0812 nan 0.1000 0.0175
## 3 0.0674 nan 0.1000 0.0166
## 4 0.0550 nan 0.1000 0.0105
## 5 0.0455 nan 0.1000 0.0089
## 6 0.0379 nan 0.1000 0.0070
## 7 0.0315 nan 0.1000 0.0076
## 8 0.0261 nan 0.1000 0.0042
## 9 0.0220 nan 0.1000 0.0046
## 10 0.0186 nan 0.1000 0.0038
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## 40 0.0045 nan 0.1000 0.0000
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## 80 0.0040 nan 0.1000 -0.0000
## 100 0.0038 nan 0.1000 -0.0001
## 120 0.0037 nan 0.1000 -0.0001
## 140 0.0035 nan 0.1000 -0.0001
## 150 0.0034 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1002 nan 0.1000 0.0179
## 2 0.0822 nan 0.1000 0.0182
## 3 0.0674 nan 0.1000 0.0172
## 4 0.0546 nan 0.1000 0.0140
## 5 0.0442 nan 0.1000 0.0099
## 6 0.0373 nan 0.1000 0.0073
## 7 0.0307 nan 0.1000 0.0056
## 8 0.0262 nan 0.1000 0.0049
## 9 0.0221 nan 0.1000 0.0049
## 10 0.0186 nan 0.1000 0.0027
## 20 0.0065 nan 0.1000 0.0004
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## 60 0.0042 nan 0.1000 -0.0000
## 80 0.0039 nan 0.1000 -0.0001
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## 120 0.0034 nan 0.1000 -0.0000
## 140 0.0033 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1304 nan 0.1000 0.0291
## 2 0.1073 nan 0.1000 0.0242
## 3 0.0893 nan 0.1000 0.0144
## 4 0.0756 nan 0.1000 0.0148
## 5 0.0639 nan 0.1000 0.0115
## 6 0.0544 nan 0.1000 0.0094
## 7 0.0461 nan 0.1000 0.0081
## 8 0.0395 nan 0.1000 0.0061
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## 140 0.0084 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1318 nan 0.1000 0.0250
## 2 0.1096 nan 0.1000 0.0230
## 3 0.0904 nan 0.1000 0.0185
## 4 0.0746 nan 0.1000 0.0167
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## 6 0.0520 nan 0.1000 0.0113
## 7 0.0447 nan 0.1000 0.0088
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1299 nan 0.1000 0.0295
## 2 0.1086 nan 0.1000 0.0237
## 3 0.0912 nan 0.1000 0.0197
## 4 0.0761 nan 0.1000 0.0157
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## 140 0.0042 nan 0.1000 -0.0000
## 150 0.0039 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0813 nan 0.1000 0.0198
## 2 0.0659 nan 0.1000 0.0161
## 3 0.0534 nan 0.1000 0.0094
## 4 0.0432 nan 0.1000 0.0105
## 5 0.0350 nan 0.1000 0.0075
## 6 0.0284 nan 0.1000 0.0077
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## 8 0.0186 nan 0.1000 0.0034
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0813 nan 0.1000 0.0190
## 2 0.0659 nan 0.1000 0.0161
## 3 0.0534 nan 0.1000 0.0125
## 4 0.0432 nan 0.1000 0.0089
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0813 nan 0.1000 0.0182
## 2 0.0659 nan 0.1000 0.0148
## 3 0.0534 nan 0.1000 0.0099
## 4 0.0432 nan 0.1000 0.0093
## 5 0.0350 nan 0.1000 0.0085
## 6 0.0284 nan 0.1000 0.0072
## 7 0.0230 nan 0.1000 0.0045
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0848 nan 0.1000 0.0203
## 2 0.0697 nan 0.1000 0.0148
## 3 0.0574 nan 0.1000 0.0131
## 4 0.0469 nan 0.1000 0.0085
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## 6 0.0312 nan 0.1000 0.0049
## 7 0.0267 nan 0.1000 0.0056
## 8 0.0226 nan 0.1000 0.0036
## 9 0.0190 nan 0.1000 0.0031
## 10 0.0162 nan 0.1000 0.0015
## 20 0.0064 nan 0.1000 0.0001
## 40 0.0046 nan 0.1000 -0.0001
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## 80 0.0041 nan 0.1000 0.0000
## 100 0.0038 nan 0.1000 0.0000
## 120 0.0035 nan 0.1000 -0.0000
## 140 0.0032 nan 0.1000 0.0000
## 150 0.0031 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0848 nan 0.1000 0.0203
## 2 0.0705 nan 0.1000 0.0142
## 3 0.0573 nan 0.1000 0.0126
## 4 0.0464 nan 0.1000 0.0088
## 5 0.0382 nan 0.1000 0.0061
## 6 0.0318 nan 0.1000 0.0068
## 7 0.0271 nan 0.1000 0.0062
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## 10 0.0168 nan 0.1000 0.0030
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## 40 0.0044 nan 0.1000 -0.0000
## 60 0.0039 nan 0.1000 -0.0000
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## 100 0.0028 nan 0.1000 0.0000
## 120 0.0022 nan 0.1000 0.0000
## 140 0.0020 nan 0.1000 -0.0000
## 150 0.0017 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0858 nan 0.1000 0.0203
## 2 0.0696 nan 0.1000 0.0156
## 3 0.0573 nan 0.1000 0.0125
## 4 0.0481 nan 0.1000 0.0121
## 5 0.0394 nan 0.1000 0.0068
## 6 0.0329 nan 0.1000 0.0060
## 7 0.0281 nan 0.1000 0.0060
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## 9 0.0196 nan 0.1000 0.0029
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## 20 0.0060 nan 0.1000 0.0003
## 40 0.0043 nan 0.1000 0.0000
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## 100 0.0024 nan 0.1000 -0.0000
## 120 0.0020 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0911 nan 0.1000 0.0191
## 2 0.0765 nan 0.1000 0.0187
## 3 0.0634 nan 0.1000 0.0165
## 4 0.0526 nan 0.1000 0.0088
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## 7 0.0314 nan 0.1000 0.0053
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0921 nan 0.1000 0.0158
## 2 0.0764 nan 0.1000 0.0172
## 3 0.0625 nan 0.1000 0.0155
## 4 0.0523 nan 0.1000 0.0112
## 5 0.0428 nan 0.1000 0.0085
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## 8 0.0250 nan 0.1000 0.0053
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## 20 0.0080 nan 0.1000 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0928 nan 0.1000 0.0222
## 2 0.0771 nan 0.1000 0.0143
## 3 0.0634 nan 0.1000 0.0135
## 4 0.0530 nan 0.1000 0.0098
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## 7 0.0303 nan 0.1000 0.0059
## 8 0.0266 nan 0.1000 0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0849 nan 0.1000 0.0208
## 2 0.0697 nan 0.1000 0.0182
## 3 0.0565 nan 0.1000 0.0139
## 4 0.0466 nan 0.1000 0.0123
## 5 0.0385 nan 0.1000 0.0080
## 6 0.0320 nan 0.1000 0.0074
## 7 0.0266 nan 0.1000 0.0073
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## 9 0.0184 nan 0.1000 0.0037
## 10 0.0152 nan 0.1000 0.0029
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0849 nan 0.1000 0.0168
## 2 0.0687 nan 0.1000 0.0185
## 3 0.0565 nan 0.1000 0.0141
## 4 0.0466 nan 0.1000 0.0094
## 5 0.0385 nan 0.1000 0.0084
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## 9 0.0180 nan 0.1000 0.0036
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0841 nan 0.1000 0.0157
## 2 0.0684 nan 0.1000 0.0144
## 3 0.0555 nan 0.1000 0.0136
## 4 0.0458 nan 0.1000 0.0097
## 5 0.0372 nan 0.1000 0.0079
## 6 0.0304 nan 0.1000 0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0656 nan 0.1000 0.0107
## 2 0.0532 nan 0.1000 0.0114
## 3 0.0431 nan 0.1000 0.0120
## 4 0.0349 nan 0.1000 0.0084
## 5 0.0283 nan 0.1000 0.0072
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## 7 0.0185 nan 0.1000 0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0656 nan 0.1000 0.0158
## 2 0.0532 nan 0.1000 0.0141
## 3 0.0431 nan 0.1000 0.0098
## 4 0.0349 nan 0.1000 0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0656 nan 0.1000 0.0175
## 2 0.0532 nan 0.1000 0.0128
## 3 0.0431 nan 0.1000 0.0087
## 4 0.0349 nan 0.1000 0.0093
## 5 0.0283 nan 0.1000 0.0050
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1009 nan 0.1000 0.0241
## 2 0.0827 nan 0.1000 0.0175
## 3 0.0680 nan 0.1000 0.0147
## 4 0.0562 nan 0.1000 0.0093
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## 6 0.0385 nan 0.1000 0.0090
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1000 nan 0.1000 0.0244
## 2 0.0813 nan 0.1000 0.0168
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## 4 0.0555 nan 0.1000 0.0146
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1010 nan 0.1000 0.0184
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0880 nan 0.1000 0.0214
## 2 0.0713 nan 0.1000 0.0185
## 3 0.0577 nan 0.1000 0.0140
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0880 nan 0.1000 0.0214
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## 3 0.0577 nan 0.1000 0.0140
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0880 nan 0.1000 0.0237
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1157 nan 0.1000 0.0208
## 2 0.0965 nan 0.1000 0.0191
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1149 nan 0.1000 0.0259
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## 20 0.0087 nan 0.1000 0.0004
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## 60 0.0056 nan 0.1000 -0.0001
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## 100 0.0045 nan 0.1000 -0.0001
## 120 0.0041 nan 0.1000 -0.0000
## 140 0.0038 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1148 nan 0.1000 0.0298
## 2 0.0943 nan 0.1000 0.0169
## 3 0.0778 nan 0.1000 0.0172
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## 140 0.0029 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0841 nan 0.1000 0.0182
## 2 0.0690 nan 0.1000 0.0161
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0849 nan 0.1000 0.0232
## 2 0.0697 nan 0.1000 0.0176
## 3 0.0567 nan 0.1000 0.0114
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0838 nan 0.1000 0.0236
## 2 0.0679 nan 0.1000 0.0160
## 3 0.0559 nan 0.1000 0.0134
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1299 nan 0.1000 0.0275
## 2 0.1079 nan 0.1000 0.0230
## 3 0.0892 nan 0.1000 0.0137
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1309 nan 0.1000 0.0225
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## 3 0.0888 nan 0.1000 0.0181
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1285 nan 0.1000 0.0279
## 2 0.1076 nan 0.1000 0.0225
## 3 0.0897 nan 0.1000 0.0172
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0936 nan 0.1000 0.0189
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0946 nan 0.1000 0.0181
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## 3 0.0645 nan 0.1000 0.0143
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0940 nan 0.1000 0.0263
## 2 0.0770 nan 0.1000 0.0158
## 3 0.0641 nan 0.1000 0.0161
## 4 0.0535 nan 0.1000 0.0100
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0873 nan 0.1000 0.0205
## 2 0.0727 nan 0.1000 0.0120
## 3 0.0607 nan 0.1000 0.0121
## 4 0.0505 nan 0.1000 0.0077
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0875 nan 0.1000 0.0149
## 2 0.0735 nan 0.1000 0.0143
## 3 0.0605 nan 0.1000 0.0122
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0904 nan 0.1000 0.0187
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0912 nan 0.1000 0.0259
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0912 nan 0.1000 0.0206
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0912 nan 0.1000 0.0236
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1064 nan 0.1000 0.0185
## 2 0.0874 nan 0.1000 0.0169
## 3 0.0719 nan 0.1000 0.0142
## 4 0.0587 nan 0.1000 0.0123
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1050 nan 0.1000 0.0262
## 2 0.0857 nan 0.1000 0.0191
## 3 0.0703 nan 0.1000 0.0164
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1048 nan 0.1000 0.0226
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1036 nan 0.1000 0.0235
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1033 nan 0.1000 0.0232
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## 3 0.0712 nan 0.1000 0.0122
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1046 nan 0.1000 0.0242
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0852 nan 0.1000 0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0860 nan 0.1000 0.0178
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0861 nan 0.1000 0.0160
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0692 nan 0.1000 0.0125
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0692 nan 0.1000 0.0168
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0684 nan 0.1000 0.0121
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## 10 0.0130 nan 0.1000 0.0026
## 20 0.0039 nan 0.1000 0.0003
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## 60 0.0024 nan 0.1000 -0.0000
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## 100 0.0022 nan 0.1000 -0.0000
## 120 0.0021 nan 0.1000 0.0000
## 140 0.0020 nan 0.1000 -0.0000
## 150 0.0020 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1232 nan 0.1000 0.0257
## 2 0.1025 nan 0.1000 0.0222
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## 150 0.0079 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1226 nan 0.1000 0.0282
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1228 nan 0.1000 0.0291
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0912 nan 0.1000 0.0217
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## 20 0.0067 nan 0.1000 0.0004
## 40 0.0047 nan 0.1000 -0.0001
## 50 0.0045 nan 0.1000 -0.0001predgbm <- predict(gbmMod, testingSet)
DF_gbm <- data.frame(predgbm, ActualHitsLanded=testingSet$TotLandsX1)
length_gbm <- length(DF_gbm$ActualHitsLanded)
sum_gbm <- sum(DF_gbm$predgbm==DF_gbm$ActualHitsLanded)
accgbm <- (sum_gbm/length_gbm)
accgbm## [1] 0head(DF_gbm)## predgbm ActualHitsLanded
## 1 4.208414e-03 0
## 2 4.208414e-03 0
## 3 4.208414e-03 0
## 4 -4.576709e-04 0
## 5 -4.576709e-04 0
## 6 -3.408523e-05 0DF_gbm$roundedPrediction <- ifelse(DF_gbm$predgbm<0,0,
ifelse(DF_gbm$predgbm>2,2,
round(DF_gbm$predgbm,0)))
DF_gbm$Correct <- ifelse(DF_gbm$ActualHitsLanded==DF_gbm$roundedPrediction,1,0)
accuracy6 <- sum(DF_gbm$Correct)/length(DF_gbm$Correct)
accuracy6## [1] 0.9620253When correcting for min/max boundaries and rounding to closest numeric class of 0, 1, or 2, the gbm algorithm scored as well as the modified rpart model at 96.2% accuracty in prediction. This model is also not the best for this data, but as good as the random forest classifier on the target feature as a factor instead of numeric.
head(DF_gbm)## predgbm ActualHitsLanded roundedPrediction Correct
## 1 4.208414e-03 0 0 1
## 2 4.208414e-03 0 0 1
## 3 4.208414e-03 0 0 1
## 4 -4.576709e-04 0 0 1
## 5 -4.576709e-04 0 0 1
## 6 -3.408523e-05 0 0 1That is it for the machine learning using the caret package of algorithms in R. Next we will compare python’s random forest, gradient boosted models, naive bayes, and convolutional neural network and deep neural networks in predicting hits landed using the reticulate package in R to run python code.
library(reticulate)## Warning: package 'reticulate' was built under R version 3.6.3conda_list(conda = "auto") ## name python
## 1 Anaconda2 C:\\Users\\m\\Anaconda2\\python.exe
## 2 djangoenv C:\\Users\\m\\Anaconda2\\envs\\djangoenv\\python.exe
## 3 python36 C:\\Users\\m\\Anaconda2\\envs\\python36\\python.exe
## 4 python37 C:\\Users\\m\\Anaconda2\\envs\\python37\\python.exe
## 5 r-reticulate C:\\Users\\m\\Anaconda2\\envs\\r-reticulate\\python.exeuse_condaenv(condaenv = "python36")import pandas as pd
import sklearn
import numpy as np
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
np.random.seed(47) Import our ML ready file to run the python algorithms on with sklearn.
Felicia = pd.read_csv('Felicia_ml_ready.csv', encoding = 'unicode_escape')
print(Felicia.shape)## (529, 93)print(Felicia.columns)## Index(['SecondsIntoRound', 'lastAction', 'SecondsLastRoundAction',
## 'cmTotHitsR.X1', 'cmTotHitsL.X1', 'cmTotHitsM.X1', 'TotLandsX1',
## 'TotMissedX1', 'TotReceivedX1', 'cmTotHitsR.X2', 'cmTotHitsL.X2',
## 'cmTotHitsM.X2', 'TotLandsX2', 'TotMissedX2', 'TotReceivedX2',
## 'Crossl.X2', 'Kneel.X2', 'Elbowl.X2', 'Hookl.X2', 'Jabl.X2', 'Kickl.X2',
## 'upperl.X2', 'takedownl.X2', 'hammerl.X2', 'Cross2l.X2', 'Knee2l.X2',
## 'Elbow2l.X2', 'Hook2l.X2', 'Jab2l.X2', 'Kick2l.X2', 'upper2l.X2',
## 'takedown2l.X2', 'hammer2l.X2', 'Cross3l.X2', 'Knee3l.X2', 'Elbow3l.X2',
## 'Hook3l.X2', 'Jab3l.X2', 'Kick3l.X2', 'upper3l.X2', 'takedown3l.X2',
## 'hammer3l.X2', 'Crossm.X2', 'Kneem.X2', 'Elbowm.X2', 'Hookm.X2',
## 'Jabm.X2', 'Kickm.X2', 'upperm.X2', 'takedownm.X2', 'hammerm.X2',
## 'Cross2m.X2', 'Knee2m.X2', 'Elbow2m.X2', 'Hook2m.X2', 'Jab2m.X2',
## 'Kick2m.X2', 'upper2m.X2', 'takedown2m.X2', 'hammer2m.X2', 'Cross3m.X2',
## 'Knee3m.X2', 'Elbow3m.X2', 'Hook3m.X2', 'Jab3m.X2', 'Kick3m.X2',
## 'upper3m.X2', 'takedown3m.X2', 'hammer3m.X2', 'holdingX1', 'holdingX2',
## 'breaksHoldX1', 'breaksHoldX2', 'caughtHoldX1', 'caughtHoldX2',
## 'lostHoldX1', 'lostHoldX2', 'muayThaiKickX1', 'muayThaiKickX2',
## 'pushKickX1', 'pushKickX2', 'totalHoldsX1', 'totalHoldsX2',
## 'totalLostHoldsX1', 'totalLostHoldsX2', 'totalCaughtHoldsX1',
## 'totalCaughtHoldsX2', 'totalBreakOutHoldsX1', 'totalBreakOutHoldsX2',
## 'totalMuayThaiKicksX1', 'totalMuayThaiKicksX2', 'totalPushKicksX1',
## 'totalPushKicksX2'],
## dtype='object')print(Felicia.head())## SecondsIntoRound lastAction ... totalPushKicksX1 totalPushKicksX2
## 0 4 0 ... 0 0
## 1 5 4 ... 0 0
## 2 9 5 ... 0 0
## 3 11 9 ... 0 0
## 4 13 11 ... 0 0
##
## [5 rows x 93 columns]print(Felicia.tail())## SecondsIntoRound lastAction ... totalPushKicksX1 totalPushKicksX2
## 524 295 294 ... 2 0
## 525 296 295 ... 2 0
## 526 297 296 ... 2 0
## 527 298 297 ... 2 0
## 528 299 298 ... 2 0
##
## [5 rows x 93 columns]print(Felicia['TotLandsX1'].unique())## [0 1 2]Reorder the rows as instances randomised to split into train and test sets of the data.
import numpy as np
Felicia = Felicia.reindex(np.random.permutation(Felicia.index))
print(Felicia.head())## SecondsIntoRound lastAction ... totalPushKicksX1 totalPushKicksX2
## 274 124 123 ... 3 0
## 453 196 187 ... 2 0
## 470 241 240 ... 2 0
## 146 194 193 ... 1 0
## 208 56 55 ... 3 0
##
## [5 rows x 93 columns]print(Felicia.tail())## SecondsIntoRound lastAction ... totalPushKicksX1 totalPushKicksX2
## 59 107 106 ... 1 0
## 23 70 69 ... 1 0
## 264 114 113 ... 3 0
## 327 177 176 ... 3 0
## 135 183 182 ... 1 0
##
## [5 rows x 93 columns]There are 529 instances in this data, and 80% is about 424, the target is the hits landed by X1.
# Split/splice into training ~ 80% and testing ~ 20%
Felicia_train = Felicia[:424]
Felicia_test = Felicia[424:]
Felicia_hits_train = Felicia['TotLandsX1'][:424]
Felicia_hits_test = Felicia['TotLandsX1'][424:]
print(Felicia_train.shape)## (424, 93)print(Felicia_test.shape)## (105, 93)mnb_Fit = MultinomialNB().fit(Felicia_train, Felicia_hits_train)predictions = mnb_Fit.predict(Felicia_test)
prd = pd.DataFrame(predictions)
prd.columns=['predictions']
prd.index=Felicia_hits_test.index
pred=pd.concat([pd.DataFrame(prd),Felicia_hits_test],axis=1)
print(pred)## predictions TotLandsX1
## 30 0 0
## 95 1 0
## 72 1 0
## 41 0 0
## 490 0 0
## .. ... ...
## 59 1 1
## 23 1 1
## 264 2 0
## 327 2 0
## 135 1 0
##
## [105 rows x 2 columns]print('accuracy', accuracy_score(Felicia_hits_test, predictions))## accuracy 0.45714285714285713print('confusion matrix')## confusion matrixprint('rows=expected, cols=predicted')## rows=expected, cols=predictedprint(confusion_matrix(Felicia_hits_test, predictions))## [[40 26 26]
## [ 3 8 2]
## [ 0 0 0]]Think of the recall as the rows that all expected instances need to be found and those outside the correct value reduce the ‘recall’ of the test. Also, think of the columns as the predicted that mean those predicted that value in the same column reduce the precision.
The seed wasn’t set, and setting it in numpy didn’t work, because the output is different. In tensorflow it works when we get to that CNN.
So we can see for 0 hits landed it expected 47, misidentified a 1 hit landed as 0 for 47/48 precision accuracy, and misidentified 17 1 hits and 30 2 hits as a 0 hits landed with a recall of 47/(47+17+30) accuracy. The 2s were mostly classified incorrectly as 0s when they were 2s, and the 1s were correctly identified for 8 instances on the test set but 17 were classified incorrectly as 0s and 2 classes that weren’t a 1 were also classified as a 1. This model for multinomial naive bayes scored the 0s accurately but misclassified the 1s by about one third and the 2s were misclassified as 0s or 1 1/32 for very inaccurate results.
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import precision_recall_fscore_support as score
import timerf=RandomForestClassifier(n_estimators=150, max_depth=None, n_jobs=-1)
start=time.time()
rf_model=rf.fit(Felicia_train,Felicia_hits_train)
end=time.time()
fit_time=(end-start)
fit_time## 1.399090051651001start=time.time()
y_pred=rf_model.predict(Felicia_test)
end=time.time()
pred_time=(end-start)
pred_time## 0.2519187927246094prd = pd.DataFrame(y_pred)
prd.columns=['Predicted']
prd.index=Felicia_hits_test.index
pred=pd.concat([pd.DataFrame(prd),Felicia_hits_test],axis=1)
print(pred)## Predicted TotLandsX1
## 30 0 0
## 95 0 0
## 72 0 0
## 41 0 0
## 490 0 0
## .. ... ...
## 59 1 1
## 23 1 1
## 264 0 0
## 327 0 0
## 135 0 0
##
## [105 rows x 2 columns]from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
print('accuracy', accuracy_score(Felicia_hits_test, y_pred))## accuracy 1.0print('confusion matrix')## confusion matrixprint(confusion_matrix(Felicia_hits_test, y_pred))## [[92 0]
## [ 0 13]]Random Forest scored 100% accuracy in predicting a hit landed in our test set. All 94 0 hits landed were identified and none of the other classes for 1 or 2 hits landed were incorrectly identified as 0, same for the 1s and the 2s. It was also unbelievably faster than I thought it would take.
gb=GradientBoostingClassifier(n_estimators=150,max_depth=11)
start=time.time()
gb_model=gb.fit(Felicia_train,Felicia_hits_train)
end=time.time()
fit_time=(end-start)
fit_time## 0.6513597965240479start=time.time()
y_pred=gb_model.predict(Felicia_test)
end=time.time()
pred_time=(end-start)
pred_time## 0.03400874137878418prd = pd.DataFrame(y_pred)
prd.columns=['Predicted']
prd.index=Felicia_hits_test.index
pred=pd.concat([pd.DataFrame(prd),Felicia_hits_test],axis=1)
print(pred)## Predicted TotLandsX1
## 30 0 0
## 95 0 0
## 72 0 0
## 41 0 0
## 490 0 0
## .. ... ...
## 59 1 1
## 23 1 1
## 264 0 0
## 327 0 0
## 135 0 0
##
## [105 rows x 2 columns]from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
print('accuracy', accuracy_score(Felicia_hits_test, y_pred))## accuracy 1.0print('confusion matrix')## confusion matrixprint(confusion_matrix(Felicia_hits_test, y_pred))## [[92 0]
## [ 0 13]]The gradient boosted model scored 100% accuracy on the test set in predicting hits landed as did the previous algorithm, random forest classifier.
Lets use convolutional neural nets now. To see if any difference. The last two sklearn model were fast and the best in accuracy as they both scored 100% on the test sets used.
import numpy as np
Felicia2 = Felicia
Felicia2## SecondsIntoRound lastAction ... totalPushKicksX1 totalPushKicksX2
## 274 124 123 ... 3 0
## 453 196 187 ... 2 0
## 470 241 240 ... 2 0
## 146 194 193 ... 1 0
## 208 56 55 ... 3 0
## .. ... ... ... ... ...
## 59 107 106 ... 1 0
## 23 70 69 ... 1 0
## 264 114 113 ... 3 0
## 327 177 176 ... 3 0
## 135 183 182 ... 1 0
##
## [529 rows x 93 columns]class_mapping = {label: idx for idx, label in enumerate(np.unique(Felicia2['TotLandsX1']))}
class_mapping## {0: 0, 1: 1, 2: 2}Felicia_hits_test = pd.DataFrame(Felicia_hits_test)
Felicia_hits_test.columns=['TotLandsX1']
Felicia_hits_test.columns## Index(['TotLandsX1'], dtype='object')Felicia_hits_test['TotLandsX1']=Felicia_hits_test['TotLandsX1'].map(class_mapping)
Felicia_hits_test.head()## TotLandsX1
## 30 0
## 95 0
## 72 0
## 41 0
## 490 0Felicia_hits_train=pd.DataFrame(Felicia_hits_train)
Felicia_hits_train.columns=['TotLandsX1']
Felicia_hits_train.columns## Index(['TotLandsX1'], dtype='object')Felicia_hits_train['TotLandsX1']=Felicia_hits_train['TotLandsX1'].map(class_mapping)
Felicia_hits_train.head()## TotLandsX1
## 274 0
## 453 0
## 470 0
## 146 0
## 208 0Felicia_train.shape## (424, 93)RStudio isn’t recognizing the module ‘tensorflow’ in my python environment named python36. This portion of the neural nets will stop here, because tensorflow isn’t being recognized, even though ‘pip list’ in the conda environment says it is a module available and my modules.
import tensorflow as tf
import tensorflow.contrib.keras as keras
#optionally use import tensorflow.keras as keras when no longer experimental contributor package development
np.random.seed(123)
tf.set_random_seed(123)
model6 = keras.models.Sequential()
model6.add(
keras.layers.Dense(
units=200, #output units need to match next layer inputs
input_dim=93, #number of features for input above says 93
kernel_initializer='glorot_uniform',# name of the guy behind Xavier Initialization; the biases to zero
bias_initializer='zeros',
activation='tanh'))## WARNING: Logging before flag parsing goes to stderr.
## W0528 08:40:54.751217 16084 deprecation.py:506] From C:\Users\m\Anaconda2\envs\python36\lib\site-packages\tensorflow\python\ops\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
## Instructions for updating:
## Call initializer instance with the dtype argument instead of passing it to the constructormodel6.add(
keras.layers.Dense(
units=100, #output matches next layer input
input_dim=200, #input matches last layer's output
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
activation='tanh'))
model6.add(
keras.layers.Dense(
units=100, #output matches next layer input
input_dim=100, #input matches last layer's output
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
activation='tanh'))
model6.add(
keras.layers.Dense(
units=100, #output matches next layer input
input_dim=100, #input matches last layer's output
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
activation='relu'))
model6.add(
keras.layers.Dense(
units=3, #these are the number of class categories in our target
input_dim=100,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
activation='softmax'))#will return the class membership probs summing to 1 of all class probs
# these are hyperparameters that can be tuned if overfitting during training, or to get better accuracy
sgd_optimizer = keras.optimizers.SGD(
lr=0.001, decay=1e-7, momentum=.8)
# categorical_crossentropy is used in multiclass classification instead of binary_crossentropy
# to match the softmax function
model6.compile(optimizer=sgd_optimizer,
loss='sparse_categorical_crossentropy')
# it was 'categorical_crossentropy', but that expects binary matrices of 1s and 0s
# it said to use sparse_categorical_crossentropyimport time
start=time.time()
history6 = model6.fit(Felicia_train, Felicia_hits_train,
batch_size=64, epochs=50,
verbose=1,
validation_split=0.15) ## Train on 360 samples, validate on 64 samples
## Epoch 1/50
##
## 64/360 [====>.........................] - ETA: 8s - loss: 1.2403
## 128/360 [=========>....................] - ETA: 3s - loss: 1.2117
## 256/360 [====================>.........] - ETA: 0s - loss: 1.1205
## 360/360 [==============================] - 2s 6ms/sample - loss: 1.0405 - val_loss: 0.7544
## Epoch 2/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.7713
## 128/360 [=========>....................] - ETA: 0s - loss: 0.7039
## 256/360 [====================>.........] - ETA: 0s - loss: 0.6243
## 360/360 [==============================] - 0s 714us/sample - loss: 0.5912 - val_loss: 0.5205
## Epoch 3/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.5375
## 256/360 [====================>.........] - ETA: 0s - loss: 0.4433
## 360/360 [==============================] - 0s 420us/sample - loss: 0.4279 - val_loss: 0.4615
## Epoch 4/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3164
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3959
## 360/360 [==============================] - 0s 472us/sample - loss: 0.3909 - val_loss: 0.4509
## Epoch 5/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3217
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3536
## 360/360 [==============================] - 0s 441us/sample - loss: 0.3801 - val_loss: 0.4466
## Epoch 6/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2802
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3517
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3559
## 360/360 [==============================] - 0s 621us/sample - loss: 0.3755 - val_loss: 0.4418
## Epoch 7/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.5323
## 192/360 [===============>..............] - ETA: 0s - loss: 0.4329
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3740
## 360/360 [==============================] - 0s 729us/sample - loss: 0.3721 - val_loss: 0.4380
## Epoch 8/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4635
## 192/360 [===============>..............] - ETA: 0s - loss: 0.4202
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3772
## 360/360 [==============================] - 0s 647us/sample - loss: 0.3684 - val_loss: 0.4339
## Epoch 9/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3672
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3832
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3802
## 360/360 [==============================] - 0s 930us/sample - loss: 0.3659 - val_loss: 0.4309
## Epoch 10/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2358
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3189
## 360/360 [==============================] - 0s 413us/sample - loss: 0.3641 - val_loss: 0.4287
## Epoch 11/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2748
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3657
## 360/360 [==============================] - 0s 445us/sample - loss: 0.3619 - val_loss: 0.4252
## Epoch 12/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3216
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3705
## 360/360 [==============================] - 0s 430us/sample - loss: 0.3603 - val_loss: 0.4235
## Epoch 13/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.6510
## 192/360 [===============>..............] - ETA: 0s - loss: 0.4132
## 360/360 [==============================] - 0s 464us/sample - loss: 0.3598 - val_loss: 0.4208
## Epoch 14/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2737
## 192/360 [===============>..............] - ETA: 0s - loss: 0.2934
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3355
## 360/360 [==============================] - 0s 649us/sample - loss: 0.3585 - val_loss: 0.4214
## Epoch 15/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.5129
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3667
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3446
## 360/360 [==============================] - 0s 694us/sample - loss: 0.3574 - val_loss: 0.4184
## Epoch 16/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3436
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3101
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3584
## 360/360 [==============================] - 0s 743us/sample - loss: 0.3563 - val_loss: 0.4165
## Epoch 17/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3930
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3530
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3536
## 360/360 [==============================] - 0s 557us/sample - loss: 0.3552 - val_loss: 0.4163
## Epoch 18/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2251
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3205
## 360/360 [==============================] - 0s 420us/sample - loss: 0.3546 - val_loss: 0.4163
## Epoch 19/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2975
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3068
## 360/360 [==============================] - 0s 421us/sample - loss: 0.3538 - val_loss: 0.4148
## Epoch 20/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3490
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3314
## 360/360 [==============================] - 0s 410us/sample - loss: 0.3530 - val_loss: 0.4145
## Epoch 21/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4889
## 128/360 [=========>....................] - ETA: 0s - loss: 0.3699
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3692
## 360/360 [==============================] - 0s 646us/sample - loss: 0.3524 - val_loss: 0.4132
## Epoch 22/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.1808
## 192/360 [===============>..............] - ETA: 0s - loss: 0.2795
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3378
## 360/360 [==============================] - 0s 646us/sample - loss: 0.3519 - val_loss: 0.4135
## Epoch 23/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3315
## 128/360 [=========>....................] - ETA: 0s - loss: 0.3672
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3540
## 360/360 [==============================] - 0s 758us/sample - loss: 0.3507 - val_loss: 0.4119
## Epoch 24/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4058
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3403
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3413
## 360/360 [==============================] - 0s 681us/sample - loss: 0.3506 - val_loss: 0.4117
## Epoch 25/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3070
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3471
## 360/360 [==============================] - 0s 414us/sample - loss: 0.3497 - val_loss: 0.4105
## Epoch 26/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3591
## 128/360 [=========>....................] - ETA: 0s - loss: 0.3677
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3373
## 360/360 [==============================] - 0s 731us/sample - loss: 0.3492 - val_loss: 0.4092
## Epoch 27/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4457
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3639
## 360/360 [==============================] - 0s 426us/sample - loss: 0.3488 - val_loss: 0.4086
## Epoch 28/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2228
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3820
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3700
## 360/360 [==============================] - 0s 614us/sample - loss: 0.3487 - val_loss: 0.4086
## Epoch 29/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3994
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3186
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3438
## 360/360 [==============================] - 0s 789us/sample - loss: 0.3477 - val_loss: 0.4092
## Epoch 30/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3742
## 192/360 [===============>..............] - ETA: 0s - loss: 0.4057
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3694
## 360/360 [==============================] - 0s 735us/sample - loss: 0.3475 - val_loss: 0.4072
## Epoch 31/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4225
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3411
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3646
## 360/360 [==============================] - 0s 642us/sample - loss: 0.3470 - val_loss: 0.4074
## Epoch 32/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3406
## 128/360 [=========>....................] - ETA: 0s - loss: 0.4074
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3793
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3610
## 360/360 [==============================] - 0s 877us/sample - loss: 0.3465 - val_loss: 0.4057
## Epoch 33/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4032
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3458
## 360/360 [==============================] - 0s 475us/sample - loss: 0.3462 - val_loss: 0.4055
## Epoch 34/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4878
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3956
## 360/360 [==============================] - 0s 406us/sample - loss: 0.3463 - val_loss: 0.4051
## Epoch 35/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3586
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3148
## 360/360 [==============================] - 0s 469us/sample - loss: 0.3454 - val_loss: 0.4059
## Epoch 36/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3464
## 192/360 [===============>..............] - ETA: 0s - loss: 0.4149
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3423
## 360/360 [==============================] - 0s 743us/sample - loss: 0.3450 - val_loss: 0.4038
## Epoch 37/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4677
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3750
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3440
## 360/360 [==============================] - 0s 683us/sample - loss: 0.3449 - val_loss: 0.4017
## Epoch 38/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4847
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3223
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3392
## 360/360 [==============================] - 0s 640us/sample - loss: 0.3442 - val_loss: 0.4035
## Epoch 39/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3054
## 128/360 [=========>....................] - ETA: 0s - loss: 0.4180
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3995
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3507
## 360/360 [==============================] - 0s 819us/sample - loss: 0.3442 - val_loss: 0.3997
## Epoch 40/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2718
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3103
## 360/360 [==============================] - 0s 448us/sample - loss: 0.3435 - val_loss: 0.4012
## Epoch 41/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2187
## 192/360 [===============>..............] - ETA: 0s - loss: 0.2902
## 360/360 [==============================] - 0s 412us/sample - loss: 0.3427 - val_loss: 0.4004
## Epoch 42/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.5298
## 192/360 [===============>..............] - ETA: 0s - loss: 0.4267
## 360/360 [==============================] - 0s 429us/sample - loss: 0.3430 - val_loss: 0.3989
## Epoch 43/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2686
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3963
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3376
## 360/360 [==============================] - 0s 679us/sample - loss: 0.3419 - val_loss: 0.3999
## Epoch 44/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3953
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3810
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3470
## 360/360 [==============================] - 0s 747us/sample - loss: 0.3417 - val_loss: 0.3999
## Epoch 45/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.4485
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3644
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3454
## 360/360 [==============================] - 0s 652us/sample - loss: 0.3412 - val_loss: 0.3990
## Epoch 46/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3610
## 128/360 [=========>....................] - ETA: 0s - loss: 0.3946
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3581
## 360/360 [==============================] - 0s 694us/sample - loss: 0.3411 - val_loss: 0.3972
## Epoch 47/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2954
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3470
## 360/360 [==============================] - 0s 486us/sample - loss: 0.3404 - val_loss: 0.3974
## Epoch 48/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3725
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3647
## 360/360 [==============================] - 0s 376us/sample - loss: 0.3402 - val_loss: 0.3978
## Epoch 49/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.3237
## 192/360 [===============>..............] - ETA: 0s - loss: 0.3086
## 360/360 [==============================] - 0s 537us/sample - loss: 0.3397 - val_loss: 0.3975
## Epoch 50/50
##
## 64/360 [====>.........................] - ETA: 0s - loss: 0.2536
## 256/360 [====================>.........] - ETA: 0s - loss: 0.3235
## 320/360 [=========================>....] - ETA: 0s - loss: 0.3298
## 360/360 [==============================] - 0s 496us/sample - loss: 0.3393 - val_loss: 0.3972end=time.time()
fit_time=(end-start)
print(start,end,fit_time)## 1590680455.403094 1590680468.721302 13.31820797920227y_train_pred6 = model6.predict_classes(Felicia_train, verbose=0)
print('First 3 predictions: ', y_train_pred6[:3])## First 3 predictions: [0 0 0]y_train_pred6 = model6.predict_classes(Felicia_train,
verbose=0)y_train_pred6 = pd.DataFrame(y_train_pred6)
y_train_pred6.columns=['predicted']
y_train6 = Felicia_hits_train
y_train6 = pd.DataFrame(y_train6)
y_train6.columns=['ActualHitsLandedbyX1']
y_train_pred6.index=y_train6.index
Train6=pd.concat([y_train6['ActualHitsLandedbyX1'],y_train_pred6['predicted']],axis=1)
print(Train6)## ActualHitsLandedbyX1 predicted
## 274 0 0
## 453 0 0
## 470 0 0
## 146 0 0
## 208 0 0
## .. ... ...
## 374 0 0
## 103 0 0
## 480 0 0
## 342 0 0
## 221 0 0
##
## [424 rows x 2 columns]y_test_pred6 = model6.predict_classes(Felicia_test,
verbose=0)y_test_pred6 = pd.DataFrame(y_test_pred6)
y_test_pred6.columns=['predicted']
y_test6 = Felicia_hits_test
y_test6 = pd.DataFrame(y_test6)
y_test6.columns=['ActualHitsLandedbyX1']
y_test_pred6.index=y_test6.index
Test6=pd.concat([y_test6['ActualHitsLandedbyX1'],y_test_pred6['predicted']],axis=1)
print(Test6)## ActualHitsLandedbyX1 predicted
## 30 0 0
## 95 0 0
## 72 0 0
## 41 0 0
## 490 0 0
## .. ... ...
## 59 1 0
## 23 1 0
## 264 0 0
## 327 0 0
## 135 0 0
##
## [105 rows x 2 columns]s = sum(Train6['ActualHitsLandedbyX1']==Train6['predicted'])
l = len(Train6['ActualHitsLandedbyX1'])
accTrain6 = s/l
print('Training Correctly Predicted:',s,'Training Accuracy:',accTrain6,'\n')## Training Correctly Predicted: 381 Training Accuracy: 0.8985849056603774y_train_pred6['predicted'].unique()## array([0], dtype=int64)Felicia_hits_train['ActualHitsLandedbyX1'].unique()## array([0, 2, 1], dtype=int64)print(confusion_matrix(Felicia_hits_train, y_train_pred6))## [[381 0 0]
## [ 35 0 0]
## [ 8 0 0]]The training set with validation training for our CNN model only predicted 0 for all possible classes. The accuracy was 89.85% with this run. In it a 3X3 array for the confusion matrix was printed. There were 381 correctly predicted class 0 hits landed, but all 35 class 1 hits landed were predicted to be 0 and all class 2 hits landed were predicted to be 0.
s = sum(Test6['ActualHitsLandedbyX1']==Test6['predicted'])
l = len(Test6['ActualHitsLandedbyX1'])
accTest6 = s/l
print('Testing Correctly Predicted:',s,'Testing Accuracy:',accTest6)## Testing Correctly Predicted: 92 Testing Accuracy: 0.8761904761904762y_test_pred6['predicted'].unique()## array([0], dtype=int64)Felicia_hits_test['ActualHitsLandedbyX1'].unique()## array([0, 1], dtype=int64)print(confusion_matrix(Felicia_hits_test, y_test_pred6))## [[92 0]
## [13 0]]In our CNN model when using it to predict on our testing set, it again predicted all 0s for the class in hits landed by X1, but only had class 0 and class 1 to predict. All 13 class 1 hits landed were misclassified as 0 hits landed.
In summary, the best accuracy in prediction were the Random Forest and Gradient Boosted classifiers in Sklearn of Python and the modified boundaries and rounded class numeric values within those boundaries for the R Caret algorithms random forest and generalized linear models at 96.8% and rpart and gbm with 96.2%.
Later we will put this together to test the accuracy of another fighter or make predictions on hits landed when we switch out Nunez with X2 and run the machine learning models of our best catch accuracy in prediction to see who is more likely to land hits in their upcoming fight June 2, 2020.
In this section we will use Felicia’s hits as X1, but substitute in the X2 fighter with Amanda’s table as x1, using R instead of Python.
Lets read in Amanda’s table with all fights and Felicia’s ML ready table.
AmandaML <- read.csv('Nunez4Fights_addedFeatures.csv', header=T, sep=',', na.strings=c('',' ','NA'))
FeliciaML <- read.csv('Felicia_ml_ready.csv', sep=',', header=T, na.strings=c('',' ','NA'))colnames(AmandaML)## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X1" "cmTotHitsL.X1"
## [7] "cmTotHitsM.X1" "TotLandsX1"
## [9] "TotMissedX1" "TotReceivedX1"
## [11] "cmTotHitsR.X2" "cmTotHitsL.X2"
## [13] "cmTotHitsM.X2" "TotLandsX2"
## [15] "TotMissedX2" "TotReceivedX2"
## [17] "Time" "FighterActionReactions.X1"
## [19] "FightersActionsReactions.X2" "Notes"
## [21] "Crossl.X1" "Kneel.X1"
## [23] "Elbowl.X1" "Hookl.X1"
## [25] "Jabl.X1" "Kickl.X1"
## [27] "upperl.X1" "takedownl.X1"
## [29] "hammerl.X1" "Cross2l.X1"
## [31] "Knee2l.X1" "Elbow2l.X1"
## [33] "Hook2l.X1" "Jab2l.X1"
## [35] "Kick2l.X1" "upper2l.X1"
## [37] "takedown2l.X1" "hammer2l.X1"
## [39] "Cross3l.X1" "Knee3l.X1"
## [41] "Elbow3l.X1" "Hook3l.X1"
## [43] "Jab3l.X1" "Kick3l.X1"
## [45] "upper3l.X1" "takedown3l.X1"
## [47] "hammer3l.X1" "Crossl.X2"
## [49] "Kneel.X2" "Elbowl.X2"
## [51] "Hookl.X2" "Jabl.X2"
## [53] "Kickl.X2" "upperl.X2"
## [55] "takedownl.X2" "hammerl.X2"
## [57] "Cross2l.X2" "Knee2l.X2"
## [59] "Elbow2l.X2" "Hook2l.X2"
## [61] "Jab2l.X2" "Kick2l.X2"
## [63] "upper2l.X2" "takedown2l.X2"
## [65] "hammer2l.X2" "Cross3l.X2"
## [67] "Knee3l.X2" "Elbow3l.X2"
## [69] "Hook3l.X2" "Jab3l.X2"
## [71] "Kick3l.X2" "upper3l.X2"
## [73] "takedown3l.X2" "hammer3l.X2"
## [75] "Crossm.X1" "Kneem.X1"
## [77] "Elbowm.X1" "Hookm.X1"
## [79] "Jabm.X1" "Kickm.X1"
## [81] "upperm.X1" "takedownm.X1"
## [83] "hammerm.X1" "Cross2m.X1"
## [85] "Knee2m.X1" "Elbow2m.X1"
## [87] "Hook2m.X1" "Jab2m.X1"
## [89] "Kick2m.X1" "upper2m.X1"
## [91] "takedown2m.X1" "hammer2m.X1"
## [93] "Cross3m.X1" "Knee3m.X1"
## [95] "Elbow3m.X1" "Hook3m.X1"
## [97] "Jab3m.X1" "Kick3m.X1"
## [99] "upper3m.X1" "takedown3m.X1"
## [101] "hammer3m.X1" "Crossm.X2"
## [103] "Kneem.X2" "Elbowm.X2"
## [105] "Hookm.X2" "Jabm.X2"
## [107] "Kickm.X2" "upperm.X2"
## [109] "takedownm.X2" "hammerm.X2"
## [111] "Cross2m.X2" "Knee2m.X2"
## [113] "Elbow2m.X2" "Hook2m.X2"
## [115] "Jab2m.X2" "Kick2m.X2"
## [117] "upper2m.X2" "takedown2m.X2"
## [119] "hammer2m.X2" "Cross3m.X2"
## [121] "Knee3m.X2" "Elbow3m.X2"
## [123] "Hook3m.X2" "Jab3m.X2"
## [125] "Kick3m.X2" "upper3m.X2"
## [127] "takedown3m.X2" "hammer3m.X2"
## [129] "Crossr.X1" "Kneer.X1"
## [131] "Elbowr.X1" "Hookr.X1"
## [133] "Jabr.X1" "Kickr.X1"
## [135] "upperr.X1" "takedownr.X1"
## [137] "hammerr.X1" "Cross2r.X1"
## [139] "Knee2r.X1" "Elbow2r.X1"
## [141] "Hook2r.X1" "Jab2r.X1"
## [143] "Kick2r.X1" "upper2r.X1"
## [145] "takedown2r.X1" "hammer2r.X1"
## [147] "Cross3r.X1" "Knee3r.X1"
## [149] "Elbow3r.X1" "Hook3r.X1"
## [151] "Jab3r.X1" "Kick3r.X1"
## [153] "upper3r.X1" "takedown3r.X1"
## [155] "hammer3r.X1" "Crossr.X2"
## [157] "Kneer.X2" "Elbowr.X2"
## [159] "Hookr.X2" "Jabr.X2"
## [161] "Kickr.X2" "upperr.X2"
## [163] "takedownr.X2" "hammerr.X2"
## [165] "Cross2r.X2" "Knee2r.X2"
## [167] "Elbow2r.X2" "Hook2r.X2"
## [169] "Jab2r.X2" "Kick2r.X2"
## [171] "upper2r.X2" "takedown2r.X2"
## [173] "hammer2r.X2" "Cross3r.X2"
## [175] "Knee3r.X2" "Elbow3r.X2"
## [177] "Hook3r.X2" "Jab3r.X2"
## [179] "Kick3r.X2" "upper3r.X2"
## [181] "takedown3r.X2" "hammer3r.X2"
## [183] "holdingX1" "holdingX2"
## [185] "breaksHoldX1" "breaksHoldX2"
## [187] "caughtHoldX1" "caughtHoldX2"
## [189] "lostHoldX1" "lostHoldX2"
## [191] "muayThaiKickX1" "muayThaiKickX2"
## [193] "pushKickX1" "pushKickX2"
## [195] "openGuardKickX1" "openGuardKickX2"
## [197] "totalHoldsX1" "totalHoldsX2"
## [199] "totalLostHoldsX1" "totalLostHoldsX2"
## [201] "totalCaughtHoldsX1" "totalCaughtHoldsX2"
## [203] "totalBreakOutHoldsX1" "totalBreakOutHoldsX2"
## [205] "totalMuayThaiKicksX1" "totalMuayThaiKicksX2"
## [207] "totalPushKicksX1" "totalPushKicksX2"
## [209] "totalopenguardKicksX1" "totalopenguardKicksX2"All columns of X2 in AmandaML have ‘X2’ in them, so we can grab only Amanda’s columns from her data by only selecting her opponent, X2, and removing it from her data table, except for the TotLandsX2 (column 14) because we want to switch that as TotLandsX1 for comparing Felicia to Germaine in Amanda’s fight and as a placeholder to predict the outcome when Felicia is substituted for Germaine as an opponent. We will rename column 14 as ‘OPPONENT_Landed’ and rename it after gathering are neccessary columns to TotLandsX1 as a placeholder for Felicia’s predicted hits landed against Amanda as X2.
colnames(AmandaML)[14] <- 'OPPONENT_LANDED'
X2amanda <- grep('X2',colnames(AmandaML))
X2amanda## [1] 11 12 13 15 16 19 48 49 50 51 52 53 54 55 56 57 58 59
## [19] 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 102 103 104
## [37] 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122
## [55] 123 124 125 126 127 128 156 157 158 159 160 161 162 163 164 165 166 167
## [73] 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 184 186 188
## [91] 190 192 194 196 198 200 202 204 206 208 210AmandaX1 <- AmandaML[,-X2amanda]
colnames(AmandaX1)## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X1" "cmTotHitsL.X1"
## [7] "cmTotHitsM.X1" "TotLandsX1"
## [9] "TotMissedX1" "TotReceivedX1"
## [11] "OPPONENT_LANDED" "Time"
## [13] "FighterActionReactions.X1" "Notes"
## [15] "Crossl.X1" "Kneel.X1"
## [17] "Elbowl.X1" "Hookl.X1"
## [19] "Jabl.X1" "Kickl.X1"
## [21] "upperl.X1" "takedownl.X1"
## [23] "hammerl.X1" "Cross2l.X1"
## [25] "Knee2l.X1" "Elbow2l.X1"
## [27] "Hook2l.X1" "Jab2l.X1"
## [29] "Kick2l.X1" "upper2l.X1"
## [31] "takedown2l.X1" "hammer2l.X1"
## [33] "Cross3l.X1" "Knee3l.X1"
## [35] "Elbow3l.X1" "Hook3l.X1"
## [37] "Jab3l.X1" "Kick3l.X1"
## [39] "upper3l.X1" "takedown3l.X1"
## [41] "hammer3l.X1" "Crossm.X1"
## [43] "Kneem.X1" "Elbowm.X1"
## [45] "Hookm.X1" "Jabm.X1"
## [47] "Kickm.X1" "upperm.X1"
## [49] "takedownm.X1" "hammerm.X1"
## [51] "Cross2m.X1" "Knee2m.X1"
## [53] "Elbow2m.X1" "Hook2m.X1"
## [55] "Jab2m.X1" "Kick2m.X1"
## [57] "upper2m.X1" "takedown2m.X1"
## [59] "hammer2m.X1" "Cross3m.X1"
## [61] "Knee3m.X1" "Elbow3m.X1"
## [63] "Hook3m.X1" "Jab3m.X1"
## [65] "Kick3m.X1" "upper3m.X1"
## [67] "takedown3m.X1" "hammer3m.X1"
## [69] "Crossr.X1" "Kneer.X1"
## [71] "Elbowr.X1" "Hookr.X1"
## [73] "Jabr.X1" "Kickr.X1"
## [75] "upperr.X1" "takedownr.X1"
## [77] "hammerr.X1" "Cross2r.X1"
## [79] "Knee2r.X1" "Elbow2r.X1"
## [81] "Hook2r.X1" "Jab2r.X1"
## [83] "Kick2r.X1" "upper2r.X1"
## [85] "takedown2r.X1" "hammer2r.X1"
## [87] "Cross3r.X1" "Knee3r.X1"
## [89] "Elbow3r.X1" "Hook3r.X1"
## [91] "Jab3r.X1" "Kick3r.X1"
## [93] "upper3r.X1" "takedown3r.X1"
## [95] "hammer3r.X1" "holdingX1"
## [97] "breaksHoldX1" "caughtHoldX1"
## [99] "lostHoldX1" "muayThaiKickX1"
## [101] "pushKickX1" "openGuardKickX1"
## [103] "totalHoldsX1" "totalLostHoldsX1"
## [105] "totalCaughtHoldsX1" "totalBreakOutHoldsX1"
## [107] "totalMuayThaiKicksX1" "totalPushKicksX1"
## [109] "totalopenguardKicksX1"We can now substitute the ‘X1’ for ‘X2’ so that Amanda will be X2 to compare to Felicia as X1.
colnames(AmandaX1) <- gsub('X1','X2',colnames(AmandaX1))
colnames(AmandaX1)## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X2" "cmTotHitsL.X2"
## [7] "cmTotHitsM.X2" "TotLandsX2"
## [9] "TotMissedX2" "TotReceivedX2"
## [11] "OPPONENT_LANDED" "Time"
## [13] "FighterActionReactions.X2" "Notes"
## [15] "Crossl.X2" "Kneel.X2"
## [17] "Elbowl.X2" "Hookl.X2"
## [19] "Jabl.X2" "Kickl.X2"
## [21] "upperl.X2" "takedownl.X2"
## [23] "hammerl.X2" "Cross2l.X2"
## [25] "Knee2l.X2" "Elbow2l.X2"
## [27] "Hook2l.X2" "Jab2l.X2"
## [29] "Kick2l.X2" "upper2l.X2"
## [31] "takedown2l.X2" "hammer2l.X2"
## [33] "Cross3l.X2" "Knee3l.X2"
## [35] "Elbow3l.X2" "Hook3l.X2"
## [37] "Jab3l.X2" "Kick3l.X2"
## [39] "upper3l.X2" "takedown3l.X2"
## [41] "hammer3l.X2" "Crossm.X2"
## [43] "Kneem.X2" "Elbowm.X2"
## [45] "Hookm.X2" "Jabm.X2"
## [47] "Kickm.X2" "upperm.X2"
## [49] "takedownm.X2" "hammerm.X2"
## [51] "Cross2m.X2" "Knee2m.X2"
## [53] "Elbow2m.X2" "Hook2m.X2"
## [55] "Jab2m.X2" "Kick2m.X2"
## [57] "upper2m.X2" "takedown2m.X2"
## [59] "hammer2m.X2" "Cross3m.X2"
## [61] "Knee3m.X2" "Elbow3m.X2"
## [63] "Hook3m.X2" "Jab3m.X2"
## [65] "Kick3m.X2" "upper3m.X2"
## [67] "takedown3m.X2" "hammer3m.X2"
## [69] "Crossr.X2" "Kneer.X2"
## [71] "Elbowr.X2" "Hookr.X2"
## [73] "Jabr.X2" "Kickr.X2"
## [75] "upperr.X2" "takedownr.X2"
## [77] "hammerr.X2" "Cross2r.X2"
## [79] "Knee2r.X2" "Elbow2r.X2"
## [81] "Hook2r.X2" "Jab2r.X2"
## [83] "Kick2r.X2" "upper2r.X2"
## [85] "takedown2r.X2" "hammer2r.X2"
## [87] "Cross3r.X2" "Knee3r.X2"
## [89] "Elbow3r.X2" "Hook3r.X2"
## [91] "Jab3r.X2" "Kick3r.X2"
## [93] "upper3r.X2" "takedown3r.X2"
## [95] "hammer3r.X2" "holdingX2"
## [97] "breaksHoldX2" "caughtHoldX2"
## [99] "lostHoldX2" "muayThaiKickX2"
## [101] "pushKickX2" "openGuardKickX2"
## [103] "totalHoldsX2" "totalLostHoldsX2"
## [105] "totalCaughtHoldsX2" "totalBreakOutHoldsX2"
## [107] "totalMuayThaiKicksX2" "totalPushKicksX2"
## [109] "totalopenguardKicksX2"Lets only keep her fight with Germaine, since it’s the only one that Amanda’s wrestling actions were recorded. And all of Felicia’s were recorded.
germaine <- grep('Germaine', AmandaX1$Notes)
AmandaX2 <- AmandaX1[germaine,]
AmandaX2[,14]## [1] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [9] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [17] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [25] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [33] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [41] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [49] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [57] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [65] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [73] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [81] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [89] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [97] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [105] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [113] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [121] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [129] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [137] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [145] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [153] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [161] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [169] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [177] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [185] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [193] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [201] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [209] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [217] Germaine Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## [225] Germaine Germaine Germaine Germaine Germaine Germaine Germaine
## Levels: Germaine Pennington Rousey TateWe should remove the TotReceivedX2 column from this ML table, because it is multicollinear with TotLandsX1 our target, and could be why some of the ML programs scored 100% accuracy.
colnames(FeliciaML)## [1] "SecondsIntoRound" "lastAction" "SecondsLastRoundAction"
## [4] "cmTotHitsR.X1" "cmTotHitsL.X1" "cmTotHitsM.X1"
## [7] "TotLandsX1" "TotMissedX1" "TotReceivedX1"
## [10] "cmTotHitsR.X2" "cmTotHitsL.X2" "cmTotHitsM.X2"
## [13] "TotLandsX2" "TotMissedX2" "TotReceivedX2"
## [16] "Crossl.X2" "Kneel.X2" "Elbowl.X2"
## [19] "Hookl.X2" "Jabl.X2" "Kickl.X2"
## [22] "upperl.X2" "takedownl.X2" "hammerl.X2"
## [25] "Cross2l.X2" "Knee2l.X2" "Elbow2l.X2"
## [28] "Hook2l.X2" "Jab2l.X2" "Kick2l.X2"
## [31] "upper2l.X2" "takedown2l.X2" "hammer2l.X2"
## [34] "Cross3l.X2" "Knee3l.X2" "Elbow3l.X2"
## [37] "Hook3l.X2" "Jab3l.X2" "Kick3l.X2"
## [40] "upper3l.X2" "takedown3l.X2" "hammer3l.X2"
## [43] "Crossm.X2" "Kneem.X2" "Elbowm.X2"
## [46] "Hookm.X2" "Jabm.X2" "Kickm.X2"
## [49] "upperm.X2" "takedownm.X2" "hammerm.X2"
## [52] "Cross2m.X2" "Knee2m.X2" "Elbow2m.X2"
## [55] "Hook2m.X2" "Jab2m.X2" "Kick2m.X2"
## [58] "upper2m.X2" "takedown2m.X2" "hammer2m.X2"
## [61] "Cross3m.X2" "Knee3m.X2" "Elbow3m.X2"
## [64] "Hook3m.X2" "Jab3m.X2" "Kick3m.X2"
## [67] "upper3m.X2" "takedown3m.X2" "hammer3m.X2"
## [70] "holdingX1" "holdingX2" "breaksHoldX1"
## [73] "breaksHoldX2" "caughtHoldX1" "caughtHoldX2"
## [76] "lostHoldX1" "lostHoldX2" "muayThaiKickX1"
## [79] "muayThaiKickX2" "pushKickX1" "pushKickX2"
## [82] "totalHoldsX1" "totalHoldsX2" "totalLostHoldsX1"
## [85] "totalLostHoldsX2" "totalCaughtHoldsX1" "totalCaughtHoldsX2"
## [88] "totalBreakOutHoldsX1" "totalBreakOutHoldsX2" "totalMuayThaiKicksX1"
## [91] "totalMuayThaiKicksX2" "totalPushKicksX1" "totalPushKicksX2"FeliciaML2 <- FeliciaML[,-15]
colnames(FeliciaML2)## [1] "SecondsIntoRound" "lastAction" "SecondsLastRoundAction"
## [4] "cmTotHitsR.X1" "cmTotHitsL.X1" "cmTotHitsM.X1"
## [7] "TotLandsX1" "TotMissedX1" "TotReceivedX1"
## [10] "cmTotHitsR.X2" "cmTotHitsL.X2" "cmTotHitsM.X2"
## [13] "TotLandsX2" "TotMissedX2" "Crossl.X2"
## [16] "Kneel.X2" "Elbowl.X2" "Hookl.X2"
## [19] "Jabl.X2" "Kickl.X2" "upperl.X2"
## [22] "takedownl.X2" "hammerl.X2" "Cross2l.X2"
## [25] "Knee2l.X2" "Elbow2l.X2" "Hook2l.X2"
## [28] "Jab2l.X2" "Kick2l.X2" "upper2l.X2"
## [31] "takedown2l.X2" "hammer2l.X2" "Cross3l.X2"
## [34] "Knee3l.X2" "Elbow3l.X2" "Hook3l.X2"
## [37] "Jab3l.X2" "Kick3l.X2" "upper3l.X2"
## [40] "takedown3l.X2" "hammer3l.X2" "Crossm.X2"
## [43] "Kneem.X2" "Elbowm.X2" "Hookm.X2"
## [46] "Jabm.X2" "Kickm.X2" "upperm.X2"
## [49] "takedownm.X2" "hammerm.X2" "Cross2m.X2"
## [52] "Knee2m.X2" "Elbow2m.X2" "Hook2m.X2"
## [55] "Jab2m.X2" "Kick2m.X2" "upper2m.X2"
## [58] "takedown2m.X2" "hammer2m.X2" "Cross3m.X2"
## [61] "Knee3m.X2" "Elbow3m.X2" "Hook3m.X2"
## [64] "Jab3m.X2" "Kick3m.X2" "upper3m.X2"
## [67] "takedown3m.X2" "hammer3m.X2" "holdingX1"
## [70] "holdingX2" "breaksHoldX1" "breaksHoldX2"
## [73] "caughtHoldX1" "caughtHoldX2" "lostHoldX1"
## [76] "lostHoldX2" "muayThaiKickX1" "muayThaiKickX2"
## [79] "pushKickX1" "pushKickX2" "totalHoldsX1"
## [82] "totalHoldsX2" "totalLostHoldsX1" "totalLostHoldsX2"
## [85] "totalCaughtHoldsX1" "totalCaughtHoldsX2" "totalBreakOutHoldsX1"
## [88] "totalBreakOutHoldsX2" "totalMuayThaiKicksX1" "totalMuayThaiKicksX2"
## [91] "totalPushKicksX1" "totalPushKicksX2"Lets get the X2 columns we want from Amanda as X2’s data table.
listFX2 <- grep('X2',colnames(FeliciaML2))
listFX2b <- colnames(FeliciaML2)[listFX2]
listFX2b## [1] "cmTotHitsR.X2" "cmTotHitsL.X2" "cmTotHitsM.X2"
## [4] "TotLandsX2" "TotMissedX2" "Crossl.X2"
## [7] "Kneel.X2" "Elbowl.X2" "Hookl.X2"
## [10] "Jabl.X2" "Kickl.X2" "upperl.X2"
## [13] "takedownl.X2" "hammerl.X2" "Cross2l.X2"
## [16] "Knee2l.X2" "Elbow2l.X2" "Hook2l.X2"
## [19] "Jab2l.X2" "Kick2l.X2" "upper2l.X2"
## [22] "takedown2l.X2" "hammer2l.X2" "Cross3l.X2"
## [25] "Knee3l.X2" "Elbow3l.X2" "Hook3l.X2"
## [28] "Jab3l.X2" "Kick3l.X2" "upper3l.X2"
## [31] "takedown3l.X2" "hammer3l.X2" "Crossm.X2"
## [34] "Kneem.X2" "Elbowm.X2" "Hookm.X2"
## [37] "Jabm.X2" "Kickm.X2" "upperm.X2"
## [40] "takedownm.X2" "hammerm.X2" "Cross2m.X2"
## [43] "Knee2m.X2" "Elbow2m.X2" "Hook2m.X2"
## [46] "Jab2m.X2" "Kick2m.X2" "upper2m.X2"
## [49] "takedown2m.X2" "hammer2m.X2" "Cross3m.X2"
## [52] "Knee3m.X2" "Elbow3m.X2" "Hook3m.X2"
## [55] "Jab3m.X2" "Kick3m.X2" "upper3m.X2"
## [58] "takedown3m.X2" "hammer3m.X2" "holdingX2"
## [61] "breaksHoldX2" "caughtHoldX2" "lostHoldX2"
## [64] "muayThaiKickX2" "pushKickX2" "totalHoldsX2"
## [67] "totalLostHoldsX2" "totalCaughtHoldsX2" "totalBreakOutHoldsX2"
## [70] "totalMuayThaiKicksX2" "totalPushKicksX2"We want to keep the other information that involves the round, seconds in the round, last action, seconds last round action, and time to combine with the data of Felicia’s when predicting hits landed based on the features we have chosen earlier. We have to get rid of the received hits of X2 and keep the features other than X2’s information. They have ‘r.X2’ in the name, the columns we want removed in Amanda’s data.
listAX2c <- colnames(AmandaX1)
listAX2c## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X2" "cmTotHitsL.X2"
## [7] "cmTotHitsM.X2" "TotLandsX2"
## [9] "TotMissedX2" "TotReceivedX2"
## [11] "OPPONENT_LANDED" "Time"
## [13] "FighterActionReactions.X2" "Notes"
## [15] "Crossl.X2" "Kneel.X2"
## [17] "Elbowl.X2" "Hookl.X2"
## [19] "Jabl.X2" "Kickl.X2"
## [21] "upperl.X2" "takedownl.X2"
## [23] "hammerl.X2" "Cross2l.X2"
## [25] "Knee2l.X2" "Elbow2l.X2"
## [27] "Hook2l.X2" "Jab2l.X2"
## [29] "Kick2l.X2" "upper2l.X2"
## [31] "takedown2l.X2" "hammer2l.X2"
## [33] "Cross3l.X2" "Knee3l.X2"
## [35] "Elbow3l.X2" "Hook3l.X2"
## [37] "Jab3l.X2" "Kick3l.X2"
## [39] "upper3l.X2" "takedown3l.X2"
## [41] "hammer3l.X2" "Crossm.X2"
## [43] "Kneem.X2" "Elbowm.X2"
## [45] "Hookm.X2" "Jabm.X2"
## [47] "Kickm.X2" "upperm.X2"
## [49] "takedownm.X2" "hammerm.X2"
## [51] "Cross2m.X2" "Knee2m.X2"
## [53] "Elbow2m.X2" "Hook2m.X2"
## [55] "Jab2m.X2" "Kick2m.X2"
## [57] "upper2m.X2" "takedown2m.X2"
## [59] "hammer2m.X2" "Cross3m.X2"
## [61] "Knee3m.X2" "Elbow3m.X2"
## [63] "Hook3m.X2" "Jab3m.X2"
## [65] "Kick3m.X2" "upper3m.X2"
## [67] "takedown3m.X2" "hammer3m.X2"
## [69] "Crossr.X2" "Kneer.X2"
## [71] "Elbowr.X2" "Hookr.X2"
## [73] "Jabr.X2" "Kickr.X2"
## [75] "upperr.X2" "takedownr.X2"
## [77] "hammerr.X2" "Cross2r.X2"
## [79] "Knee2r.X2" "Elbow2r.X2"
## [81] "Hook2r.X2" "Jab2r.X2"
## [83] "Kick2r.X2" "upper2r.X2"
## [85] "takedown2r.X2" "hammer2r.X2"
## [87] "Cross3r.X2" "Knee3r.X2"
## [89] "Elbow3r.X2" "Hook3r.X2"
## [91] "Jab3r.X2" "Kick3r.X2"
## [93] "upper3r.X2" "takedown3r.X2"
## [95] "hammer3r.X2" "holdingX2"
## [97] "breaksHoldX2" "caughtHoldX2"
## [99] "lostHoldX2" "muayThaiKickX2"
## [101] "pushKickX2" "openGuardKickX2"
## [103] "totalHoldsX2" "totalLostHoldsX2"
## [105] "totalCaughtHoldsX2" "totalBreakOutHoldsX2"
## [107] "totalMuayThaiKicksX2" "totalPushKicksX2"
## [109] "totalopenguardKicksX2"listAX2d <- grep('r.X2',listAX2c)
listAX2e <- listAX2c[-listAX2d]
listAX2e## [1] "Round" "SecondsIntoRound"
## [3] "lastAction" "SecondsLastRoundAction"
## [5] "cmTotHitsR.X2" "cmTotHitsL.X2"
## [7] "cmTotHitsM.X2" "TotLandsX2"
## [9] "TotMissedX2" "TotReceivedX2"
## [11] "OPPONENT_LANDED" "Time"
## [13] "FighterActionReactions.X2" "Notes"
## [15] "Crossl.X2" "Kneel.X2"
## [17] "Elbowl.X2" "Hookl.X2"
## [19] "Jabl.X2" "Kickl.X2"
## [21] "upperl.X2" "takedownl.X2"
## [23] "hammerl.X2" "Cross2l.X2"
## [25] "Knee2l.X2" "Elbow2l.X2"
## [27] "Hook2l.X2" "Jab2l.X2"
## [29] "Kick2l.X2" "upper2l.X2"
## [31] "takedown2l.X2" "hammer2l.X2"
## [33] "Cross3l.X2" "Knee3l.X2"
## [35] "Elbow3l.X2" "Hook3l.X2"
## [37] "Jab3l.X2" "Kick3l.X2"
## [39] "upper3l.X2" "takedown3l.X2"
## [41] "hammer3l.X2" "Crossm.X2"
## [43] "Kneem.X2" "Elbowm.X2"
## [45] "Hookm.X2" "Jabm.X2"
## [47] "Kickm.X2" "upperm.X2"
## [49] "takedownm.X2" "hammerm.X2"
## [51] "Cross2m.X2" "Knee2m.X2"
## [53] "Elbow2m.X2" "Hook2m.X2"
## [55] "Jab2m.X2" "Kick2m.X2"
## [57] "upper2m.X2" "takedown2m.X2"
## [59] "hammer2m.X2" "Cross3m.X2"
## [61] "Knee3m.X2" "Elbow3m.X2"
## [63] "Hook3m.X2" "Jab3m.X2"
## [65] "Kick3m.X2" "upper3m.X2"
## [67] "takedown3m.X2" "hammer3m.X2"
## [69] "holdingX2" "breaksHoldX2"
## [71] "caughtHoldX2" "lostHoldX2"
## [73] "muayThaiKickX2" "pushKickX2"
## [75] "openGuardKickX2" "totalHoldsX2"
## [77] "totalLostHoldsX2" "totalCaughtHoldsX2"
## [79] "totalBreakOutHoldsX2" "totalMuayThaiKicksX2"
## [81] "totalPushKicksX2" "totalopenguardKicksX2"We should remove the total recieved hits by X2, round, time, notes, and action/reactions columns as well from Amanda’s data.
listAX2f <- listAX2e[-c(1,10,12:14)]
AmandaX2b <- AmandaX2[,listAX2f]
colnames(AmandaX2b)## [1] "SecondsIntoRound" "lastAction" "SecondsLastRoundAction"
## [4] "cmTotHitsR.X2" "cmTotHitsL.X2" "cmTotHitsM.X2"
## [7] "TotLandsX2" "TotMissedX2" "OPPONENT_LANDED"
## [10] "Crossl.X2" "Kneel.X2" "Elbowl.X2"
## [13] "Hookl.X2" "Jabl.X2" "Kickl.X2"
## [16] "upperl.X2" "takedownl.X2" "hammerl.X2"
## [19] "Cross2l.X2" "Knee2l.X2" "Elbow2l.X2"
## [22] "Hook2l.X2" "Jab2l.X2" "Kick2l.X2"
## [25] "upper2l.X2" "takedown2l.X2" "hammer2l.X2"
## [28] "Cross3l.X2" "Knee3l.X2" "Elbow3l.X2"
## [31] "Hook3l.X2" "Jab3l.X2" "Kick3l.X2"
## [34] "upper3l.X2" "takedown3l.X2" "hammer3l.X2"
## [37] "Crossm.X2" "Kneem.X2" "Elbowm.X2"
## [40] "Hookm.X2" "Jabm.X2" "Kickm.X2"
## [43] "upperm.X2" "takedownm.X2" "hammerm.X2"
## [46] "Cross2m.X2" "Knee2m.X2" "Elbow2m.X2"
## [49] "Hook2m.X2" "Jab2m.X2" "Kick2m.X2"
## [52] "upper2m.X2" "takedown2m.X2" "hammer2m.X2"
## [55] "Cross3m.X2" "Knee3m.X2" "Elbow3m.X2"
## [58] "Hook3m.X2" "Jab3m.X2" "Kick3m.X2"
## [61] "upper3m.X2" "takedown3m.X2" "hammer3m.X2"
## [64] "holdingX2" "breaksHoldX2" "caughtHoldX2"
## [67] "lostHoldX2" "muayThaiKickX2" "pushKickX2"
## [70] "openGuardKickX2" "totalHoldsX2" "totalLostHoldsX2"
## [73] "totalCaughtHoldsX2" "totalBreakOutHoldsX2" "totalMuayThaiKicksX2"
## [76] "totalPushKicksX2" "totalopenguardKicksX2"We removed the wrestling holds of X1 from the table of Amanda’s because we are assuming those holds wouldn’t align with Felicia’s actions or reactions, and so we must also remove the X1 holds from Felicia’s table and exclude these features in predicting hits landed by Felicia when up against Amanda’s actions as X2. We want to exclude all columns with ‘X1’ except TotLandsX1 that is column 7 listed as the 4th index of columns with ‘X1’.
colnames(FeliciaML2)## [1] "SecondsIntoRound" "lastAction" "SecondsLastRoundAction"
## [4] "cmTotHitsR.X1" "cmTotHitsL.X1" "cmTotHitsM.X1"
## [7] "TotLandsX1" "TotMissedX1" "TotReceivedX1"
## [10] "cmTotHitsR.X2" "cmTotHitsL.X2" "cmTotHitsM.X2"
## [13] "TotLandsX2" "TotMissedX2" "Crossl.X2"
## [16] "Kneel.X2" "Elbowl.X2" "Hookl.X2"
## [19] "Jabl.X2" "Kickl.X2" "upperl.X2"
## [22] "takedownl.X2" "hammerl.X2" "Cross2l.X2"
## [25] "Knee2l.X2" "Elbow2l.X2" "Hook2l.X2"
## [28] "Jab2l.X2" "Kick2l.X2" "upper2l.X2"
## [31] "takedown2l.X2" "hammer2l.X2" "Cross3l.X2"
## [34] "Knee3l.X2" "Elbow3l.X2" "Hook3l.X2"
## [37] "Jab3l.X2" "Kick3l.X2" "upper3l.X2"
## [40] "takedown3l.X2" "hammer3l.X2" "Crossm.X2"
## [43] "Kneem.X2" "Elbowm.X2" "Hookm.X2"
## [46] "Jabm.X2" "Kickm.X2" "upperm.X2"
## [49] "takedownm.X2" "hammerm.X2" "Cross2m.X2"
## [52] "Knee2m.X2" "Elbow2m.X2" "Hook2m.X2"
## [55] "Jab2m.X2" "Kick2m.X2" "upper2m.X2"
## [58] "takedown2m.X2" "hammer2m.X2" "Cross3m.X2"
## [61] "Knee3m.X2" "Elbow3m.X2" "Hook3m.X2"
## [64] "Jab3m.X2" "Kick3m.X2" "upper3m.X2"
## [67] "takedown3m.X2" "hammer3m.X2" "holdingX1"
## [70] "holdingX2" "breaksHoldX1" "breaksHoldX2"
## [73] "caughtHoldX1" "caughtHoldX2" "lostHoldX1"
## [76] "lostHoldX2" "muayThaiKickX1" "muayThaiKickX2"
## [79] "pushKickX1" "pushKickX2" "totalHoldsX1"
## [82] "totalHoldsX2" "totalLostHoldsX1" "totalLostHoldsX2"
## [85] "totalCaughtHoldsX1" "totalCaughtHoldsX2" "totalBreakOutHoldsX1"
## [88] "totalBreakOutHoldsX2" "totalMuayThaiKicksX1" "totalMuayThaiKicksX2"
## [91] "totalPushKicksX1" "totalPushKicksX2"listFa <- grep('X1',colnames(FeliciaML2))
listFa## [1] 4 5 6 7 8 9 69 71 73 75 77 79 81 83 85 87 89 91listFb <- listFa[-4]
listFb## [1] 4 5 6 8 9 69 71 73 75 77 79 81 83 85 87 89 91FeliciaML3 <- FeliciaML2[,-listFb]
colnames(FeliciaML3)## [1] "SecondsIntoRound" "lastAction" "SecondsLastRoundAction"
## [4] "TotLandsX1" "cmTotHitsR.X2" "cmTotHitsL.X2"
## [7] "cmTotHitsM.X2" "TotLandsX2" "TotMissedX2"
## [10] "Crossl.X2" "Kneel.X2" "Elbowl.X2"
## [13] "Hookl.X2" "Jabl.X2" "Kickl.X2"
## [16] "upperl.X2" "takedownl.X2" "hammerl.X2"
## [19] "Cross2l.X2" "Knee2l.X2" "Elbow2l.X2"
## [22] "Hook2l.X2" "Jab2l.X2" "Kick2l.X2"
## [25] "upper2l.X2" "takedown2l.X2" "hammer2l.X2"
## [28] "Cross3l.X2" "Knee3l.X2" "Elbow3l.X2"
## [31] "Hook3l.X2" "Jab3l.X2" "Kick3l.X2"
## [34] "upper3l.X2" "takedown3l.X2" "hammer3l.X2"
## [37] "Crossm.X2" "Kneem.X2" "Elbowm.X2"
## [40] "Hookm.X2" "Jabm.X2" "Kickm.X2"
## [43] "upperm.X2" "takedownm.X2" "hammerm.X2"
## [46] "Cross2m.X2" "Knee2m.X2" "Elbow2m.X2"
## [49] "Hook2m.X2" "Jab2m.X2" "Kick2m.X2"
## [52] "upper2m.X2" "takedown2m.X2" "hammer2m.X2"
## [55] "Cross3m.X2" "Knee3m.X2" "Elbow3m.X2"
## [58] "Hook3m.X2" "Jab3m.X2" "Kick3m.X2"
## [61] "upper3m.X2" "takedown3m.X2" "hammer3m.X2"
## [64] "holdingX2" "breaksHoldX2" "caughtHoldX2"
## [67] "lostHoldX2" "muayThaiKickX2" "pushKickX2"
## [70] "totalHoldsX2" "totalLostHoldsX2" "totalCaughtHoldsX2"
## [73] "totalBreakOutHoldsX2" "totalMuayThaiKicksX2" "totalPushKicksX2"colnames(AmandaX2b)## [1] "SecondsIntoRound" "lastAction" "SecondsLastRoundAction"
## [4] "cmTotHitsR.X2" "cmTotHitsL.X2" "cmTotHitsM.X2"
## [7] "TotLandsX2" "TotMissedX2" "OPPONENT_LANDED"
## [10] "Crossl.X2" "Kneel.X2" "Elbowl.X2"
## [13] "Hookl.X2" "Jabl.X2" "Kickl.X2"
## [16] "upperl.X2" "takedownl.X2" "hammerl.X2"
## [19] "Cross2l.X2" "Knee2l.X2" "Elbow2l.X2"
## [22] "Hook2l.X2" "Jab2l.X2" "Kick2l.X2"
## [25] "upper2l.X2" "takedown2l.X2" "hammer2l.X2"
## [28] "Cross3l.X2" "Knee3l.X2" "Elbow3l.X2"
## [31] "Hook3l.X2" "Jab3l.X2" "Kick3l.X2"
## [34] "upper3l.X2" "takedown3l.X2" "hammer3l.X2"
## [37] "Crossm.X2" "Kneem.X2" "Elbowm.X2"
## [40] "Hookm.X2" "Jabm.X2" "Kickm.X2"
## [43] "upperm.X2" "takedownm.X2" "hammerm.X2"
## [46] "Cross2m.X2" "Knee2m.X2" "Elbow2m.X2"
## [49] "Hook2m.X2" "Jab2m.X2" "Kick2m.X2"
## [52] "upper2m.X2" "takedown2m.X2" "hammer2m.X2"
## [55] "Cross3m.X2" "Knee3m.X2" "Elbow3m.X2"
## [58] "Hook3m.X2" "Jab3m.X2" "Kick3m.X2"
## [61] "upper3m.X2" "takedown3m.X2" "hammer3m.X2"
## [64] "holdingX2" "breaksHoldX2" "caughtHoldX2"
## [67] "lostHoldX2" "muayThaiKickX2" "pushKickX2"
## [70] "openGuardKickX2" "totalHoldsX2" "totalLostHoldsX2"
## [73] "totalCaughtHoldsX2" "totalBreakOutHoldsX2" "totalMuayThaiKicksX2"
## [76] "totalPushKicksX2" "totalopenguardKicksX2"We also need to remove the open guard kicks, that were exclusive only to Amanda’s fight with Germaine, and not in Felicia’s table. Note that Felicia also likes to do flying elbows and we didn’t add that feature to her table nor Amanda’s table, but it was included as a hit landed/missed for elbow as is. Also, change the placeholder for X1 hits landed to TotLandsX1. Also, reorder so that TotLandsX1 is before cmTotHitsR.X2 in column order to align with Felicia’s data table.
list3 <- grep('open', colnames(AmandaX2b))
AmandaX2c <- AmandaX2b[,-list3]
colnames(AmandaX2c)[9] <- 'TotLandsX1'
AmandaX2d <- AmandaX2c[,c(1:3,9,4:8,10:75)]
colnames(AmandaX2d)## [1] "SecondsIntoRound" "lastAction" "SecondsLastRoundAction"
## [4] "TotLandsX1" "cmTotHitsR.X2" "cmTotHitsL.X2"
## [7] "cmTotHitsM.X2" "TotLandsX2" "TotMissedX2"
## [10] "Crossl.X2" "Kneel.X2" "Elbowl.X2"
## [13] "Hookl.X2" "Jabl.X2" "Kickl.X2"
## [16] "upperl.X2" "takedownl.X2" "hammerl.X2"
## [19] "Cross2l.X2" "Knee2l.X2" "Elbow2l.X2"
## [22] "Hook2l.X2" "Jab2l.X2" "Kick2l.X2"
## [25] "upper2l.X2" "takedown2l.X2" "hammer2l.X2"
## [28] "Cross3l.X2" "Knee3l.X2" "Elbow3l.X2"
## [31] "Hook3l.X2" "Jab3l.X2" "Kick3l.X2"
## [34] "upper3l.X2" "takedown3l.X2" "hammer3l.X2"
## [37] "Crossm.X2" "Kneem.X2" "Elbowm.X2"
## [40] "Hookm.X2" "Jabm.X2" "Kickm.X2"
## [43] "upperm.X2" "takedownm.X2" "hammerm.X2"
## [46] "Cross2m.X2" "Knee2m.X2" "Elbow2m.X2"
## [49] "Hook2m.X2" "Jab2m.X2" "Kick2m.X2"
## [52] "upper2m.X2" "takedown2m.X2" "hammer2m.X2"
## [55] "Cross3m.X2" "Knee3m.X2" "Elbow3m.X2"
## [58] "Hook3m.X2" "Jab3m.X2" "Kick3m.X2"
## [61] "upper3m.X2" "takedown3m.X2" "hammer3m.X2"
## [64] "holdingX2" "breaksHoldX2" "caughtHoldX2"
## [67] "lostHoldX2" "muayThaiKickX2" "pushKickX2"
## [70] "totalHoldsX2" "totalLostHoldsX2" "totalCaughtHoldsX2"
## [73] "totalBreakOutHoldsX2" "totalMuayThaiKicksX2" "totalPushKicksX2"Lets check that the columns are aligned the same.
colnames(AmandaX2d)==colnames(FeliciaML3)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEThey are aligned the same, so we will make Felicia’s table the training set, and Amanda’s data table the testing set to see how Felicia’s actions compare to Amanda’s. We will use the best performing ML algorithms in R and Python. Note that we need to validate the data in training the model of Felicia’s actions and reactions with our new set of features but keeping the same target feature of hits landed of Felicia’s. We errored earlier in evaluating the algorithms on Felicia when including the total hits received by X2, a multicollinear (identical or identical partial units to each other i.e. x1=(1/3)x2 or x1=x2) vector or feature to our target of TotHitsLandX1 some of the decision tree algorithms might have picked up on this hence their fast computation time and near or at 100% accuracy in prediction. We will rerun those algorithms adjusting for the new data table that omits that multicollinear variable.
First lets save the data as we want them to recall them later with ease.
TrainingSetFelicia <- FeliciaML3
TestingSetAmanda <- AmandaX2dLets write these tables out to use in python’s ML algorithms later.
write.csv(TrainingSetFelicia,'FeliciaM-asX2-L-v2.csv',row.names=FALSE)
write.csv(TestingSetAmanda,'AmandaML-asX1-v2.csv', row.names=FALSE)dim(TrainingSetFelicia)## [1] 529 75dim(TestingSetAmanda)## [1] 231 75231/529## [1] 0.436673The ratio of samples to build the training model is about 56 to 44 training to testing split. This is only as a comparison. Where we will just sum up the hits landed as predicted by Felicia’s training model to the sum of hits landed as the actual hits landed by Amanda’s opponent Germaine. We have that actual value already as follows:
AmandasOpponentsHitsLanded <- sum(TestingSetAmanda$TotLandsX1)
AmandasOpponentsHitsLanded## [1] 13So, for this data there are 13 hits landed by the opponent we are comparing Felicia’s predicted hits landed to, after building her prediction model.
Lets split the data into a 70% training set and a 30% testing set.
set.seed(12345)
inTrain <- createDataPartition(y=TrainingSetFelicia$TotLandsX1, p=0.7, list=FALSE)
trainingSet <- TrainingSetFelicia[inTrain,]
testingSet <- TrainingSetFelicia[-inTrain,]Lets get the dimensions of the testing and training set to see how many observations are in each subset of our data.
dim(testingSet)## [1] 158 75dim(trainingSet)## [1] 371 75library(caret)We will test the random forest model of R’s caret package used earlier to score 96.8% accuracy.
rf_cv15 <- train(TotLandsX1~., method='rf',
na.action=na.pass,
data=(trainingSet), preProc = c("center", "scale"),
trControl=trainControl(method='cv', classProbs = T), number=15)predRF_cv15 <- predict(rf_cv15, testingSet)
DF_cv15 <- data.frame(predRF_cv15, ActualHitsLanded=testingSet$TotLandsX1)
length_cv15 <- length(DF_cv15$ActualHitsLanded)
sum_cv15 <- sum(DF_cv15$predRF_boot==DF_cv15$ActualHitsLanded)
accRF_cv15 <- (sum_cv15/length_cv15)
accRF_cv15## [1] 0head(DF_cv15,30)## predRF_cv15 ActualHitsLanded
## 2 0.1154049 0
## 4 0.1273436 0
## 11 0.1159029 0
## 19 0.1212142 0
## 20 0.1079231 0
## 22 0.1211210 0
## 26 0.1223981 0
## 28 0.1161997 0
## 29 0.1163701 0
## 30 0.1163701 0
## 33 0.1131449 0
## 39 0.1127840 0
## 43 0.1117226 0
## 45 0.1117226 0
## 48 0.1117226 0
## 50 0.1117226 0
## 54 0.1117226 0
## 64 0.1143172 0
## 69 0.1141623 0
## 70 0.1141623 0
## 84 0.1134518 0
## 85 0.1134518 0
## 87 0.1135843 0
## 89 0.1148826 0
## 95 0.1191483 0
## 96 0.1173503 0
## 97 0.1173503 1
## 99 0.1158176 0
## 100 0.1214371 0
## 102 0.1173503 0DF_cv15$roundedPrediction <- ifelse(DF_cv15$predRF_cv15 < 0, 0,
ifelse(DF_cv15$predRF_cv15 > 2, 2,
round(DF_cv15$predRF_cv15)
)
)
DF_cv15$Correct <- ifelse(DF_cv15$ActualHitsLanded==DF_cv15$roundedPrediction,1,0)
accuracy1 <- sum(DF_cv15$Correct)/length(DF_cv15$Correct)
accuracy1## [1] 0.9050633head(DF_cv15)## predRF_cv15 ActualHitsLanded roundedPrediction Correct
## 2 0.1154049 0 0 1
## 4 0.1273436 0 0 1
## 11 0.1159029 0 0 1
## 19 0.1212142 0 0 1
## 20 0.1079231 0 0 1
## 22 0.1211210 0 0 1Now we’ll test the generalized linear model of R’s caret package that also scored 96.8% after modifications for boundaries and rounding.
glmMod2 <- train(TotLandsX1 ~ .,
method='glm', data=trainingSet)predglm2 <- predict(glmMod2, testingSet)
DF_glm2 <- data.frame(predglm2, ActualHitsLanded=testingSet$TotLandsX1)
length_glm2 <- length(DF_glm2$ActualHitsLanded)
sum_glm2 <- sum(DF_glm2$predglm2==DF_glm2$ActualHitsLanded)
accglm2 <- (sum_glm2/length_glm2)
accglm2## [1] 0head(DF_glm2)## predglm2 ActualHitsLanded
## 2 -0.1043763 0
## 4 0.1044623 0
## 11 -0.0188745 0
## 19 0.4053709 0
## 20 0.1050554 0
## 22 0.2850252 0DF_glm2$roundedPrediction <- ifelse(DF_glm2$predglm2<0,0,
ifelse(DF_glm2$predglm2>2,2,
round(DF_glm2$predglm2,0)))
DF_glm2$Correct <- ifelse(DF_glm2$ActualHitsLanded==DF_glm2$roundedPrediction,1,0)
accuracy4 <- sum(DF_glm2$Correct)/length(DF_glm2$Correct)
accuracy4## [1] 0.8987342head(DF_glm2)## predglm2 ActualHitsLanded roundedPrediction Correct
## 2 -0.1043763 0 0 1
## 4 0.1044623 0 0 1
## 11 -0.0188745 0 0 1
## 19 0.4053709 0 0 1
## 20 0.1050554 0 0 1
## 22 0.2850252 0 0 1With our adjusted data when validating the prediction of hits landed for Felicia in the reduced and corrected feature data, the Random Forest and Generalized Linear Models scored 90.5% and 89.9% accuracy in prediction respectively.
Now, lets use the random forest and gradient boosted models of python’s sklearn package to test the new data of Felicia’s when comparing actions to Amanda’s later.
library(reticulate)conda_list(conda = "auto") ## name python
## 1 Anaconda2 C:\\Users\\m\\Anaconda2\\python.exe
## 2 djangoenv C:\\Users\\m\\Anaconda2\\envs\\djangoenv\\python.exe
## 3 python36 C:\\Users\\m\\Anaconda2\\envs\\python36\\python.exe
## 4 python37 C:\\Users\\m\\Anaconda2\\envs\\python37\\python.exe
## 5 r-reticulate C:\\Users\\m\\Anaconda2\\envs\\r-reticulate\\python.exeuse_condaenv(condaenv = "python36")import pandas as pd
import sklearn
import numpy as np
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
np.random.seed(47) Import our ML ready file to run the python algorithms on with sklearn.
Felicia = pd.read_csv('FeliciaM-asX2-L-v2.csv', encoding = 'unicode_escape')
print(Felicia.shape)## (529, 75)print(Felicia.columns)## Index(['SecondsIntoRound', 'lastAction', 'SecondsLastRoundAction',
## 'TotLandsX1', 'cmTotHitsR.X2', 'cmTotHitsL.X2', 'cmTotHitsM.X2',
## 'TotLandsX2', 'TotMissedX2', 'Crossl.X2', 'Kneel.X2', 'Elbowl.X2',
## 'Hookl.X2', 'Jabl.X2', 'Kickl.X2', 'upperl.X2', 'takedownl.X2',
## 'hammerl.X2', 'Cross2l.X2', 'Knee2l.X2', 'Elbow2l.X2', 'Hook2l.X2',
## 'Jab2l.X2', 'Kick2l.X2', 'upper2l.X2', 'takedown2l.X2', 'hammer2l.X2',
## 'Cross3l.X2', 'Knee3l.X2', 'Elbow3l.X2', 'Hook3l.X2', 'Jab3l.X2',
## 'Kick3l.X2', 'upper3l.X2', 'takedown3l.X2', 'hammer3l.X2', 'Crossm.X2',
## 'Kneem.X2', 'Elbowm.X2', 'Hookm.X2', 'Jabm.X2', 'Kickm.X2', 'upperm.X2',
## 'takedownm.X2', 'hammerm.X2', 'Cross2m.X2', 'Knee2m.X2', 'Elbow2m.X2',
## 'Hook2m.X2', 'Jab2m.X2', 'Kick2m.X2', 'upper2m.X2', 'takedown2m.X2',
## 'hammer2m.X2', 'Cross3m.X2', 'Knee3m.X2', 'Elbow3m.X2', 'Hook3m.X2',
## 'Jab3m.X2', 'Kick3m.X2', 'upper3m.X2', 'takedown3m.X2', 'hammer3m.X2',
## 'holdingX2', 'breaksHoldX2', 'caughtHoldX2', 'lostHoldX2',
## 'muayThaiKickX2', 'pushKickX2', 'totalHoldsX2', 'totalLostHoldsX2',
## 'totalCaughtHoldsX2', 'totalBreakOutHoldsX2', 'totalMuayThaiKicksX2',
## 'totalPushKicksX2'],
## dtype='object')print(Felicia.head())## SecondsIntoRound lastAction ... totalMuayThaiKicksX2 totalPushKicksX2
## 0 4 0 ... 0 0
## 1 5 4 ... 0 0
## 2 9 5 ... 0 0
## 3 11 9 ... 0 0
## 4 13 11 ... 0 0
##
## [5 rows x 75 columns]print(Felicia.tail())## SecondsIntoRound lastAction ... totalMuayThaiKicksX2 totalPushKicksX2
## 524 295 294 ... 9 0
## 525 296 295 ... 9 0
## 526 297 296 ... 9 0
## 527 298 297 ... 9 0
## 528 299 298 ... 9 0
##
## [5 rows x 75 columns]print(Felicia['TotLandsX1'].unique())## [0 1 2]Reorder the rows as instances randomised to split into train and test sets of the data.
import numpy as np
Felicia = Felicia.reindex(np.random.permutation(Felicia.index))
print(Felicia.head())## SecondsIntoRound lastAction ... totalMuayThaiKicksX2 totalPushKicksX2
## 274 124 123 ... 0 0
## 453 196 187 ... 7 0
## 470 241 240 ... 9 0
## 146 194 193 ... 0 0
## 208 56 55 ... 0 0
##
## [5 rows x 75 columns]print(Felicia.tail())## SecondsIntoRound lastAction ... totalMuayThaiKicksX2 totalPushKicksX2
## 59 107 106 ... 0 0
## 23 70 69 ... 0 0
## 264 114 113 ... 0 0
## 327 177 176 ... 0 0
## 135 183 182 ... 0 0
##
## [5 rows x 75 columns]There are 529 instances in this data, and 80% is about 424, the target is the hits landed by X1.
# Split/splice into training ~ 80% and testing ~ 20%
Felicia_train = Felicia[:424]
Felicia_test = Felicia[424:]
Felicia_hits_train = Felicia['TotLandsX1'][:424]
Felicia_hits_test = Felicia['TotLandsX1'][424:]
print(Felicia_train.shape)## (424, 75)print(Felicia_test.shape)## (105, 75)Python’s Sklearn’s Random Forest and Gradient Boosting Classifier results
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import precision_recall_fscore_support as score
import timerf=RandomForestClassifier(n_estimators=150, max_depth=None, n_jobs=-1)
start=time.time()
rf_model=rf.fit(Felicia_train,Felicia_hits_train)
end=time.time()
fit_time=(end-start)
fit_time## 0.513810396194458start=time.time()
y_pred=rf_model.predict(Felicia_test)
end=time.time()
pred_time=(end-start)
pred_time## 0.2555067539215088prd = pd.DataFrame(y_pred)
prd.columns=['Predicted']
prd.index=Felicia_hits_test.index
pred=pd.concat([pd.DataFrame(prd),Felicia_hits_test],axis=1)
print(pred)## Predicted TotLandsX1
## 30 0 0
## 95 0 0
## 72 0 0
## 41 0 0
## 490 0 0
## .. ... ...
## 59 1 1
## 23 1 1
## 264 0 0
## 327 0 0
## 135 0 0
##
## [105 rows x 2 columns]accuracy_Felicia_valid_RF = sum(pred['Predicted']==pred['TotLandsX1'])/len(pred['Predicted'])
accuracy_Felicia_valid_RF## 0.9904761904761905The python Random Forest Classifier scored 99% accuracy in prediction for Felicia’s actions and reactions. This is a great measure and score to compare her model to Amanda’s later.
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
print('accuracy', accuracy_score(Felicia_hits_test, y_pred))## accuracy 0.9904761904761905print('confusion matrix')## confusion matrixprint(confusion_matrix(Felicia_hits_test, y_pred))## [[92 0]
## [ 1 12]]The above confusion matrix for the random forest classifier for Felicia shows that there weren’t any class 2 in the testing set, and that 12/13 1s were classified correctly and that all 0s were classified correctly but that one 1 value for hits landed was classified as a 0 hits landed. The rows are the expected or actual values and the columns are the predicted values in order top to bottom or left to right as lowest to highest value numerically.
Python’s Sklearn’s Gradient Boosted Model
gb=GradientBoostingClassifier(n_estimators=150,max_depth=11)
start=time.time()
gb_model=gb.fit(Felicia_train,Felicia_hits_train)
end=time.time()
fit_time=(end-start)
fit_time## 0.6853897571563721start=time.time()
y_pred=gb_model.predict(Felicia_test)
end=time.time()
pred_time=(end-start)
pred_time## 0.06401610374450684prd = pd.DataFrame(y_pred)
prd.columns=['Predicted']
prd.index=Felicia_hits_test.index
pred=pd.concat([pd.DataFrame(prd),Felicia_hits_test],axis=1)
print(pred)## Predicted TotLandsX1
## 30 0 0
## 95 0 0
## 72 0 0
## 41 0 0
## 490 0 0
## .. ... ...
## 59 1 1
## 23 1 1
## 264 0 0
## 327 0 0
## 135 0 0
##
## [105 rows x 2 columns]accuracy_Felicia_valid_GB = sum(pred['Predicted']==pred['TotLandsX1'])/len(pred['Predicted'])
accuracy_Felicia_valid_GB## 1.0The python Gradient Boosted Classifier scored 100% accuracy in prediction for Felicia’s actions and reactions. This is a great measure and score to compare her model to Amanda’s later.
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
print('accuracy', accuracy_score(Felicia_hits_test, y_pred))## accuracy 1.0print('confusion matrix')## confusion matrixprint(confusion_matrix(Felicia_hits_test, y_pred))## [[92 0]
## [ 0 13]]Now that we have validated our model and show that our new limited data of Felicia’s actions and reactions are 90-100% accurate using R we scored 90% with random forest and generalized linear modeling, and with Python we scored 99-100% with random forest and gradient boosted classifiers, we can start comparing how well Felicia would do when facing Amanda as an opponent.
We are going to use R to show the predictions of hit landed for Felicia against Amanda using random forest and glm as we just did in validating Felicia’s prediction model.
# library(caret)We will test the random forest model of R’s caret package used earlier to score 96.8% accuracy.
rf_cv15 <- train(TotLandsX1~., method='rf',
na.action=na.pass,
data=(TrainingSetFelicia), preProc = c("center", "scale"),
trControl=trainControl(method='cv', classProbs = T), number=15)predRF_cv15 <- predict(rf_cv15, TestingSetAmanda)
DF_cv15 <- data.frame(predRF_cv15, ActualHitsLanded=TestingSetAmanda$TotLandsX1)
length_cv15 <- length(DF_cv15$ActualHitsLanded)
sum_cv15 <- sum(DF_cv15$predRF_boot==DF_cv15$ActualHitsLanded)
accRF_cv15 <- (sum_cv15/length_cv15)
accRF_cv15## [1] 0head(DF_cv15,30)## predRF_cv15 ActualHitsLanded
## 1 0.13117920 0
## 2 0.11432451 0
## 3 0.12668851 0
## 4 0.12997718 0
## 5 0.11149705 0
## 6 0.10345997 1
## 7 0.10615724 0
## 8 0.12194363 1
## 9 0.12180125 0
## 10 0.12189502 0
## 11 0.12189502 0
## 12 0.12419176 0
## 13 0.12014195 0
## 14 0.11222695 0
## 15 0.12017917 0
## 16 0.13461567 0
## 17 0.09863531 0
## 18 0.10762622 0
## 19 0.11633216 0
## 20 0.11062021 0
## 21 0.11578408 0
## 22 0.11515224 0
## 23 0.11493792 0
## 24 0.11493792 0
## 25 0.11493792 0
## 26 0.11493792 0
## 27 0.11493792 0
## 28 0.11493792 0
## 29 0.11493792 0
## 30 0.10738488 0DF_cv15$roundedPrediction <- ifelse(DF_cv15$predRF_cv15 < 0, 0,
ifelse(DF_cv15$predRF_cv15 > 2, 2,
round(DF_cv15$predRF_cv15)
)
)
DF_cv15$Correct <- ifelse(DF_cv15$ActualHitsLanded==DF_cv15$roundedPrediction,1,0)
accuracy1 <- sum(DF_cv15$Correct)/length(DF_cv15$Correct)
accuracy1## [1] 0.965368head(DF_cv15)## predRF_cv15 ActualHitsLanded roundedPrediction Correct
## 1 0.1311792 0 0 1
## 2 0.1143245 0 0 1
## 3 0.1266885 0 0 1
## 4 0.1299772 0 0 1
## 5 0.1114970 0 0 1
## 6 0.1034600 1 0 0sum(DF_cv15$roundedPrediction)## [1] 0unique(DF_cv15$roundedPrediction)## [1] 0Even though the prediction accuracy for Felicia’s hits landed compared to Amanda’s opponent Germaine scored a 96% accuracy with random forest, it predicted all hits landed by Felicia to be 0. And recall that the hits landed by Germaine were 13/231. So, it looks like our model using R’s random forest for Felicia’s training model has a 90% chance that Felicia’s hits landed when compared to hits landed by Germaine to Amanda of 96% chance of not landing one hit. Germaine and Felicia do have different fighting styles as Germaine’s was mostly on the ground.
Lets see how the generalized linear model scores Felicia’s hits landed with Amanda, or how it predicts a hit landed by Felicia.
glmMod2 <- train(TotLandsX1 ~ .,
method='glm', data=TrainingSetFelicia)predglm2 <- predict(glmMod2, TestingSetAmanda)
DF_glm2 <- data.frame(predglm2, ActualHitsLanded=TestingSetAmanda$TotLandsX1)
length_glm2 <- length(DF_glm2$ActualHitsLanded)
sum_glm2 <- sum(DF_glm2$predglm2==DF_glm2$ActualHitsLanded)
accglm2 <- (sum_glm2/length_glm2)
accglm2## [1] 0head(DF_glm2)## predglm2 ActualHitsLanded
## 1 0.087128987 0
## 2 -0.070072193 0
## 3 0.048109374 0
## 4 0.124529280 0
## 5 0.003080329 0
## 6 -0.135513966 1DF_glm2$roundedPrediction <- ifelse(DF_glm2$predglm2<0,0,
ifelse(DF_glm2$predglm2>2,2,
round(DF_glm2$predglm2,0)))
DF_glm2$Correct <- ifelse(DF_glm2$ActualHitsLanded==DF_glm2$roundedPrediction,1,0)
accuracy4 <- sum(DF_glm2$Correct)/length(DF_glm2$Correct)
accuracy4## [1] 0.961039head(DF_glm2)## predglm2 ActualHitsLanded roundedPrediction Correct
## 1 0.087128987 0 0 1
## 2 -0.070072193 0 0 1
## 3 0.048109374 0 0 1
## 4 0.124529280 0 0 1
## 5 0.003080329 0 0 1
## 6 -0.135513966 1 0 0sum(DF_glm2$roundedPrediction)## [1] 1unique(DF_glm2$roundedPrediction)## [1] 0 1The GLM model scored 96% accuracy in prediction, similarly as with random forest, but in this model it predicted one instance where Felicia will land a hit out of 231. Lets see what instance that is.
hit <- DF_glm2$roundedPrediction==1
FeliciaHit <- TestingSetAmanda[hit,]
FeliciaHit## SecondsIntoRound lastAction SecondsLastRoundAction TotLandsX1 cmTotHitsR.X2
## 32 88 87 1 0 2
## cmTotHitsL.X2 cmTotHitsM.X2 TotLandsX2 TotMissedX2 Crossl.X2 Kneel.X2
## 32 4 14 0 2 0 0
## Elbowl.X2 Hookl.X2 Jabl.X2 Kickl.X2 upperl.X2 takedownl.X2 hammerl.X2
## 32 0 0 0 0 0 0 0
## Cross2l.X2 Knee2l.X2 Elbow2l.X2 Hook2l.X2 Jab2l.X2 Kick2l.X2 upper2l.X2
## 32 0 0 0 0 0 0 0
## takedown2l.X2 hammer2l.X2 Cross3l.X2 Knee3l.X2 Elbow3l.X2 Hook3l.X2 Jab3l.X2
## 32 0 0 0 0 0 0 0
## Kick3l.X2 upper3l.X2 takedown3l.X2 hammer3l.X2 Crossm.X2 Kneem.X2 Elbowm.X2
## 32 0 0 0 0 0 1 0
## Hookm.X2 Jabm.X2 Kickm.X2 upperm.X2 takedownm.X2 hammerm.X2 Cross2m.X2
## 32 1 0 0 0 0 0 0
## Knee2m.X2 Elbow2m.X2 Hook2m.X2 Jab2m.X2 Kick2m.X2 upper2m.X2 takedown2m.X2
## 32 0 0 0 0 0 0 0
## hammer2m.X2 Cross3m.X2 Knee3m.X2 Elbow3m.X2 Hook3m.X2 Jab3m.X2 Kick3m.X2
## 32 0 0 0 0 0 0 0
## upper3m.X2 takedown3m.X2 hammer3m.X2 holdingX2 breaksHoldX2 caughtHoldX2
## 32 0 0 0 1 0 1
## lostHoldX2 muayThaiKickX2 pushKickX2 totalHoldsX2 totalLostHoldsX2
## 32 0 0 0 6 1
## totalCaughtHoldsX2 totalBreakOutHoldsX2 totalMuayThaiKicksX2
## 32 10 1 5
## totalPushKicksX2
## 32 0It looks like the prediciton of one hit landed by Felicia is not an actual hit landed by Germaine against Amanda. But under the conditions that Amanda lands no hits that second, 88 seconds into the round, with an accumulated 14 hits by Amanda missed and 4 hits landed by Amanda and a cumulative total of 2 hits recieved by Amanda (from 2 hits landed by opponent), and 1 second since an action taken by either fighter, as well as a knee and hook missed by Amanda and holding some part of the body while being caught in a hold of some sort, Amanda having had 6 holds up to 88 seconds in the round, and Amanda having lost 1 of those holds, Amanda having been caught in 10 holds and broken out of 1 hold, attempted 5 muay thai kicks and 0 push kicks. Lets see what those holds are that Amanda is in and that Germaine was in, that the predictive model is predicting Felicia to land a hit. It is the instance 88 seconds into the round with Germaine from Amanda’s data that included the notes and action notes of each fighter. In this exact data table X1 is Amanda, and that’s who’s actions we’re interested in.
AllElseEqual <- subset(AmandaML, AmandaML$SecondsIntoRound==88 & AmandaML$Notes=='Germaine')
AllElseEqual$FighterActionReactions.X1## [1] caught in open guard hold continues and misses downward R hook to head while pushing and twisting L and holding L knee hold continues
## 155 Levels: holding side control hold continues ...AllElseEqual$FightersActionsReactions.X2## [1] holding open guard hold continues and caught in L knee hold
## 139 Levels: attempting to get up with opponent and do a waste takedown against cage and caught in guillotine hold ...So, when Amanda is caught in an open guard and holding opponent’s Left knee and also misses a downward Right hook to the head of her opponent (Germaine) the glm model predicts that Felicia would react unlike Germaine and will land a hit. Because Germaine didn’t actually land a hit in this instance but based on Felicia’s model we trained, it predicts Felicia would land a hit without specifying which one. Interesting. Now, lets see how Python compares the fight between Felicia and Amanda.
Python models to predict hits landed for Felicia against Amanda.
library(reticulate)conda_list(conda = "auto") ## name python
## 1 Anaconda2 C:\\Users\\m\\Anaconda2\\python.exe
## 2 djangoenv C:\\Users\\m\\Anaconda2\\envs\\djangoenv\\python.exe
## 3 python36 C:\\Users\\m\\Anaconda2\\envs\\python36\\python.exe
## 4 python37 C:\\Users\\m\\Anaconda2\\envs\\python37\\python.exe
## 5 r-reticulate C:\\Users\\m\\Anaconda2\\envs\\r-reticulate\\python.exeuse_condaenv(condaenv = "python36")import pandas as pd
import sklearn
import numpy as np
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
np.random.seed(47) Import our ML ready file to run the python algorithms on with sklearn.
TrainingSetFelicia = pd.read_csv('FeliciaM-asX2-L-v2.csv', encoding = 'unicode_escape')
print(TrainingSetFelicia.shape)## (529, 75)TestingSetAmanda = pd.read_csv('AmandaML-asX1-v2.csv', encoding='unicode_escape')
print(TestingSetAmanda.shape)## (231, 75)Felicia_train = TrainingSetFelicia
Felicia_test = TestingSetAmanda
Felicia_hits_train = TrainingSetFelicia['TotLandsX1']
Felicia_hits_test = TestingSetAmanda['TotLandsX1']
print(Felicia_train.shape)## (529, 75)print(Felicia_test.shape)## (231, 75)Python’s Sklearn’s Random Forest and Gradient Boosting Classifier results
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import precision_recall_fscore_support as score
import timerf=RandomForestClassifier(n_estimators=150, max_depth=None, n_jobs=-1)
start=time.time()
rf_model=rf.fit(Felicia_train,Felicia_hits_train)
end=time.time()
fit_time=(end-start)
fit_time## 0.39334535598754883start=time.time()
y_pred=rf_model.predict(Felicia_test)
end=time.time()
pred_time=(end-start)
pred_time## 0.16477632522583008prd = pd.DataFrame(y_pred)
prd.columns=['Predicted']
prd.index=Felicia_hits_test.index
pred=pd.concat([pd.DataFrame(prd),Felicia_hits_test],axis=1)
print(pred)## Predicted TotLandsX1
## 0 0 0
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## .. ... ...
## 226 0 0
## 227 0 0
## 228 0 0
## 229 0 0
## 230 0 0
##
## [231 rows x 2 columns]accuracy_Felicia_valid_RF = sum(pred['Predicted']==pred['TotLandsX1'])/len(pred['Predicted'])
accuracy_Felicia_valid_RF## 0.9783549783549783The python Random Forest Classifier scored 97.8% accuracy in prediction for Felicia’s actions and reactions.
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
print('accuracy', accuracy_score(Felicia_hits_test, y_pred))## accuracy 0.9783549783549783print('confusion matrix')## confusion matrixprint(confusion_matrix(Felicia_hits_test, y_pred))## [[223 0 0]
## [ 0 3 0]
## [ 0 5 0]]All 0 hits were predicted to be 0, all 1 hits were predicted to be 1s, but all 2 hits landed were predicted to be 1 hit landed. There were 13 total hits by Germaine that we are predicting if Felicia will land any hits as actions compared to the actions of Amanda when they compete against each other. This model predicted that Felicia will also land 13 hits but they aren’t all the exact same instances that Germaine landed her hits against Amanda. Lets see which instances those are.
hitsPredicted = pred[pred['Predicted']>0]
hitsPredicted## Predicted TotLandsX1
## 5 1 1
## 7 1 1
## 51 1 1
## 57 1 2
## 58 1 2
## 60 1 2
## 62 1 2
## 65 1 2rows = hitsPredicted.index
rows## Int64Index([5, 7, 51, 57, 58, 60, 62, 65], dtype='int64')TestingSetAmanda.shape## (231, 75)TestingSetAmanda.iloc[rows]## SecondsIntoRound lastAction ... totalMuayThaiKicksX2 totalPushKicksX2
## 5 19 18 ... 3 0
## 7 34 31 ... 4 0
## 51 111 109 ... 5 1
## 57 117 116 ... 5 1
## 58 118 117 ... 5 1
## 60 120 119 ... 5 1
## 62 122 121 ... 5 1
## 65 125 124 ... 5 1
##
## [8 rows x 75 columns]Lets look at these indices in the R version of the table with the action notes as descriptive information.
predHitsPyRF <- c(5, 7, 51, 57, 58, 60, 62, 65)
AmandaML[predHitsPyRF,19]## [1] <NA>
## [2] <NA>
## [3] holds L ft of opponent after stepping back from upward kicks
## [4] caught in R arm hold and holding upper body hold continues
## [5] lands R knee to upper L leg of opponent while holding opponent against the cage and caught in R arm hold and holding upper body hold continues
## [6] caught in R arm hold and holding upper body hold continues
## [7] caught in R arm hold and holding upper body hold continues
## [8] caught in R arm hold and holding upper body hold continues
## 139 Levels: attempting to get up with opponent and do a waste takedown against cage and caught in guillotine hold ...From the above data, we can see that Python’s sklearn’s random forest predicts that when X1 as Felicia is against X2 as Amanda under the above circumstances Felicia will land a hit. Amanda is open stand up, Felicia is in open guard trying to kick Amanda off of her, Amanda is holding Felicia’s upper body and Felicia is holding Amanda’s Right arm, Amanda is holding Felicia’s upper body and Felicia is holding Amanda’s R arm, and when Amanda holds Felicia against the cage holding her upper body and Felicia is holding her right arm then Felicia will land a Right knee to the upper leg of Amanda.
Now lets see how the gradient boosting classifier predicts hits landed for Felicia against Amanda.
gb=GradientBoostingClassifier(n_estimators=150,max_depth=11)
start=time.time()
gb_model=gb.fit(Felicia_train,Felicia_hits_train)
end=time.time()
fit_time=(end-start)
fit_time## 0.5254030227661133start=time.time()
y_pred=gb_model.predict(Felicia_test)
end=time.time()
pred_time=(end-start)
pred_time## 0.051012516021728516prd = pd.DataFrame(y_pred)
prd.columns=['Predicted']
prd.index=Felicia_hits_test.index
pred=pd.concat([pd.DataFrame(prd),Felicia_hits_test],axis=1)
print(pred)## Predicted TotLandsX1
## 0 0 0
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## .. ... ...
## 226 0 0
## 227 0 0
## 228 0 0
## 229 0 0
## 230 0 0
##
## [231 rows x 2 columns]accuracy_Felicia_valid_GB = sum(pred['Predicted']==pred['TotLandsX1'])/len(pred['Predicted'])
accuracy_Felicia_valid_GB## 1.0The python Gradient Boosted Classifier scored 100% accuracy in prediction for Felicia’s actions and reactions.
from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix
print('accuracy', accuracy_score(Felicia_hits_test, y_pred))## accuracy 1.0print('confusion matrix')## confusion matrixprint(confusion_matrix(Felicia_hits_test, y_pred))## [[223 0 0]
## [ 0 3 0]
## [ 0 0 5]]We can see that the predicted 0s, 1s, and 2s, of hits landed per second by Felicia are the same predicted hits landed by Germaine against Amanda. The gradient boosted model was able to correctly predict all the 2 hits per second landed that the random forest classifier didn’t. This says that the Gradient Boosted Classifer thinks Amanda’s hits against her three opponents’ actions will be identical to the same type of actions Germaine had when fighting Amanda.
hitsPredicted = pred[pred['Predicted']==1]
hitsPredicted## Predicted TotLandsX1
## 5 1 1
## 7 1 1
## 51 1 1hitsPredicted2 = pred[pred['Predicted']==2]
hitsPredicted2## Predicted TotLandsX1
## 57 2 2
## 58 2 2
## 60 2 2
## 62 2 2
## 65 2 2rows = hitsPredicted.index
rows## Int64Index([5, 7, 51], dtype='int64')rows2 = hitsPredicted2.index
rows2## Int64Index([57, 58, 60, 62, 65], dtype='int64')TestingSetAmanda.iloc[rows]## SecondsIntoRound lastAction ... totalMuayThaiKicksX2 totalPushKicksX2
## 5 19 18 ... 3 0
## 7 34 31 ... 4 0
## 51 111 109 ... 5 1
##
## [3 rows x 75 columns]TestingSetAmanda.iloc[rows2]## SecondsIntoRound lastAction ... totalMuayThaiKicksX2 totalPushKicksX2
## 57 117 116 ... 5 1
## 58 118 117 ... 5 1
## 60 120 119 ... 5 1
## 62 122 121 ... 5 1
## 65 125 124 ... 5 1
##
## [5 rows x 75 columns]The following circumstances or conditions are for Amanda, when under these conditions, it is predicted that Felicia will land 1 hit per second.
predHits1PyGB <- c(5, 7, 51)
AmandaML[predHits1PyGB,19]## [1] <NA>
## [2] <NA>
## [3] holds L ft of opponent after stepping back from upward kicks
## 139 Levels: attempting to get up with opponent and do a waste takedown against cage and caught in guillotine hold ...The following actions/reactions are for Amanda, when Amanda is under these similar circumstances Felicia is predicted to land 2 hits per second.
predHits2PyGB <- c(57, 58, 60, 62, 65)
AmandaML[predHits2PyGB,19]## [1] caught in R arm hold and holding upper body hold continues
## [2] lands R knee to upper L leg of opponent while holding opponent against the cage and caught in R arm hold and holding upper body hold continues
## [3] caught in R arm hold and holding upper body hold continues
## [4] caught in R arm hold and holding upper body hold continues
## [5] caught in R arm hold and holding upper body hold continues
## 139 Levels: attempting to get up with opponent and do a waste takedown against cage and caught in guillotine hold ...That was interesting to see how these models predicted that Felicia Spencer would do against her opponent in the upcoming June 2, 2020 fight with Amanda Nunez. Given the data we have on Felicia we trained a model to test on her fighter reaction with 3 fighters’ first round fights with Felicia, and then tested those reactions with 90-100% accuracy in prediction. We then used those models to test how well they would predict Felicia to react with a successful landed hit against the actions of Amanda. It looks like the sklearn Gradient Boosted classifier thinks that Amanda is 100% similar to Germaine’s reactions against Amanda for hits landed, sklearn’s random forest thinks she is 97% similar but not going to land as many fast hits per second as Germaine. And for Random forest in R’s caret package, it predicts Felicia will not land any hits in her fight with Amanda while the generalized linear models R caret package predicts she will land one hit against Amanda if she is on her back in open guard where Amanda misses a Right hook to Felicia’s head and while Amanda is holding Felicia’s Left knee in her open guard.
There is more that could be done to do action by action of the most actions used, and predict one of those actions being used or landed based on Amanda’s actions, and a great number of variations of this model could predict likely results. We can have fun assuming this model works and see how the fight plays out. These fighters could be learning from their mistakes and the others’ previous fights available of their opponent to master the others weaknesses much like modifications and tuned parameter variations of our models to improve hits landed and go for their desired outcome. Outcomes weren’t recorded for this ML project, but Amanda won against Germaine after five rounds as a unanimous decision when she almost had a technical knockout in the first round of Germaine but the referee didn’t call it. And the Felicia fights ended in her victory over Zarah and Megan in submissions of a Rear Naked Choke or RNC in the first round and a unanimous decision loss to Christiane Cyborg after all five rounds of five minutes each.
I want to add another technique to use in analyzing this fighter, Felicia Spencer, using her actions and hits landed only in a script obtained from Sebastian Raschka in his book, ‘Python: Machine Learning’ - chapter 16 on Recursive Neural Networks. This script is exactly the code used in this chapter but modified for this data using his two projects to predict sentiment on IMDB movie reviews and to predict a script up to a set amount of characters based on the input string characters. This book is available in a Kindle version that was used.
library(reticulate)conda_list(conda = "auto") ## name python
## 1 Anaconda2 C:\\Users\\m\\Anaconda2\\python.exe
## 2 djangoenv C:\\Users\\m\\Anaconda2\\envs\\djangoenv\\python.exe
## 3 python36 C:\\Users\\m\\Anaconda2\\envs\\python36\\python.exe
## 4 python37 C:\\Users\\m\\Anaconda2\\envs\\python37\\python.exe
## 5 r-reticulate C:\\Users\\m\\Anaconda2\\envs\\r-reticulate\\python.exeuse_condaenv(condaenv = "python36")We are using the same Felicia Spencer data file, but changed the name of TotLandsX1 to sentiment, and the name of the FighterActionsReactionsX1 to review. This file is also in github, modified outside of R and Python as ‘2columnsSpencer.csv’ and the NAs were also removed.
import pyprind
import pandas as pd
from string import punctuation
import re
import numpy as np
df = pd.read_csv('2columnsSpencer.csv', encoding='utf-8')
print(df.head(10))## review sentiment
## 0 missed L jab 0
## 1 missed R cross, missed L jab 0
## 2 missed L jab 0
## 3 missed R cross 0
## 4 missed L mt kick to low leg 0
## 5 misses R cross 0
## 6 misses L jab 0
## 7 lands R push kick to upper body 1
## 8 lands L mt kick to inside leg 1
## 9 misses L jab to face 0df.shape## (497, 2)type(df['review'])## <class 'pandas.core.series.Series'>## Separate words and
## count each word's occurrence
from collections import Counter
counts = Counter()
pbar = pyprind.ProgBar(len(df['review']),
title='Counting words occurences')## Counting words occurencesfor i,review in enumerate(df['review']):
text = ''.join([c if c not in punctuation else ' '+c+' ' \
for c in review]).lower()
df.loc[i,'review'] = text
pbar.update()
counts.update(text.split())##
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## Total time elapsed: 00:00:00## Create a mapping:
## Map each unique word to an integer
word_counts = sorted(counts, key=counts.get, reverse=True)
print(word_counts[:5])## ['hold', 'holding', 'continues', 'r', 'and']word_to_int = {word: ii for ii, word in enumerate(word_counts, 1)}
mapped_reviews = []
pbar = pyprind.ProgBar(len(df['review']),
title='Map reviews to ints')## Map reviews to intsfor review in df['review']:
mapped_reviews.append([word_to_int[word] for word in review.split()])
pbar.update()##
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## Total time elapsed: 00:00:00len(mapped_reviews)## 497sequence_length = 200 ## sequence length (or T in our formulas)
sequences = np.zeros((len(mapped_reviews), sequence_length), dtype=int)
for i, row in enumerate(mapped_reviews):
review_arr = np.array(row)
sequences[i, -len(row):] = review_arr[-sequence_length:]sequences.shape## (497, 200)## Define fixed-length sequences:
## Use the last 200 elements of each sequence
## if sequence length < 200: left-pad with zeros
sequence_length = 200 ## sequence length (or T in our formulas)
sequences = np.zeros((len(mapped_reviews), sequence_length), dtype=int)
for i, row in enumerate(mapped_reviews):
review_arr = np.array(row)
sequences[i, -len(row):] = review_arr[-sequence_length:]
X_train = sequences[:347, :]
y_train = df.loc[:347, 'sentiment'].values
X_test = sequences[347:, :]
#the number of samples has to be divisible exactly by batch size or predictions will be cut off, if test #samples are 147 and batch size 15, the predictions will only have 135 samples
y_test = df.loc[347:, 'sentiment'].values
np.random.seed(123) # for reproducibility
## Function to generate minibatches:
def create_batch_generator(x, y=None, batch_size=64):
n_batches = len(x)//batch_size
x= x[:n_batches*batch_size]
if y is not None:
y = y[:n_batches*batch_size]
for ii in range(0, len(x), batch_size):
if y is not None:
yield x[ii:ii+batch_size], y[ii:ii+batch_size]
else:
yield x[ii:ii+batch_size]import tensorflow as tf
class SentimentRNN(object):
def __init__(self, n_words, seq_len=200,
lstm_size=256, num_layers=1, batch_size=64,
learning_rate=0.0001, embed_size=200):
self.n_words = n_words
self.seq_len = seq_len
self.lstm_size = lstm_size ## number of hidden units
self.num_layers = num_layers
self.batch_size = batch_size
self.learning_rate = learning_rate
self.embed_size = embed_size
self.g = tf.Graph()
with self.g.as_default():
tf.set_random_seed(123)
self.build()
self.saver = tf.train.Saver()
self.init_op = tf.global_variables_initializer()
def build(self):
## Define the placeholders
tf_x = tf.placeholder(tf.int32,
shape=(self.batch_size, self.seq_len),
name='tf_x')
tf_y = tf.placeholder(tf.float32,
shape=(self.batch_size),
name='tf_y')
tf_keepprob = tf.placeholder(tf.float32,
name='tf_keepprob')
## Create the embedding layer
embedding = tf.Variable(
tf.random_uniform(
(self.n_words, self.embed_size),
minval=-1, maxval=1),
name='embedding')
embed_x = tf.nn.embedding_lookup(
embedding, tf_x,
name='embeded_x')
## Define LSTM cell and stack them together
cells = tf.contrib.rnn.MultiRNNCell(
[tf.contrib.rnn.DropoutWrapper(
tf.contrib.rnn.BasicLSTMCell(self.lstm_size),
output_keep_prob=tf_keepprob)
for i in range(self.num_layers)])
## Define the initial state:
self.initial_state = cells.zero_state(
self.batch_size, tf.float32)
print(' << initial state >> ', self.initial_state)
lstm_outputs, self.final_state = tf.nn.dynamic_rnn(
cells, embed_x,
initial_state=self.initial_state)
## Note: lstm_outputs shape:
## [batch_size, max_time, cells.output_size]
print('\n << lstm_output >> ', lstm_outputs)
print('\n << final state >> ', self.final_state)
## Apply a FC layer after on top of RNN output:
logits = tf.layers.dense(
inputs=lstm_outputs[:, -1],
units=1, activation=None,
name='logits')
logits = tf.squeeze(logits, name='logits_squeezed')
print ('\n << logits >> ', logits)
y_proba = tf.nn.sigmoid(logits, name='probabilities')
predictions = {
'probabilities': y_proba,
'labels' : tf.cast(tf.round(y_proba), tf.int32,
name='labels')
}
print('\n << predictions >> ', predictions)
## Define the cost function
cost = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf_y, logits=logits),
name='cost')
## Define the optimizer
optimizer = tf.train.AdamOptimizer(self.learning_rate)
train_op = optimizer.minimize(cost, name='train_op')
def train(self, X_train, y_train, num_epochs):
with tf.Session(graph=self.g) as sess:
sess.run(self.init_op)
iteration = 1
for epoch in range(num_epochs):
state = sess.run(self.initial_state)
for batch_x, batch_y in create_batch_generator(
X_train, y_train, self.batch_size):
feed = {'tf_x:0': batch_x,
'tf_y:0': batch_y,
'tf_keepprob:0': 0.5,
self.initial_state : state}
loss, _, state = sess.run(
['cost:0', 'train_op',
self.final_state],
feed_dict=feed)
if iteration % 20 == 0:
print("Epoch: %d/%d Iteration: %d "
"| Train loss: %.5f" % (
epoch + 1, num_epochs,
iteration, loss))
iteration +=1
if (epoch+1)%10 == 0:
self.saver.save(sess,
"model/sentiment-%d.ckpt" % epoch)
def predict(self, X_data, return_proba=False):
preds = []
with tf.Session(graph = self.g) as sess:
self.saver.restore(
sess, tf.train.latest_checkpoint('model/'))
test_state = sess.run(self.initial_state)
for ii, batch_x in enumerate(
create_batch_generator(
X_data, None, batch_size=self.batch_size), 1):
feed = {'tf_x:0' : batch_x,
'tf_keepprob:0': 1.0,
self.initial_state : test_state}
if return_proba:
pred, test_state = sess.run(
['probabilities:0', self.final_state],
feed_dict=feed)
else:
pred, test_state = sess.run(
['labels:0', self.final_state],
feed_dict=feed)
preds.append(pred)
return np.concatenate(preds)## Train:
n_words = max(list(word_to_int.values())) + 1
rnn = SentimentRNN(n_words=n_words,
seq_len=sequence_length,
embed_size=256,
lstm_size=128,
num_layers=1,
# batch_size has to divide evenly into the number of testing set samples, or some samples will be removed
batch_size=15,
learning_rate=0.001)## << initial state >> (LSTMStateTuple(c=<tf.Tensor 'MultiRNNCellZeroState/DropoutWrapperZeroState/BasicLSTMCellZeroState/zeros:0' shape=(15, 128) dtype=float32>, h=<tf.Tensor 'MultiRNNCellZeroState/DropoutWrapperZeroState/BasicLSTMCellZeroState/zeros_1:0' shape=(15, 128) dtype=float32>),)
##
## << lstm_output >> Tensor("rnn/transpose_1:0", shape=(15, 200, 128), dtype=float32)
##
## << final state >> (LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_3:0' shape=(15, 128) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_4:0' shape=(15, 128) dtype=float32>),)
##
## << logits >> Tensor("logits_squeezed:0", shape=(15,), dtype=float32)
##
## << predictions >> {'probabilities': <tf.Tensor 'probabilities:0' shape=(15,) dtype=float32>, 'labels': <tf.Tensor 'labels:0' shape=(15,) dtype=int32>}
##
## W0528 08:46:01.902913 16084 deprecation.py:323] From <string>:45: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
## Instructions for updating:
## This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
## W0528 08:46:01.913917 16084 deprecation.py:323] From <string>:45: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
## Instructions for updating:
## This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.
## W0528 08:46:01.951609 16084 deprecation.py:323] From <string>:54: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
## Instructions for updating:
## Please use `keras.layers.RNN(cell)`, which is equivalent to this API
## W0528 08:46:02.730317 16084 deprecation.py:506] From C:\Users\m\Anaconda2\envs\python36\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py:738: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
## Instructions for updating:
## Call initializer instance with the dtype argument instead of passing it to the constructor
## W0528 08:46:03.655428 16084 deprecation.py:323] From <string>:64: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
## Instructions for updating:
## Use keras.layers.dense instead.
## W0528 08:46:04.304435 16084 deprecation.py:323] From C:\Users\m\Anaconda2\envs\python36\lib\site-packages\tensorflow\python\ops\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
## Instructions for updating:
## Use tf.where in 2.0, which has the same broadcast rule as np.wherernn.train(X_train, y_train, num_epochs=5)## Epoch: 1/5 Iteration: 20 | Train loss: 0.09227
## Epoch: 2/5 Iteration: 40 | Train loss: 0.02640
## Epoch: 3/5 Iteration: 60 | Train loss: 0.07121
## Epoch: 4/5 Iteration: 80 | Train loss: 0.19046
## Epoch: 5/5 Iteration: 100 | Train loss: -0.77173X_test.shape## (150, 200)y_test## array([0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
## 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)y_test.shape## (150,)X_test## array([[ 0, 0, 0, ..., 13, 17, 22],
## [ 0, 0, 0, ..., 32, 4, 28],
## [ 0, 0, 0, ..., 26, 17, 22],
## ...,
## [ 0, 0, 0, ..., 4, 10, 1],
## [ 0, 0, 0, ..., 4, 10, 1],
## [ 0, 0, 0, ..., 4, 10, 1]])## Test:
preds = rnn.predict(X_test)## W0528 08:50:51.029401 16084 deprecation.py:323] From C:\Users\m\Anaconda2\envs\python36\lib\site-packages\tensorflow\python\training\saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
## Instructions for updating:
## Use standard file APIs to check for files with this prefix.y_true = y_test[:len(preds)]
print('Test Acc.: %.3f' % (
np.sum(preds == y_true) / len(y_true)))## Test Acc.: 1.000preds## array([0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
## 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])preds.shape## (150,)prd = pd.DataFrame(preds)
prd.columns=['Predicted']
type(prd)## <class 'pandas.core.frame.DataFrame'>prd## Predicted
## 0 0
## 1 0
## 2 1
## 3 0
## 4 1
## .. ...
## 145 0
## 146 0
## 147 0
## 148 0
## 149 0
##
## [150 rows x 1 columns]true = pd.DataFrame(y_test)
true.columns=['trueValue']
prd.index=true.index
pred=pd.concat([pd.DataFrame(prd),true],axis=1)
print(pred)## Predicted trueValue
## 0 0 0
## 1 0 0
## 2 1 1
## 3 0 0
## 4 1 1
## .. ... ...
## 145 0 0
## 146 0 0
## 147 0 0
## 148 0 0
## 149 0 0
##
## [150 rows x 2 columns]## Get probabilities:
proba = rnn.predict(X_test, return_proba=True)proba## array([2.6762486e-05, 5.9038401e-05, 1.0000000e+00, 2.0533800e-05,
## 9.9999988e-01, 9.0688467e-05, 4.7385693e-05, 7.7486038e-07,
## 2.1755695e-06, 2.1755695e-06, 1.0000000e+00, 2.1755695e-06,
## 2.1780613e-06, 2.1780636e-06, 2.1780636e-06, 2.1159649e-06,
## 2.1159649e-06, 1.0000000e+00, 1.0000000e+00, 2.1755695e-06,
## 2.1755695e-06, 2.1159649e-06, 1.0000000e+00, 2.1159649e-06,
## 2.1159649e-06, 2.2053719e-06, 2.1159649e-06, 1.0000000e+00,
## 2.1780593e-06, 2.1780572e-06, 2.1755695e-06, 2.1159649e-06,
## 1.0000000e+00, 2.1755695e-06, 2.1159649e-06, 2.1159649e-06,
## 2.1755695e-06, 2.1755695e-06, 2.1159649e-06, 1.0000000e+00,
## 2.1159649e-06, 2.1159649e-06, 2.1781987e-06, 8.9368143e-07,
## 2.1780593e-06, 2.1159649e-06, 2.1159649e-06, 2.1159649e-06,
## 2.1755695e-06, 2.1159649e-06, 3.2484531e-06, 1.8775463e-06,
## 1.7881393e-06, 1.7881393e-06, 1.8775463e-06, 1.7881393e-06,
## 1.7881393e-06, 1.8755559e-06, 1.8755542e-06, 4.7560457e-06,
## 2.1159649e-06, 1.0000000e+00, 1.0000000e+00, 8.6426735e-07,
## 3.2782555e-07, 3.7491322e-05, 3.2535195e-04, 3.2535195e-04,
## 3.2380223e-04, 1.3351440e-05, 7.7012479e-03, 4.4822693e-05,
## 8.9176938e-06, 1.7723907e-05, 1.3380422e-05, 2.7856231e-04,
## 3.5762787e-07, 3.0198693e-04, 2.7856231e-04, 1.0000000e+00,
## 1.0000000e+00, 3.7491322e-05, 3.7491322e-05, 2.7856231e-04,
## 1.5795231e-06, 1.3113022e-06, 1.2516975e-06, 1.3055668e-06,
## 4.8880929e-07, 4.8880975e-07, 4.4703484e-07, 4.7683716e-07,
## 1.0000000e+00, 4.4703484e-07, 4.4703484e-07, 4.4703484e-07,
## 4.7683716e-07, 4.4703484e-07, 4.4703484e-07, 3.7252903e-06,
## 4.4703484e-07, 4.7683716e-07, 4.8881208e-07, 4.8881304e-07,
## 1.0000000e+00, 1.8179417e-06, 4.7683716e-07, 4.7683716e-07,
## 4.7683716e-07, 4.7683716e-07, 4.7683716e-07, 4.7683716e-07,
## 4.7683716e-07, 4.7683716e-07, 4.7683716e-07, 4.7683716e-07,
## 4.7683716e-07, 4.8881304e-07, 4.8881304e-07, 4.8885590e-07,
## 4.7683716e-07, 4.7683716e-07, 4.7683716e-07, 4.7683716e-07,
## 4.7683716e-07, 5.3644180e-07, 1.0000000e+00, 2.0039082e-04,
## 4.7683716e-07, 4.7683716e-07, 5.3644180e-07, 4.7683716e-07,
## 4.8881304e-07, 4.8881350e-07, 4.8881304e-07, 4.7683716e-07,
## 4.7683716e-07, 4.7683716e-07, 4.7683716e-07, 5.3644180e-07,
## 4.7683716e-07, 4.7683716e-07, 4.7683716e-07, 4.7683716e-07,
## 4.7683716e-07, 4.7683716e-07, 4.7683716e-07, 4.8881304e-07,
## 4.8881304e-07, 4.8881304e-07], dtype=float32)proba.shape## (150,)This next RNN will make an attempt to predict the text as a limited output based on the actions over sequential data of instances for Felicia Spencer as a text document.
import numpy as np
## Reading and processing text
with open('2columnsSpencer.txt', 'r', encoding='utf-8') as f:
text=f.read()text## "missed L jab\nmissed R cross, missed L jab\nmissed L jab\nmissed R cross \nmissed L mt kick to low leg\nmisses R cross\nmisses L jab\nlands R push kick to upper body\nlands L mt kick to inside leg\nmisses L jab to face\nmissed R cross\nmisses attempted clinch\nmisses L hook\nmisses L jab to face\nmisses L mt kick to upper body and caught in L foot hold\nbreaks L foot hold\nlands L jab to body\nmisses body takedown\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nholding upper body hold starts and caught in L arm hold\nbreaks L arm hold and caught in R arm hold while holding upper body continues\ncaught in R arm hold while holding upper body hold\ncaught in R arm hold while holding upper body hold\nlands L knee to back of L leg while caught in R arm hold while holding upper body hold\ncaught in R arm hold while holding upper body hold\ncaught in R arm hold while holding upper body hold\ncaught in R arm hold while holding upper body hold\ncaught in R arm hold while holding upper body hold\ncaught in R arm hold while holding upper body hold\ncaught in R arm hold while holding upper body hold\nlands judo type takedown while caught in R arm hold and while holding upper body\nmount attempt and holding full mount hold starts\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\nlands L hook to head while caught in R arm hold and holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\ncaught in R arm hold while holding full mount hold\nbreaks R arm hold while holding full mount hold\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nlands elbow to face holding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nlands R hook to face holding full mount hold continues\nholding full mount hold continues\ncaught in body hold and holding full mount hold continues\nbreaks body hold and holding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\ncaught in body hold and holding full mount hold continues\ncaught in body hold and holding full mount hold continues\nbreaks body hold and lands L elbow to face and holding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nlands L cross to face and holding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues and lands R elbow to face, lands R elbow to face \nlands R elbow to face and holding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nlands L elbow to face and holding full mount hold continues\nlands L elbow to face and holding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nlands L cross to face and holding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nloses full mount hold and holding side mount hold starts\nholding side mount hold continues\nholding back mount hold starts and loses side mount hold\nholding back mount hold continues\nholding back mount hold continues\nmisses L hook to face holding back mount hold continues\nloses back mount hold and holding full mount hold starts\nholding full mount hold on top and caught in upper body hold\nholding full mount hold on top and caught in upper body hold\nlands R hook to face holding full mount hold continues and caught in upper body hold\nlands R hook to face holding full mount hold continues and caught in upper body hold\nlands L elbow to face holding full mount hold continues and caught in upper body hold\nlands L elbow to face holding full mount hold continues and caught in upper body hold\nholding full mount hold continues and caught in upper body hold\nloses full mount hold and holding side control mount starts and breaks upper body hold\nholding side mount hold continues\nholding side mount hold continues\nlands L hammer to face holding side mount hold continues\nholding side mount hold continues\nholding side mount hold continues\nloses side mount hold and holding full mount hold starts\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold continues\nholding full mount hold lands R hook to face\nholding full mount hold lands R hook to face\nholding full mount hold\nholding full mount hold lands L hook to face\nholding full mount hold lands L hook to face\nholding full mount hold\nholding full mount hold\nholding full mount hold misses L elbow to face\nholding full mount hold misses L elbow to face, misses L elbow to face\nTKO referree stoppage\ntesting L push kick\ntesting L mt kick to low leg\nmisses L push kick\nlands R cross\nmisses L mt kick to face, misses R cross\nmisses R push kick\nmisses upper body takedown and holding upper body hold starts\nlands L knee to body holding upper body hold continues\nholding upper body hold continues\nholding upper body hold continues\nholding upper body hold continues\nholding upper body hold continues\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\npushes against cage holding upper body hold continues and caught in R arm hold\nlands R knee to L leg holding upper body hold continues and caught in R arm hold, lands L knee to inner leg \nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nlands R knee to inner leg holding upper body hold continues and caught in R arm hold, lands L knee to inner leg\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nlands R knee to inner leg and holding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nlands R knee to outer leg and holding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nbreaks R arm hold and loses upper body hold\nlands judo type takedown\nmoves out attempting a ground takedown flip\nholding under shoulders and upper body hold starts on ground\nholding upper body continues\nholding upper body continues\njumps on back losing upper body hold and holding back mount hold starts\nsinks feet in thighs while holding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues and holding L underarm choke hold starts\nholding back mount hold continues\nflips forward taking the back, lands on side holding back mount hold continues and holding L underarm choke hold continues\ngrabs under arms holding back mount hold continues and holding L underarm choke hold continues\nholding back mount hold continues and holding L underarm choke hold continues\nholding back mount hold continues and holding L underarm choke hold continues\nholding back mount hold continues and holding L underarm choke hold continues\nholding back mount hold continues and holding L underarm choke hold continues\nholding back mount hold continues and holding L underarm choke hold continues\nholding back mount hold continues and holding L underarm choke hold continues\nholding back mount hold continues and holding L underarm choke hold continues\nholding back mount hold continues and loses L underarm choke hold\nholding back mount hold\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount hold and holding R underarm choke hold starts\nholding back mount continues on top from on back\nholding back mount\nloses back mount hold and holding side mount hold starts\nholding side mount hold continues\nmisses elbow holding side mount hold continues\nholding side mount hold continues\nloses side mount hold and holding back mount hold starts\nholding back mount hold continues\nholding back mount hold continues\nlands R hammer hit holding back mount hold continues, lands R hammer hit\nlands R hammer hit holding back mount hold continues, lands R hammer hit\nholding back mount hold\nholding back mount hold\nholding back mount hold\nloses back mount hold and holding back hold starts\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nholding back hold continues\nlands R hook holding back hold continues, lands R hook\nlands R hook holding back hold continues, lands R hook\nloses back hold and holding back mount hold starts\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nlands R hook holding back mount hold continues, lands R hook\nlands R hook holding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues\nholding back mount hold continues and holding neck hold starts\nlands R hook holding back mount hold continues and holding neck hold continues\nlands R hook holding back mount hold continues and holding neck hold continues\nholding back mount hold continues and holding neck hold continues\nholding back mount hold continues and holding neck hold continues\nholding back mount hold continues and holding neck hold continues\nholding back mount hold continues and holding neck hold continues\nholding neck choke hold starts while holding back mount hold continues and loses neck hold\nholding neck choke hold starts while holding back mount hold continues\nholding neck choke hold starts while holding back mount hold continues\nholding neck choke hold starts while holding back mount hold continues\nholding neck choke hold starts while holding back mount hold continues\nholding neck choke hold starts while holding back mount hold continues\nwins by submission\nmissed R cross\nmissed R cross\nlands R mt kick\ngrabs leg, misses single leg takedown\nmissed L knee to body, missed L to face\nmissed R cross\nlands flying elbow to face\nmissed R cross to face\nlands R push kick\ngrabs head against cage\npushes against cage\nmisses takedown holding upper bodyhold starts and caught in R arm hold\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\nlands L knee to inner R upper leg while caught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\nlands R knee to inner R leg caught in R arm hold and holding upper body hold continues\nlands R knee to inner R leg caught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\nlands R knee to inner R leg caught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\nlands R knee to inner R leg caught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\nlands R knee to outer L leg caught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\nlands R knee to inner R leg caught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ngets up holding upper body hold continues and caught in R arm hold\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\nbreaks R arm hold caught in L arm hold and holding upper body hold continues\ncaught in L arm hold and holding upper body hold continues\ncaught in L arm hold and holding upper body hold continues\ncaught in L arm hold and holding upper body hold continues\ncaught in L arm hold and holding upper body hold continues\ncaught in L arm hold and holding upper body hold continues\ncaught in L arm hold and holding upper body hold continues\ncaught in L arm hold and holding upper body hold continues\ncaught in L arm hold and holding upper body hold continues\nbreaks L arm hold and caught in R arm hold while holding upper body hold continues\ncaught in R arm hold and holding upper body hold continues\nlands R knee to inner R leg and caught in R arm hold and holding upper body hold continues\nlands L hook to head and caught in R arm hold and holding upper body hold continues\nbreaks R arm hold and loses upper body hold\nmisses L hook to face\nmisses R cross\nfalls\nfalls\ngets up\nmisses L jab\nmisses flying elbow to face, misses R uppercut to face\nmisses attempted clinch\nmisses R push kick\nmisses R jab\nmisses L jab\nmisses attempted takedown\nmisses L hook to face\nmisses takedown\nmisses attempted takedown\nlands L flying elbow to face \nlands R elbow to face\nmisses R cross\nmisses R cross\nmisses attempted takedown\nholding upper body hold starts\nholding upper body hold continues\nholding upper body hold continues\nholding upper body hold continues\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nlands R knee to inner R leg and caught in R arm hold and holding upper body hold continues\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nmisses backwards upward elbow to face while holding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nlands R knee to inner R leg holding upper body hold continues and caught in R arm hold\nmisses single leg takedown attempt while holding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nlands R knee to inner R leg holding upper body hold continues and caught in R arm hold\npulls off opponent's R hand blocking airway with L hand while holding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\nholding upper body hold continues and caught in R arm hold\n"chars = set(text)
char2int = {ch:i for i,ch in enumerate(chars)}
int2char = dict(enumerate(chars))
text_ints = np.array([char2int[ch] for ch in text],
dtype=np.int32)def reshape_data(sequence, batch_size, num_steps):
tot_batch_length = batch_size * num_steps
num_batches = int(len(sequence) / tot_batch_length)
if num_batches*tot_batch_length + 1 > len(sequence):
num_batches = num_batches - 1
## Truncate the sequence at the end to get rid of
## remaining charcaters that do not make a full batch
x = sequence[0 : num_batches*tot_batch_length]
y = sequence[1 : num_batches*tot_batch_length + 1]
## Split x & y into a list batches of sequences:
x_batch_splits = np.split(x, batch_size)
y_batch_splits = np.split(y, batch_size)
## Stack the batches together
## batch_size x tot_batch_length
x = np.stack(x_batch_splits)
y = np.stack(y_batch_splits)
return x, y
## Testing:
train_x, train_y = reshape_data(text_ints, 64, 10)
print(train_x.shape)## (64, 390)print(train_x[0, :10])## [14 11 15 15 20 23 4 7 4 26]print(train_y[0, :10])## [11 15 15 20 23 4 7 4 26 6]print(''.join(int2char[i] for i in train_x[0, :50]))## missed L jab
## missed R cross, missed L jab
## missed Lnp.random.seed(123)
def create_batch_generator(data_x, data_y, num_steps):
batch_size, tot_batch_length = data_x.shape
num_batches = int(tot_batch_length/num_steps)
for b in range(num_batches):
yield (data_x[:, b*num_steps: (b+1)*num_steps],
data_y[:, b*num_steps: (b+1)*num_steps])
bgen = create_batch_generator(train_x[:,:100], train_y[:,:100], 15)
for b in bgen:
print(b[0].shape, b[1].shape, end=' ')
print(''.join(int2char[i] for i in b[0][0,:]).replace('\n', '*'), ' ',
''.join(int2char[i] for i in b[1][0,:]).replace('\n', '*'))## (64, 15) (64, 15) missed L jab*mi issed L jab*mis
## (64, 15) (64, 15) ssed R cross, m sed R cross, mi
## (64, 15) (64, 15) issed L jab*mis ssed L jab*miss
## (64, 15) (64, 15) sed L jab*misse ed L jab*missed
## (64, 15) (64, 15) d R cross *miss R cross *misse
## (64, 15) (64, 15) ed L mt kick to d L mt kick toimport tensorflow as tf
import os
class CharRNN(object):
def __init__(self, num_classes, batch_size=64,
num_steps=100, lstm_size=128,
num_layers=1, learning_rate=0.001,
keep_prob=0.5, grad_clip=5,
sampling=False):
self.num_classes = num_classes
self.batch_size = batch_size
self.num_steps = num_steps
self.lstm_size = lstm_size
self.num_layers = num_layers
self.learning_rate = learning_rate
self.keep_prob = keep_prob
self.grad_clip = grad_clip
self.g = tf.Graph()
with self.g.as_default():
tf.set_random_seed(123)
self.build(sampling=sampling)
self.saver = tf.train.Saver()
self.init_op = tf.global_variables_initializer()
def build(self, sampling):
if sampling == True:
batch_size, num_steps = 1, 1
else:
batch_size = self.batch_size
num_steps = self.num_steps
tf_x = tf.placeholder(tf.int32,
shape=[batch_size, num_steps],
name='tf_x')
tf_y = tf.placeholder(tf.int32,
shape=[batch_size, num_steps],
name='tf_y')
tf_keepprob = tf.placeholder(tf.float32,
name='tf_keepprob')
# One-hot encoding:
x_onehot = tf.one_hot(tf_x, depth=self.num_classes)
y_onehot = tf.one_hot(tf_y, depth=self.num_classes)
### Build the multi-layer RNN cells
cells = tf.contrib.rnn.MultiRNNCell(
[tf.contrib.rnn.DropoutWrapper(
tf.contrib.rnn.BasicLSTMCell(self.lstm_size),
output_keep_prob=tf_keepprob)
for _ in range(self.num_layers)])
## Define the initial state
self.initial_state = cells.zero_state(
batch_size, tf.float32)
## Run each sequence step through the RNN
lstm_outputs, self.final_state = tf.nn.dynamic_rnn(
cells, x_onehot,
initial_state=self.initial_state)
print(' << lstm_outputs >>', lstm_outputs)
seq_output_reshaped = tf.reshape(
lstm_outputs,
shape=[-1, self.lstm_size],
name='seq_output_reshaped')
logits = tf.layers.dense(
inputs=seq_output_reshaped,
units=self.num_classes,
activation=None,
name='logits')
proba = tf.nn.softmax(
logits,
name='probabilities')
print(proba)
y_reshaped = tf.reshape(
y_onehot,
shape=[-1, self.num_classes],
name='y_reshaped')
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits,
labels=y_reshaped),
name='cost')
# Gradient clipping to avoid "exploding gradients"
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(
tf.gradients(cost, tvars),
self.grad_clip)
optimizer = tf.train.AdamOptimizer(self.learning_rate)
train_op = optimizer.apply_gradients(
zip(grads, tvars),
name='train_op')
def train(self, train_x, train_y,
num_epochs, ckpt_dir='./model/'):
## Create the checkpoint directory
## if does not exists
if not os.path.exists(ckpt_dir):
os.mkdir(ckpt_dir)
with tf.Session(graph=self.g) as sess:
sess.run(self.init_op)
n_batches = int(train_x.shape[1]/self.num_steps)
iterations = n_batches * num_epochs
for epoch in range(num_epochs):
# Train network
new_state = sess.run(self.initial_state)
loss = 0
## Minibatch generator:
bgen = create_batch_generator(
train_x, train_y, self.num_steps)
for b, (batch_x, batch_y) in enumerate(bgen, 1):
iteration = epoch*n_batches + b
feed = {'tf_x:0': batch_x,
'tf_y:0': batch_y,
'tf_keepprob:0': self.keep_prob,
self.initial_state : new_state}
batch_cost, _, new_state = sess.run(
['cost:0', 'train_op',
self.final_state],
feed_dict=feed)
if iteration % 10 == 0:
print('Epoch %d/%d Iteration %d'
'| Training loss: %.4f' % (
epoch + 1, num_epochs,
iteration, batch_cost))
## Save the trained model
self.saver.save(
sess, os.path.join(
ckpt_dir, 'language_modeling.ckpt'))
def sample(self, output_length,
ckpt_dir, starter_seq="The "):
observed_seq = [ch for ch in starter_seq]
with tf.Session(graph=self.g) as sess:
self.saver.restore(
sess,
tf.train.latest_checkpoint(ckpt_dir))
## 1: run the model using the starter sequence
new_state = sess.run(self.initial_state)
for ch in starter_seq:
x = np.zeros((1, 1))
x[0,0] = char2int[ch]
feed = {'tf_x:0': x,
'tf_keepprob:0': 1.0,
self.initial_state: new_state}
proba, new_state = sess.run(
['probabilities:0', self.final_state],
feed_dict=feed)
ch_id = get_top_char(proba, len(chars))
observed_seq.append(int2char[ch_id])
## 2: run the model using the updated observed_seq
for i in range(output_length):
x[0,0] = ch_id
feed = {'tf_x:0': x,
'tf_keepprob:0': 1.0,
self.initial_state: new_state}
proba, new_state = sess.run(
['probabilities:0', self.final_state],
feed_dict=feed)
ch_id = get_top_char(proba, len(chars))
observed_seq.append(int2char[ch_id])
return ''.join(observed_seq)def get_top_char(probas, char_size, top_n=5):
p = np.squeeze(probas)
p[np.argsort(p)[:-top_n]] = 0.0
p = p / np.sum(p)
ch_id = np.random.choice(char_size, 1, p=p)[0]
return ch_idbatch_size = 64
num_steps = 100
train_x, train_y = reshape_data(text_ints,
batch_size,
num_steps)
rnn = CharRNN(num_classes=len(chars), batch_size=batch_size)## << lstm_outputs >> Tensor("rnn/transpose_1:0", shape=(64, 100, 128), dtype=float32)
## Tensor("probabilities:0", shape=(6400, 32), dtype=float32)
##
## W0528 08:51:14.293642 16084 deprecation.py:323] From <string>:85: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
## Instructions for updating:
##
## Future major versions of TensorFlow will allow gradients to flow
## into the labels input on backprop by default.
##
## See `tf.nn.softmax_cross_entropy_with_logits_v2`.rnn.train(train_x, train_y,
num_epochs=100,
#if you don't have this checkpoint directory, it is made to store results
ckpt_dir='./model-100/') ## Epoch 4/100 Iteration 10| Training loss: 3.2197
## Epoch 7/100 Iteration 20| Training loss: 3.0128
## Epoch 10/100 Iteration 30| Training loss: 2.9582
## Epoch 14/100 Iteration 40| Training loss: 2.9453
## Epoch 17/100 Iteration 50| Training loss: 2.8356
## Epoch 20/100 Iteration 60| Training loss: 2.7651
## Epoch 24/100 Iteration 70| Training loss: 2.7066
## Epoch 27/100 Iteration 80| Training loss: 2.4829
## Epoch 30/100 Iteration 90| Training loss: 2.3698
## Epoch 34/100 Iteration 100| Training loss: 2.2667
## Epoch 37/100 Iteration 110| Training loss: 1.9694
## Epoch 40/100 Iteration 120| Training loss: 1.8873
## Epoch 44/100 Iteration 130| Training loss: 1.8310
## Epoch 47/100 Iteration 140| Training loss: 1.5265
## Epoch 50/100 Iteration 150| Training loss: 1.5308
## Epoch 54/100 Iteration 160| Training loss: 1.5079
## Epoch 57/100 Iteration 170| Training loss: 1.2340
## Epoch 60/100 Iteration 180| Training loss: 1.2520
## Epoch 64/100 Iteration 190| Training loss: 1.2860
## Epoch 67/100 Iteration 200| Training loss: 1.0275
## Epoch 70/100 Iteration 210| Training loss: 1.0687
## Epoch 74/100 Iteration 220| Training loss: 1.1252
## Epoch 77/100 Iteration 230| Training loss: 0.8629
## Epoch 80/100 Iteration 240| Training loss: 0.9240
## Epoch 84/100 Iteration 250| Training loss: 0.9973
## Epoch 87/100 Iteration 260| Training loss: 0.7609
## Epoch 90/100 Iteration 270| Training loss: 0.8086
## Epoch 94/100 Iteration 280| Training loss: 0.9098
## Epoch 97/100 Iteration 290| Training loss: 0.6763
## Epoch 100/100 Iteration 300| Training loss: 0.7251np.random.seed(123)
rnn = CharRNN(len(chars), sampling=True)## << lstm_outputs >> Tensor("rnn/transpose_1:0", shape=(1, 1, 128), dtype=float32)
## Tensor("probabilities:0", shape=(1, 32), dtype=float32)print(rnn.sample(ckpt_dir='./model-100/',
output_length=500))## The cotinuger had acahhhdind R ann arm hold hold snuers ads holding Rppperrbboyy hold hold cnttinue holding uull mount hotl aadts and hold nn holl conutnues aad caugh tin auk hold
## holding bpld coukt hold cnntinuhs hold cg unutt and
## holdingg uped bold contintes
## aned hod ing bac mouun hold cndtinues hodd aug holding full oold holdssguerr mobym hold colninus bade holding full mont hold
## sans hhd ontinue coldiuuues hod cotntingss aar hold cnughold naam hold
## and holdinn R ard hold cnuuuet nnd bcdshau## run for 200 epochs
batch_size = 64
num_steps = 100
rnn = CharRNN(num_classes=len(chars), batch_size=batch_size)## << lstm_outputs >> Tensor("rnn/transpose_1:0", shape=(64, 100, 128), dtype=float32)
## Tensor("probabilities:0", shape=(6400, 32), dtype=float32)rnn.train(train_x, train_y,
num_epochs=200,
ckpt_dir='./model-200/')## Epoch 4/200 Iteration 10| Training loss: 3.2172
## Epoch 7/200 Iteration 20| Training loss: 3.0124
## Epoch 10/200 Iteration 30| Training loss: 2.9576
## Epoch 14/200 Iteration 40| Training loss: 2.9452
## Epoch 17/200 Iteration 50| Training loss: 2.8366
## Epoch 20/200 Iteration 60| Training loss: 2.7720
## Epoch 24/200 Iteration 70| Training loss: 2.7072
## Epoch 27/200 Iteration 80| Training loss: 2.5115
## Epoch 30/200 Iteration 90| Training loss: 2.3933
## Epoch 34/200 Iteration 100| Training loss: 2.2851
## Epoch 37/200 Iteration 110| Training loss: 2.0054
## Epoch 40/200 Iteration 120| Training loss: 1.9141
## Epoch 44/200 Iteration 130| Training loss: 1.8469
## Epoch 47/200 Iteration 140| Training loss: 1.5454
## Epoch 50/200 Iteration 150| Training loss: 1.5338
## Epoch 54/200 Iteration 160| Training loss: 1.5176
## Epoch 57/200 Iteration 170| Training loss: 1.2411
## Epoch 60/200 Iteration 180| Training loss: 1.2567
## Epoch 64/200 Iteration 190| Training loss: 1.2836
## Epoch 67/200 Iteration 200| Training loss: 1.0280
## Epoch 70/200 Iteration 210| Training loss: 1.0752
## Epoch 74/200 Iteration 220| Training loss: 1.1231
## Epoch 77/200 Iteration 230| Training loss: 0.8614
## Epoch 80/200 Iteration 240| Training loss: 0.9250
## Epoch 84/200 Iteration 250| Training loss: 0.9939
## Epoch 87/200 Iteration 260| Training loss: 0.7587
## Epoch 90/200 Iteration 270| Training loss: 0.8056
## Epoch 94/200 Iteration 280| Training loss: 0.8981
## Epoch 97/200 Iteration 290| Training loss: 0.6600
## Epoch 100/200 Iteration 300| Training loss: 0.7264
## Epoch 104/200 Iteration 310| Training loss: 0.8183
## Epoch 107/200 Iteration 320| Training loss: 0.5950
## Epoch 110/200 Iteration 330| Training loss: 0.6600
## Epoch 114/200 Iteration 340| Training loss: 0.7615
## Epoch 117/200 Iteration 350| Training loss: 0.5359
## Epoch 120/200 Iteration 360| Training loss: 0.5880
## Epoch 124/200 Iteration 370| Training loss: 0.7083
## Epoch 127/200 Iteration 380| Training loss: 0.4892
## Epoch 130/200 Iteration 390| Training loss: 0.5403
## Epoch 134/200 Iteration 400| Training loss: 0.6656
## Epoch 137/200 Iteration 410| Training loss: 0.4578
## Epoch 140/200 Iteration 420| Training loss: 0.5025
## Epoch 144/200 Iteration 430| Training loss: 0.6201
## Epoch 147/200 Iteration 440| Training loss: 0.4282
## Epoch 150/200 Iteration 450| Training loss: 0.4731
## Epoch 154/200 Iteration 460| Training loss: 0.5919
## Epoch 157/200 Iteration 470| Training loss: 0.3930
## Epoch 160/200 Iteration 480| Training loss: 0.4395
## Epoch 164/200 Iteration 490| Training loss: 0.5546
## Epoch 167/200 Iteration 500| Training loss: 0.3691
## Epoch 170/200 Iteration 510| Training loss: 0.4128
## Epoch 174/200 Iteration 520| Training loss: 0.5341
## Epoch 177/200 Iteration 530| Training loss: 0.3492
## Epoch 180/200 Iteration 540| Training loss: 0.3915
## Epoch 184/200 Iteration 550| Training loss: 0.5111
## Epoch 187/200 Iteration 560| Training loss: 0.3380
## Epoch 190/200 Iteration 570| Training loss: 0.3659
## Epoch 194/200 Iteration 580| Training loss: 0.4892
## Epoch 197/200 Iteration 590| Training loss: 0.3154
## Epoch 200/200 Iteration 600| Training loss: 0.3333del rnn
np.random.seed(123)
rnn = CharRNN(len(chars), sampling=True)## << lstm_outputs >> Tensor("rnn/transpose_1:0", shape=(1, 1, 128), dtype=float32)
## Tensor("probabilities:0", shape=(1, 32), dtype=float32)print(rnn.sample(ckpt_dir='./model-200/',
output_length=500))## The coket lnd starts
## holding back mount hold and holding R underarm choke hold
## startss hold
## ng fuld mount hold continues
## holding full mount hold continues
## holding back mount hold continues
## holding bbckk hokd cottinues
## holding back mount hold couttinues and cangho in R arm hold
## holding baud mount hold ant holding back mount hold starts and holding back mount hold ant holding back mount hold holding back moutt hold starss and holding back mount hold continues
## holding back mougt hold continues,h alding