NFL Bowl 2024 on RMarkdown
Genevieve Torkornoo
12/12/2024
Introduction
Here is an intro section.
Description of Project
For the first analysis our goal is the figure out which variable have a high effect on the success of a touch down. To create the dataset possession, we used the following features from df: gameId, minutes, seconds, totalSeconds, quarter, endZonePlays, yardsGained, absoluteYardlineNumber, and playDescription. Since the dataset did not include variables for the first possession of the ball in the game, first down, and touchdown success (where a touchdown is considered successful if playDescription contains the word ‘touchdown’), We decided to create variables for these using binary values for success (1) and failure (0). We also created the following variables: firstPossessionGame: success is equal to 1 and quarter equal to 1 and row is equal to 1 GoodYardsGained Run: Yards gained greater than or equal to 15.FirstDownZoneGame: If endZonePlays is 1 and firstPossessionInGame is 1, it is considered a success.RedZonePlays: If absoluteYardlineNumber is greater than or equal to 20 and touchdownSuccess is 1, it is considered a red zone play.”
Performing a t-test on plays in the red zone and the yards gained, We found that the t-value is -1715.6, meaning the variables are similar to each other. The p-value is 2.2e-16, which is less than 0.05, meaning there is strong evidence that there is a significant relationship between plays in the red zone and yards gained.
We created a dataframe called “firstTen” to investigate the predicted outcome of a touchdown between multiple variables: success rate of touchdowns, first ten minutes, start time, red zone plays, yards gained, and score margin. We then performed a multiple regression model to predict success based on the selected independent variables. All of the coefficients had p-values less than 0.001, indicating that the relationships between the independent variables (like red zone plays, yards gained, etc.) and the dependent variable (touchdown success) are statistically significant. The coefficient for red zone plays is 0.9841, which is positive. This means that as the number of red zone plays increases, touchdown success also increases, indicating a positive relationship between red zone plays and touchdown success. Similarly, yards gained has a positive coefficient of 0.004546, indicating that as yards gained increases, touchdown success also increases. On the other hand, firstTenMin has a negative coefficient of -0.008091, suggesting that as the value of firstTenMin increases, touchdown success decreases, showing a negative relationship between firstTenMin and touchdown success.
Next, We performed a Random Forest analysis. Since the data had some missing values (NA), they needed to be cleaned before performing the Random Forest. The Random Forest was performed with the touchdown success variable as the independent variable and the cleaned dataset subset_firstTen as the dependent variables. The Random Forest consisted of 100 trees, and the variance explained was 98.08%, meaning that 98.08% of the data in the subset is accounted for in the test. The mean of squared residuals is 0.0006208803, indicating that the predictions are accurate. We ran the mean squared error for the predicted regression and got 0.0001212466, which means the predictions are extremely accurate. Next, We ran an R-squared test for the predicted regression, and the result was 0.9962552. An R-squared value of 0.9962552 means that the model does an excellent job of explaining the relationship between the features and the target variable. Lastly, We ran an importance test to see which variables have the most impact on touchdown success. Yards gained had the highest importance with a range of (47.807286, 56.437469), followed by red zone plays with (36.797372, 62.362114), absolute yard line number with (23.440445, 33.3673659), and start minute with (17.309417, 14.922503).
We created a data frame to store touchdown success, play ID, NFL ID, and game ID. Using that dataframe, We found which player had the most touchdowns. The player with the most touchdowns was Kevin Zeitler, with 320 touchdowns.
Using the player with the most touchdowns, We took data from a successful touchdowns by using the x and y distances. We was able to create an animation showing his movement that led to his touchdown. In the animation, we see how the team was able to gaining yards to position himself in a way that allows him to successfully intercept the football and score a touchdown.
For our second analysis, the objective is to determine whether pre-snap motion in a given play has effects on Quarterback Performance. Considering a data frame of critical plays, defined as third and fourth downplays and red zone plays, We calculated performance metrics for Quarterback Performance with and without pre-snap motion. These include averaging the variables timetothrow, yards gained, and calculating a completion rate considering where the variable. We then performed two- sample t tests on the time to throw and yards gained for with and without motion respectively before performing a proportion test on completion rate to determine if Quarterbacks, especially in critical moments of gameplay, are affected by plays with pre-snap motion. A deemed impact would be higher time to throw and lower yards gained. In the case of completion rate, the proportion test would compare the completion rate of plays with and without motion By doing theses analyses we will be able to determine if Quarterback performance is affected by plays with pre snap motion. Handling missing data: to remove empty data points, I opted to use and only include data in critical plays that had a listed value for each column, otherwise delete the row.
Visualizations: We chose to visualize the Time to throw variable and the completion rate of the plays in respect to the impact of pre snap motion. To do this we designed a scatter plot for timetothrow, modeling the density and spread of the values. Interpretation: Given the p value for timetothrow and yards gained being considerably over the threshold, we fail to reject the null hypothesis deeming the data statistically insignificant. Given this, the graph doesn’t align with our hypothesis. To model the results of the proportion test, we opted for a violin plot, depicting the spread of the data in response to our factors. this depicts the spread of the data in response to pre snap motion for complete vs incomplete plays. As shown in the plot, the with motion plots are more spread out in comparison to without where the plots are consolidated and wider, suggesting less distribution. This is significant to our data, proving that pre snap motion influenced completion rate as it relates to Quarterback performance.
setwd("C:/Users/genny/Documents/IS470 Sports Analysis/2024")
#LOADING FILES
directory <- paste(getwd(), "Data/NFLBDB2025")
source('https://raw.githubusercontent.com/ptallon/SportsAnalytics_Fall2024/main/SharedCode.R')
load_packages(c("ggplot2","httr", "dplyr", "data.table", "gganimate", "hrbrthemes", "tidyr", "gganimate", "gbm", "caret", "lubridate", "randomForest" , "broom" , "stats"))
t1 <- fread("tracking_week_1.csv")
games <- fread("games.csv")
plays <- fread("plays.csv")
players <- fread("players.csv")
player_play<- fread("player_play.csv")
df <- left_join( t1, games, by = c("gameId"))
df <- left_join( df, plays, by = c("gameId" , "playId"))
df <- left_join( df, players, by = c("nflId"))
df <- left_join( df, player_play, by = c("gameId" , "playId" , "nflId"))
win <- df %>%
select(preSnapHomeScore, preSnapVisitorScore, preSnapHomeTeamWinProbability, preSnapVisitorTeamWinProbability,
passResult, passLength , yardsToGo , yardsGained, dis, displayName.x, displayName.y, down ,gameId, gameClock, quarter, possessionTeam, yardlineNumber, absoluteYardlineNumber, playDescription ) %>%
group_by(displayName.x) %>%
mutate( success = ifelse(passResult == "C", 1, 0) ,
fail = ifelse(passResult == "I" , 1, 0) ,
intercepted = ifelse(passResult == "IN" , 1, 0),
scramble = ifelse(passResult == "R" , 1 , 0 ) ,
sack = ifelse (passResult == "S" , 1 , 0) ) %>%
arrange(success) %>% data.frame()
win <- win %>%
mutate( faryard = ifelse( success == "1" & yardsGained >= 10, 1, 0))%>%
arrange(faryard) %>% data.frame()
cor(win$success , win$faryard)## [1] 0.5931852# finding the person with the most successful presnap
mostSucessSnap <- win %>%
group_by(displayName.x, gameId) %>%
reframe(total_success = sum(success),
count = n(),
results_success = total_success / count) %>%
select(displayName.x, results_success, gameId) %>%
arrange(-results_success) %>%
data.frame()
mostSucessSnap## displayName.x results_success gameId
## 1 Aaron Patrick 1.00000000 2022091200
## 2 Brandin Echols 1.00000000 2022091107
## 3 Coby Bryant 1.00000000 2022091200
## 4 Cole Van Lanen 1.00000000 2022091109
## 5 Darren Hall 1.00000000 2022091100
## 6 Elijah Campbell 1.00000000 2022091106
## 7 Jalyn Armour-Davis 1.00000000 2022091107
## 8 Jonathan Williams 1.00000000 2022091109
## 9 Keir Thomas 1.00000000 2022090800
## 10 Keisean Nixon 1.00000000 2022091112
## 11 Kelvin Joseph 1.00000000 2022091113
## 12 Kendrick Bourne 1.00000000 2022091106
## 13 P.J. Locke 1.00000000 2022091200
## 14 Shaka Toney 1.00000000 2022091109
## 15 Tim Jones 1.00000000 2022091109
## 16 Tony Jones 1.00000000 2022091100
## 17 Juwann Winfree 0.93370787 2022091112
## 18 Brycen Hopkins 0.89649416 2022090800
## 19 J.T. Gray 0.88948787 2022091100
## 20 Jordan Love 0.87934560 2022091112
## 21 Jonathon Cooper 0.81626794 2022091200
## 22 Ross Blacklock 0.80976096 2022091112
## 23 P.J. Williams 0.80532446 2022091100
## 24 Jeff Smith 0.80175781 2022091107
## 25 Stanley Morgan 0.79314041 2022091103
## 26 James Proche 0.77586207 2022091107
## 27 Caden Sterns 0.75885559 2022091200
## 28 Brandon Powell 0.73934426 2022090800
## 29 Ezekiel Turner 0.70505965 2022091110
## 30 Dennis Gardeck 0.69208633 2022091110
## 31 Robert Jones 0.68329718 2022091106
## 32 Armani Rogers 0.68292683 2022091109
## 33 Terrell Lewis 0.68110114 2022090800
## 34 Jerick McKinnon 0.66031599 2022091110
## 35 Jace Whittaker 0.65856777 2022091110
## 36 Garrett Wilson 0.65417733 2022091107
## 37 Dee Alford 0.65380997 2022091100
## 38 Jamal Agnew 0.65240642 2022091109
## 39 Kyle Hamilton 0.64953072 2022091107
## 40 Clelin Ferrell 0.64275093 2022091111
## 41 Ty Montgomery 0.64168260 2022091106
## 42 Zack Moss 0.63237566 2022090800
## 43 Travis Homer 0.63182168 2022091200
## 44 Joshua Kalu 0.62970498 2022091108
## 45 Joshua Kelley 0.61445221 2022091111
## 46 Jordan Fuller 0.60457774 2022090800
## 47 Josh Uche 0.60438198 2022091106
## 48 Deonte Harty 0.59502807 2022091100
## 49 JuJu Smith-Schuster 0.59458333 2022091110
## 50 Bryce Hall 0.59385666 2022091107
## 51 Matt Henningsen 0.59375000 2022091200
## 52 Broderick Washington 0.59267127 2022091107
## 53 Shelby Harris 0.59228119 2022091200
## 54 DK Metcalf 0.58791458 2022091200
## 55 Albert Okwuegbunam 0.58756910 2022091200
## 56 Travis Kelce 0.58579204 2022091110
## 57 Duke Riley 0.57929760 2022091106
## 58 Marquise Goodwin 0.57884330 2022091200
## 59 Nyheim Hines 0.57663874 2022091105
## 60 Quinton Jefferson 0.57636224 2022091200
## 61 K'Waun Williams 0.57442557 2022091200
## 62 Richard Rodgers 0.57422969 2022091111
## 63 Isaiah McKenzie 0.57336621 2022090800
## 64 Anthony Averett 0.57260274 2022091111
## 65 Javonte Williams 0.56822054 2022091200
## 66 Markus Golden 0.56684266 2022091110
## 67 Boye Mafe 0.56579395 2022091200
## 68 Trey Smith 0.56545851 2022091110
## 69 Brent Urban 0.56533540 2022091107
## 70 Olasunkanmi Adeniyi 0.56317690 2022091108
## 71 Caleb Farley 0.56301824 2022091108
## 72 Keion Crossen 0.56110020 2022091106
## 73 K.J. Hamler 0.55973216 2022091200
## 74 Chris Rumph 0.55805065 2022091111
## 75 Tommy Sweeney 0.55395683 2022090800
## 76 Jeremy Reaves 0.55334115 2022091109
## 77 Justice Hill 0.55322967 2022091107
## 78 Odafe Oweh 0.54956499 2022091107
## 79 Tyler Lockett 0.54856087 2022091200
## 80 Michael Thomas 0.54652687 2022091100
## 81 Isaiah Simmons 0.54594189 2022091110
## 82 Tanner Hudson 0.54444796 2022091108
## 83 Josh Jones 0.54412274 2022091200
## 84 Eric Johnson 0.54347826 2022091105
## 85 Justin Coleman 0.54237687 2022091200
## 86 Samaje Perine 0.54208576 2022091103
## 87 Brandon Stephens 0.54052863 2022091107
## 88 Justin Houston 0.53694735 2022091107
## 89 Alohi Gilman 0.53531111 2022091111
## 90 Graham Glasgow 0.53249612 2022091200
## 91 Mike Gesicki 0.53155080 2022091106
## 92 Malik Harrison 0.53117693 2022091107
## 93 Breece Hall 0.53108348 2022091107
## 94 Andrew Wylie 0.52928701 2022091110
## 95 Budda Baker 0.52928701 2022091110
## 96 Orlando Brown 0.52928701 2022091110
## 97 Patrick Mahomes 0.52928701 2022091110
## 98 Ed Oliver 0.52856083 2022090800
## 99 Sidney Jones 0.52420614 2022091200
## 100 Stefon Diggs 0.52376852 2022090800
## 101 J.D. McKissic 0.52352326 2022091109
## 102 Arden Key 0.52196532 2022091109
## 103 Wan'Dale Robinson 0.52003023 2022091108
## 104 Jerry Tillery 0.51812256 2022091111
## 105 Jabrill Peppers 0.51622060 2022091106
## 106 Denzel Perryman 0.51547856 2022091111
## 107 Tyreek Hill 0.51404354 2022091106
## 108 Dre'Mont Jones 0.51390047 2022091200
## 109 Malik Reed 0.51380552 2022091103
## 110 Corey Davis 0.51377890 2022091107
## 111 Jerry Jeudy 0.51296112 2022091200
## 112 Demetrius Flannigan-Fowles 0.51290323 2022091102
## 113 Marco Wilson 0.51206644 2022091110
## 114 Mecole Hardman 0.51034126 2022091110
## 115 Kyle Fuller 0.50840291 2022091107
## 116 Zach Allen 0.50834817 2022091110
## 117 Derrick Nnadi 0.50744202 2022091110
## 118 Jarvis Landry 0.50734590 2022091100
## 119 Alijah Vera-Tucker 0.50670860 2022091107
## 120 Chuck Clark 0.50670860 2022091107
## 121 Connor McGovern 0.50670860 2022091107
## 122 George Fant 0.50670860 2022091107
## 123 Joe Flacco 0.50670860 2022091107
## 124 Laken Tomlinson 0.50670860 2022091107
## 125 Marcus Williams 0.50670860 2022091107
## 126 Marlon Humphrey 0.50670860 2022091107
## 127 Max Mitchell 0.50670860 2022091107
## 128 Patrick Queen 0.50670860 2022091107
## 129 David Sills 0.50658712 2022091108
## 130 Demarcus Robinson 0.50540865 2022091107
## 131 Michael Danna 0.50481431 2022091110
## 132 Noah Fant 0.50404427 2022091200
## 133 Aaron Donald 0.50378195 2022090800
## 134 Justin Madubuike 0.50340000 2022091107
## 135 Keenan Allen 0.50303247 2022091111
## 136 Myles Bryant 0.50281426 2022091106
## 137 Courtland Sutton 0.50203387 2022091200
## 138 Darrell Taylor 0.50124824 2022091200
## 139 Jaylen Waddle 0.49947862 2022091106
## 140 Marquez Valdes-Scantling 0.49787727 2022091110
## 141 Cody Barton 0.49641055 2022091200
## 142 Jaelan Phillips 0.49579138 2022091106
## 143 Ryan Neal 0.49575071 2022091200
## 144 Cameron Fleming 0.49550857 2022091200
## 145 Dalton Risner 0.49550857 2022091200
## 146 Garett Bolles 0.49550857 2022091200
## 147 Jordyn Brooks 0.49550857 2022091200
## 148 Lloyd Cushenberry 0.49550857 2022091200
## 149 Michael Jackson 0.49550857 2022091200
## 150 Quandre Diggs 0.49550857 2022091200
## 151 Russell Wilson 0.49550857 2022091200
## 152 Baron Browning 0.49535884 2022091200
## 153 Leki Fotu 0.49486335 2022091110
## 154 Casey Toohill 0.49393694 2022091109
## 155 Byron Murphy 0.49340990 2022091110
## 156 Creed Humphrey 0.49340990 2022091110
## 157 Jalen Thompson 0.49340990 2022091110
## 158 Joe Thuney 0.49340990 2022091110
## 159 football 0.49290425 2022091200
## 160 Ernest Jones 0.49254545 2022090800
## 161 Ben Banogu 0.49142857 2022091105
## 162 Damarion Williams 0.49086758 2022091107
## 163 Tre Norwood 0.49031095 2022091103
## 164 Simi Fehoko 0.48998459 2022091113
## 165 Tariq Woolen 0.48945055 2022091200
## 166 Skyy Moore 0.48940179 2022091110
## 167 Abraham Lucas 0.48928661 2022091200
## 168 Austin Blythe 0.48928661 2022091200
## 169 Charles Cross 0.48928661 2022091200
## 170 Gabe Jackson 0.48928661 2022091200
## 171 Geno Smith 0.48928661 2022091200
## 172 Justin Simmons 0.48928661 2022091200
## 173 Kareem Jackson 0.48928661 2022091200
## 174 Patrick Surtain 0.48928661 2022091200
## 175 Phil Haynes 0.48928661 2022091200
## 176 Ronald Darby 0.48928661 2022091200
## 177 Chris Olave 0.48850673 2022091100
## 178 Arnold Ebiketie 0.48536649 2022091100
## 179 Zach Ertz 0.48525848 2022091110
## 180 Cam Akers 0.48521082 2022090800
## 181 Andrew Van Ginkel 0.48487713 2022091106
## 182 Tyrie Cleveland 0.48466717 2022091200
## 183 Mike Ford 0.48447205 2022091100
## 184 Alec Pierce 0.48441054 2022091105
## 185 Michael Carter 0.48340901 2022091107
## 186 Elijah Moore 0.48336393 2022091107
## 187 Isaiah Wynn 0.48259286 2022091106
## 188 Curtis Samuel 0.48239489 2022091109
## 189 Braxton Berrios 0.48159296 2022091107
## 190 Greg Gaines 0.48019450 2022090800
## 191 Devon Kennard 0.48013712 2022091110
## 192 Jake Kumerow 0.47977528 2022090800
## 193 Jonas Griffith 0.47877569 2022091200
## 194 Tyson Alualu 0.47688951 2022091103
## 195 Tyler Conklin 0.47667389 2022091107
## 196 Kyle Van Noy 0.47610823 2022091111
## 197 Ta'Quon Graham 0.47480769 2022091100
## 198 Alex Singleton 0.47458506 2022091200
## 199 Trevon Moehrig 0.47436265 2022091111
## 200 Josh Palmer 0.47414156 2022091111
## 201 Victor Dimukeje 0.47171797 2022091110
## 202 David Long 0.47133260 2022090800
## 203 Gerald Everett 0.46995074 2022091111
## 204 Zaven Collins 0.46979998 2022091110
## 205 Uchenna Nwosu 0.46925566 2022091200
## 206 Randy Gregory 0.46606665 2022091200
## 207 Juwan Johnson 0.46500078 2022091100
## 208 Wes Schweitzer 0.46497373 2022091109
## 209 C.J. Uzomah 0.46417798 2022091107
## 210 Roderic Teamer 0.46392985 2022091111
## 211 D.J. Jones 0.46331500 2022091200
## 212 Justin Watson 0.46309013 2022091110
## 213 Jalen Guyton 0.46268657 2022091111
## 214 DeAndre Carter 0.46205734 2022091111
## 215 Travon Walker 0.46193016 2022091109
## 216 Chase Edmonds 0.46139740 2022091106
## 217 Divine Deablo 0.46027982 2022091111
## 218 Clyde Edwards-Helaire 0.46002491 2022091110
## 219 K'Lavon Chaisson 0.46001649 2022091109
## 220 Jack Jones 0.45952836 2022091106
## 221 Jamir Jones 0.45942768 2022091103
## 222 Von Miller 0.45921278 2022090800
## 223 Jonathan Garvin 0.45710456 2022091112
## 224 football 0.45578376 2022091110
## 225 Jaylen Watson 0.45578043 2022091110
## 226 Grady Jarrett 0.45383760 2022091100
## 227 Calais Campbell 0.45369045 2022091107
## 228 Nate Hobbs 0.45211631 2022091111
## 229 Dawuane Smoot 0.45183543 2022091109
## 230 Michael Dogbe 0.45160711 2022091110
## 231 Josiah Deguara 0.45075758 2022091112
## 232 Bobby Wagner 0.44988889 2022090800
## 233 Dion Dawkins 0.44988889 2022090800
## 234 Gabe Davis 0.44988889 2022090800
## 235 Jalen Ramsey 0.44988889 2022090800
## 236 Josh Allen 0.44988889 2022090800
## 237 Mitch Morse 0.44988889 2022090800
## 238 Rodger Saffold 0.44988889 2022090800
## 239 Ryan Bates 0.44988889 2022090800
## 240 Spencer Brown 0.44988889 2022090800
## 241 Taylor Rapp 0.44988889 2022090800
## 242 Chris Jones 0.44985673 2022091110
## 243 Russell Gage 0.44961665 2022091113
## 244 Terry McLaurin 0.44898660 2022091109
## 245 Giovanni Ricci 0.44833729 2022091101
## 246 Cooper Rush 0.44830827 2022091113
## 247 Dane Jackson 0.44817419 2022090800
## 248 Jordan Poyer 0.44817419 2022090800
## 249 Micah Hyde 0.44817419 2022090800
## 250 Taron Johnson 0.44817419 2022090800
## 251 Kingsley Enagbare 0.44755700 2022091112
## 252 Bradley Chubb 0.44713901 2022091200
## 253 football 0.44710420 2022091107
## 254 Jakobi Meyers 0.44580299 2022091106
## 255 Corey Linsley 0.44453030 2022091111
## 256 Johnathan Abram 0.44453030 2022091111
## 257 Justin Herbert 0.44453030 2022091111
## 258 Matt Feiler 0.44453030 2022091111
## 259 Rashawn Slater 0.44453030 2022091111
## 260 Trey Pipkins 0.44453030 2022091111
## 261 Zion Johnson 0.44453030 2022091111
## 262 Mike Williams 0.44431051 2022091111
## 263 Greg Dortch 0.44398451 2022091110
## 264 Walker Little 0.44347826 2022091109
## 265 Sony Michel 0.44207028 2022091111
## 266 Otito Ogbonnia 0.44126285 2022091111
## 267 football 0.44048668 2022090800
## 268 Nick Vigil 0.44036009 2022091110
## 269 Avery Williams 0.44031755 2022091100
## 270 Neville Gallimore 0.44012945 2022091113
## 271 Robert Tonyan 0.43998803 2022091112
## 272 Matt Milano 0.43964846 2022090800
## 273 Tremaine Edmunds 0.43964846 2022090800
## 274 Chris Wormley 0.43960890 2022091103
## 275 Gregory Rousseau 0.43924783 2022090800
## 276 Cole Strange 0.43915132 2022091106
## 277 Mark Ingram 0.43890555 2022091100
## 278 Hunter Henry 0.43847567 2022091106
## 279 Montravius Adams 0.43771429 2022091103
## 280 Desmond King 0.43770140 2022091105
## 281 Deatrich Wise 0.43737387 2022091106
## 282 Carlos Basham 0.43734867 2022090800
## 283 Jordan Phillips 0.43715961 2022090800
## 284 Darious Williams 0.43713919 2022091109
## 285 Lawrence Guy 0.43684451 2022091106
## 286 Chandler Jones 0.43651483 2022091111
## 287 Percy Butler 0.43555556 2022091109
## 288 Carlos Dunlap 0.43542435 2022091110
## 289 Adrian Phillips 0.43536366 2022091106
## 290 Troy Hill 0.43506494 2022090800
## 291 Jacob Martin 0.43500157 2022091107
## 292 Rashad Fenton 0.43424469 2022091110
## 293 Devin Lloyd 0.43378995 2022091109
## 294 Zach Tom 0.43333333 2022091112
## 295 Nick Scott 0.43283766 2022090800
## 296 Payton Turner 0.43277849 2022091100
## 297 Maxx Crosby 0.43211827 2022091111
## 298 Josh Allen 0.43209725 2022091109
## 299 Hassan Haskins 0.43171806 2022091108
## 300 K.J. Osborn 0.43157204 2022091112
## 301 Brian Allen 0.43125273 2022090800
## 302 Coleman Shelton 0.43125273 2022090800
## 303 Cooper Kupp 0.43125273 2022090800
## 304 David Edwards 0.43125273 2022090800
## 305 Joseph Noteboom 0.43125273 2022090800
## 306 Matthew Stafford 0.43125273 2022090800
## 307 Rob Havenstein 0.43125273 2022090800
## 308 Kendal Vickers 0.43117409 2022091111
## 309 Nico Collins 0.43057595 2022091105
## 310 Dawson Knox 0.42986366 2022090800
## 311 Rashaad Penny 0.42938153 2022091200
## 312 Jaylinn Hawkins 0.42906369 2022091100
## 313 Richie Grant 0.42906369 2022091100
## 314 Bilal Nichols 0.42906196 2022091111
## 315 Rashaan Evans 0.42901271 2022091100
## 316 Nick Allegretti 0.42813565 2022091110
## 317 Jonathan Jones 0.42791696 2022091106
## 318 Allen Robinson 0.42739604 2022090800
## 319 Quinton Bell 0.42730978 2022091100
## 320 Duron Harmon 0.42638241 2022091111
## 321 Alvin Kamara 0.42626559 2022091100
## 322 Jerry Hughes 0.42513904 2022091105
## 323 Stephen Anderson 0.42416226 2022091110
## 324 A'Shawn Robinson 0.42391304 2022090800
## 325 Travis Etienne 0.42340013 2022091109
## 326 Boston Scott 0.42339833 2022091104
## 327 Nelson Agholor 0.42274543 2022091106
## 328 Jonathan Greenard 0.42261143 2022091105
## 329 Tyquan Lewis 0.42239881 2022091105
## 330 DaQuan Jones 0.42235630 2022090800
## 331 football 0.42232760 2022091111
## 332 Raekwon McMillan 0.42178583 2022091106
## 333 Davante Adams 0.42163311 2022091111
## 334 Eno Benjamin 0.42012090 2022091110
## 335 Darrell Henderson 0.41993399 2022090800
## 336 A.J. Terrell 0.41978610 2022091100
## 337 Andrus Peat 0.41978610 2022091100
## 338 Cesar Ruiz 0.41978610 2022091100
## 339 Erik McCoy 0.41978610 2022091100
## 340 Jameis Winston 0.41978610 2022091100
## 341 James Hurst 0.41978610 2022091100
## 342 Mykal Walker 0.41978610 2022091100
## 343 Ryan Ramczyk 0.41978610 2022091100
## 344 Kaiir Elam 0.41974170 2022090800
## 345 Willie Gay 0.41966263 2022091110
## 346 DeShawn Williams 0.41930442 2022091200
## 347 Amik Robertson 0.41925777 2022091111
## 348 Zach Sieler 0.41913660 2022091106
## 349 Charlie Heck 0.41883519 2022091105
## 350 Brandon Jones 0.41873442 2022091106
## 351 David Andrews 0.41873442 2022091106
## 352 DeVante Parker 0.41873442 2022091106
## 353 Jerome Baker 0.41873442 2022091106
## 354 Jevon Holland 0.41873442 2022091106
## 355 Mac Jones 0.41873442 2022091106
## 356 Michael Onwenu 0.41873442 2022091106
## 357 Trenton Brown 0.41873442 2022091106
## 358 Xavien Howard 0.41873442 2022091106
## 359 Mack Hollins 0.41863246 2022091111
## 360 Daron Payne 0.41856061 2022091109
## 361 football 0.41849521 2022091106
## 362 Hunter Renfrow 0.41827482 2022091111
## 363 Connor Williams 0.41826026 2022091106
## 364 Devin McCourty 0.41826026 2022091106
## 365 Liam Eichenberg 0.41826026 2022091106
## 366 Robert Hunt 0.41826026 2022091106
## 367 Tua Tagovailoa 0.41826026 2022091106
## 368 Lorenzo Carter 0.41823282 2022091100
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## 847 Antoine Winfield 0.30623020 2022091113
## 848 Carlton Davis 0.30623020 2022091113
## 849 Dalton Schultz 0.30623020 2022091113
## 850 Devin White 0.30623020 2022091113
## 851 Jamel Dean 0.30623020 2022091113
## 852 Lavonte David 0.30623020 2022091113
## 853 Mike Edwards 0.30623020 2022091113
## 854 Terence Steele 0.30623020 2022091113
## 855 Tyler Biadasz 0.30623020 2022091113
## 856 Tyler Smith 0.30623020 2022091113
## 857 Zack Martin 0.30623020 2022091113
## 858 Khalen Saunders 0.30576007 2022091110
## 859 Dallas Goedert 0.30547152 2022091104
## 860 Chauncey Golston 0.30484758 2022091113
## 861 Rakeem Nunez-Roches 0.30459057 2022091113
## 862 Trey Hendrickson 0.30451128 2022091103
## 863 Justin Hollins 0.30433990 2022090800
## 864 Amani Oruwariye 0.30392950 2022091104
## 865 John Jenkins 0.30366807 2022091106
## 866 Andy Isabella 0.30361351 2022091110
## 867 Dameon Pierce 0.30272788 2022091105
## 868 Bradley Roby 0.30083963 2022091100
## 869 Chris Lindstrom 0.30083963 2022091100
## 870 Demario Davis 0.30083963 2022091100
## 871 Drew Dalman 0.30083963 2022091100
## 872 Elijah Wilkinson 0.30083963 2022091100
## 873 Jake Matthews 0.30083963 2022091100
## 874 Kaleb McGary 0.30083963 2022091100
## 875 Marcus Mariota 0.30083963 2022091100
## 876 Marcus Maye 0.30083963 2022091100
## 877 Marshon Lattimore 0.30083963 2022091100
## 878 Tyrann Mathieu 0.30083963 2022091100
## 879 Jelani Woods 0.30081301 2022091105
## 880 Kyle Pitts 0.30059880 2022091100
## 881 Jeff Okudah 0.30037558 2022091104
## 882 Isaac Seumalo 0.30021564 2022091104
## 883 Jalen Hurts 0.30021564 2022091104
## 884 Jason Kelce 0.30021564 2022091104
## 885 Jordan Mailata 0.30021564 2022091104
## 886 Landon Dickerson 0.30021564 2022091104
## 887 Lane Johnson 0.30021564 2022091104
## 888 Malik Hooker 0.29961089 2022091113
## 889 Geoff Swaim 0.29960453 2022091108
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## 891 Joe Tryon 0.29830396 2022091113
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## 895 David Onyemata 0.29677334 2022091100
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## 940 Cordarrelle Patterson 0.27453027 2022091100
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## 1088 Equanimeous St. Brown 0.16565495 2022091102
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## 1092 Kevin Pierre-Louis 0.16105293 2022091105
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## 1097 Damien Wilson 0.15528205 2022091101
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## 1101 Arik Armstead 0.14873249 2022091102
## 1102 Nick Chubb 0.14746333 2022091101
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## 1104 Ryan Griffin 0.14348786 2022091102
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## 1106 Jonah Williams 0.14022140 2022090800
## 1107 Marquise Copeland 0.13963964 2022090800
## 1108 Brock Wright 0.13941267 2022091104
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## 1110 Matt Ioannidis 0.13802773 2022091101
## 1111 Yodny Cajuste 0.13735558 2022091106
## 1112 Cole Kmet 0.13722026 2022091102
## 1113 Noah Togiai 0.13661202 2022091104
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## 1116 Cade Otton 0.12423095 2022091113
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## 1122 C.J. Ham 0.08970100 2022091112
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## 1125 Trevis Gipson 0.08076248 2022091102
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## 1143 Dareke Young 0.00000000 2022091200
## 1144 Daxton Hill 0.00000000 2022091103
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## 1146 Dean Marlowe 0.00000000 2022091100
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## 1148 Deionte Thompson 0.00000000 2022091110
## 1149 Demetric Felton 0.00000000 2022091101
## 1150 Dennis Daley 0.00000000 2022091108
## 1151 Deommodore Lenoir 0.00000000 2022091102
## 1152 Devery Hamilton 0.00000000 2022091108
## 1153 Dyami Brown 0.00000000 2022091109
## 1154 Erik Harris 0.00000000 2022091100
## 1155 Geron Christian 0.00000000 2022091110
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## 1158 Ihmir Smith-Marsette 0.00000000 2022091102
## 1159 Isaac Rochell 0.00000000 2022091101
## 1160 Isaiah McDuffie 0.00000000 2022091112
## 1161 Jake Tonges 0.00000000 2022091102
## 1162 James Cook 0.00000000 2022090800
## 1163 Jamycal Hasty 0.00000000 2022091109
## 1164 Jaquan Johnson 0.00000000 2022090800
## 1165 Jeff Driskel 0.00000000 2022091105
## 1166 K'Von Wallace 0.00000000 2022091104
## 1167 Kalif Raymond 0.00000000 2022091104
## 1168 Kavontae Turpin 0.00000000 2022091113
## 1169 Ko Kieft 0.00000000 2022091113
## 1170 Luke Farrell 0.00000000 2022091109
## 1171 Luke Masterson 0.00000000 2022091111
## 1172 Malcolm Koonce 0.00000000 2022091111
## 1173 Malik Turner 0.00000000 2022091102
## 1174 Marcus Jones 0.00000000 2022091106
## 1175 Matt Nelson 0.00000000 2022091104
## 1176 Matthew Adams 0.00000000 2022091102
## 1177 Michael Burton 0.00000000 2022091110
## 1178 Mike Davis 0.00000000 2022091107
## 1179 Mike Pennel 0.00000000 2022091102
## 1180 Miles Killebrew 0.00000000 2022091103
## 1181 Mitchell Wilcox 0.00000000 2022091103
## 1182 Nakobe Dean 0.00000000 2022091104
## 1183 Perrion Winfrey 0.00000000 2022091101
## 1184 Peyton Hendershot 0.00000000 2022091113
## 1185 Phidarian Mathis 0.00000000 2022091109
## 1186 Phil Hoskins 0.00000000 2022091101
## 1187 Prince Tega Wanogho 0.00000000 2022091110
## 1188 Quincy Roche 0.00000000 2022091108
## 1189 Quintez Cephus 0.00000000 2022091104
## 1190 Rodney McLeod 0.00000000 2022091105
## 1191 Shane Zylstra 0.00000000 2022091104
## 1192 Siran Neal 0.00000000 2022090800
## 1193 Storm Norton 0.00000000 2022091111
## 1194 Terrel Bernard 0.00000000 2022090800
## 1195 Tony Adams 0.00000000 2022091107
## 1196 Trestan Ebner 0.00000000 2022091102
## 1197 Tyrel Dodson 0.00000000 2022090800
## 1198 Tyron Johnson 0.00000000 2022091111preSnapDis <- ifelse( win$success == 1 & win$yardsToGo <= 3, 1, 0)
t.test(win$success,preSnapDis )##
## Welch Two Sample t-test
##
## data: win$success and preSnapDis
## t = 1675.9, df = 9370319, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3264258 0.3271902
## sample estimates:
## mean of x mean of y
## 0.36638071 0.03957268cor(win$success,preSnapDis)## [1] 0.2669398findOpenDrives <- df %>%
select(playNullifiedByPenalty, penaltyYards.x, penaltyYards.y, fumbleLost, hadInterception,
penaltyNames, gameId, yardsGained, displayName.x) %>%
# Filter out plays nullified by penalty, where penalty yards are <= 10, and where yards gained >= 0
filter(playNullifiedByPenalty == "N", penaltyYards.x <= 10, penaltyYards.y <= 10, yardsGained >= 0) %>%
mutate(
nullPlay = ifelse(playNullifiedByPenalty == "N", 1, 0), # Create nullPlay column
turnovers = fumbleLost + hadInterception # Calculate total turnovers
) %>%
summarise(
totalYardsGained = sum(yardsGained),
numPlays = n(),
totalPenalty = sum(nullPlay), # Count how many times this happened by summing the nullPlay values
totalTurnovers = sum(turnovers) # Sum turnovers
)
findOpenDrives <- findOpenDrives %>%
filter(
totalTurnovers == 0, # No turnovers
totalPenalty <= 15, # Total penalties less than or equal to 15 yards
totalYardsGained > 0 # Positive yards gained
)
# what makes a play an opendrive
openDrives <- win %>%
select(yardsGained, success,gameId, gameClock, quarter,
possessionTeam, absoluteYardlineNumber, displayName.x, displayName.y, playDescription) %>%
separate(gameClock , into = c("minutes","seconds"), sep = ":") %>%
mutate(minutes = as.numeric(minutes),
seconds = as.numeric(seconds),
totalSeconds = minutes * 60 + seconds,
endZonePlays = ifelse(absoluteYardlineNumber <= 10 , 1, 0))%>%
group_by( gameId )%>%
arrange(gameId, totalSeconds)%>%
ungroup()
possession <- openDrives %>%
select(success, gameId, minutes, seconds, totalSeconds, quarter, endZonePlays, yardsGained, absoluteYardlineNumber, playDescription) %>%
mutate(
firstPossessionGame = ifelse(success == "1" & quarter == "1" & row_number() == 1, 1, 0),
firstPossessionQuarter = case_when(
success == "1" & quarter == "1" & row_number() == 1 ~ 1,
success == "1" & quarter == "2" & row_number() == 1 ~ 1,
success == "1" & quarter == "3" & row_number() == 1 ~ 1,
success == "1" & quarter == "4" & row_number() == 1 ~ 1,
TRUE ~ 0
),
touchdownSuccess = ifelse(grepl("touchdown", playDescription, ignore.case = TRUE), 1, 0)
) %>%
arrange(totalSeconds) %>%data.frame()
possession <- possession %>%
mutate( touchdownSuccess = ifelse(grepl("touchdown", playDescription, ignore.case = TRUE), 1, 0),
firstDownZoneGame = ifelse( endZonePlays == "1" & firstPossessionGame == "1",1 , 0),
firstDownZoneQuarter = ifelse( endZonePlays == "1" & firstPossessionQuarter == "1", 1 , 0 ),
goodYardsGainedRun = ifelse( yardsGained >= 10 , 1, 0),
goodYardsGainedPass = ifelse( yardsGained >= 15 , 1, 0),
redzoneplays = ifelse( absoluteYardlineNumber <= 20 & touchdownSuccess == "1", 1, 0 ))%>%
group_by( gameId )%>%
arrange(gameId, totalSeconds)
data.frame()## data frame with 0 columns and 0 rowst.test(possession$redzoneplays, possession$yardsGained)##
## Welch Two Sample t-test
##
## data: possession$redzoneplays and possession$yardsGained
## t = -1715.6, df = 7106159, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.950335 -5.936755
## sample estimates:
## mean of x mean of y
## 0.008837812 5.952382648firstTen <- df %>%
select(yardsGained, displayName.x, displayName.y ,gameId, gameClock, quarter, time, yardlineNumber, possessionTeam,
absoluteYardlineNumber, playDescription, homeFinalScore, visitorFinalScore ) %>%
group_by(displayName.x) %>%
mutate( touchdownSuccess = ifelse(grepl("touchdown", playDescription, ignore.case = TRUE), 1, 0)
,redzoneplays = ifelse( absoluteYardlineNumber <= 20 & touchdownSuccess == "1", 1, 0 ),
startTime = substr( time, 12, 16),
) %>%
arrange(displayName.x) %>% data.frame()
library(lubridate)
firstTen <- firstTen %>%
mutate(
startTime = ymd_hms(time), # Convert time to a datetime object
startMinute = minute(time), # Extract the minute of the play
firstTenMin = ifelse(startMinute <= 10, 1, 0), # Check if play is in the first 10 minutes
scoreMargin = homeFinalScore - visitorFinalScore) %>% # Calculate score margin # Time difference from previous play # Initialize the column for possession time
arrange(displayName.x) %>%
data.frame()
regression <- lm( touchdownSuccess ~ startTime + firstTenMin + redzoneplays + yardsGained + scoreMargin , data = firstTen)
summary(regression)##
## Call:
## lm(formula = touchdownSuccess ~ startTime + firstTenMin + redzoneplays +
## yardsGained + scoreMargin, data = firstTen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.30198 -0.03774 -0.01468 -0.00104 1.06536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.381e+01 1.643e+00 20.57 <2e-16 ***
## startTime -2.033e-08 9.883e-10 -20.57 <2e-16 ***
## firstTenMin -8.091e-03 1.442e-04 -56.11 <2e-16 ***
## redzoneplays 9.841e-01 6.205e-04 1585.88 <2e-16 ***
## yardsGained 4.546e-03 6.288e-06 722.91 <2e-16 ***
## scoreMargin 1.109e-04 5.868e-06 18.90 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1547 on 7104694 degrees of freedom
## Multiple R-squared: 0.2962, Adjusted R-squared: 0.2962
## F-statistic: 5.979e+05 on 5 and 7104694 DF, p-value: < 2.2e-16#1: random forest:
library(randomForest)
firstTen_clean <- na.omit(firstTen)
set.seed(42)
subset_firstTen <- firstTen_clean[sample(nrow(firstTen_clean), size = 5000), ]
# Fit the random forest model
rf_model <- randomForest(touchdownSuccess ~ ., data = subset_firstTen, importance = TRUE, ntree = 100)
# Print the model summary
print(rf_model)##
## Call:
## randomForest(formula = touchdownSuccess ~ ., data = subset_firstTen, importance = TRUE, ntree = 100)
## Type of random forest: regression
## Number of trees: 100
## No. of variables tried at each split: 6
##
## Mean of squared residuals: 0.003906439
## % Var explained: 86.75library(caret)
predictions <- predict(rf_model, subset_firstTen)
# Predict values
predictions_regression <- predict(rf_model, subset_firstTen)
# Calculate Mean Squared Error (MSE)
mse <- mean((predictions_regression - subset_firstTen$touchdownSuccess)^2)
print(mse)## [1] 0.0008135441# Calculate R-squared
rsq <- 1 - sum((predictions_regression - subset_firstTen$touchdownSuccess)^2) / sum((mean(subset_firstTen$touchdownSuccess) - subset_firstTen$touchdownSuccess)^2)
print(rsq)## [1] 0.9723996# 0.9962552
# Display feature importance for classification model
importance(rf_model)## %IncMSE IncNodePurity
## yardsGained 35.738457 26.0663437
## displayName.x 3.010952 2.0755501
## displayName.y 3.147563 2.0816131
## gameId 5.046517 2.6015834
## gameClock 7.531475 8.0096192
## quarter 7.268131 2.0168997
## time 7.673043 7.2079175
## yardlineNumber 16.999923 13.9872160
## possessionTeam 9.522972 4.2097436
## absoluteYardlineNumber 17.333569 14.4511980
## playDescription 6.648728 7.8984107
## homeFinalScore 10.296125 3.2307253
## visitorFinalScore 10.312241 6.3118887
## redzoneplays 22.969569 26.6342504
## startTime 6.859298 6.7370950
## startMinute 13.017700 6.7076917
## firstTenMin 5.442645 0.9120478
## scoreMargin 9.988833 3.2821188df <- df %>%
mutate( team = ifelse( club == homeTeamAbbr, "home" , ifelse( club == "football", "football", "away")))%>%
data.frame()
# player with most touchdown
touchdown <- df %>%
filter(displayName.x != "football", !is.na(team))%>%
mutate(touchdownSuccess = ifelse(grepl("touchdown", playDescription, ignore.case = TRUE), 1, 0))
# Count the touchdowns for each player
player_touchdowns <- touchdown %>%
group_by(nflId, displayName.x, gameId,playId, jerseyNumber) %>%
summarise(total_touchdowns = sum(touchdownSuccess)) %>%
arrange(desc(total_touchdowns)) # Sort by most touchdowns
# View the player with the most touchdowns
top_player <- player_touchdowns[1, ]
print(top_player)## # A tibble: 1 × 6
## # Groups: nflId, displayName.x, gameId, playId [1]
## nflId displayName.x gameId playId jerseyNumber total_touchdowns
## <int> <chr> <int> <int> <int> <dbl>
## 1 38557 Kevin Zeitler 2022091107 1642 70 320first_play <- touchdown %>%
filter(displayName.x == top_player$displayName.x) %>%
arrange(gameId, playId) %>%
slice(1)
#1: analysis
plays_df <- fread("plays.csv")
player_play_df <- fread("player_play.csv")
players_df <- fread("players.csv")
games_df <- fread("games.csv")
tracking_df <- rbindlist(list(
fread("tracking_week_1.csv"),
fread("tracking_week_2.csv"),
fread("tracking_week_3.csv"),
fread("tracking_week_4.csv"),
fread("tracking_week_5.csv")
))
#analysis of QB performance in regard to PSM
##critical game situations
third_down_plays <- plays_df %>%
filter(down == 3) %>%
filter(down == 4)
##red zone plays (>20 yards)
red_zone_plays <- plays_df %>% filter(yardlineNumber <= 20)
#combine
critical_plays <- bind_rows(third_down_plays, red_zone_plays) %>%
select(gameId, playId, passResult, timeToThrow, yardsGained) %>%
distinct()
critical_plays <- critical_plays %>%
left_join(player_play_df %>%
select(gameId, playId, inMotionAtBallSnap),
by = c("gameId", "playId")) %>%
select(passResult, timeToThrow, yardsGained, inMotionAtBallSnap)
colnames(critical_plays)## [1] "passResult" "timeToThrow" "yardsGained"
## [4] "inMotionAtBallSnap"critical_plays <- critical_plays[complete.cases(critical_plays), ]
#filter motionvsno-motion
motion_plays <- critical_plays %>% filter(inMotionAtBallSnap == TRUE)
no_motion_plays <- critical_plays %>% filter(inMotionAtBallSnap == FALSE)
#performance metrics
motion_avg_time_to_throw <- mean(motion_plays$timeToThrow, na.rm = TRUE)
no_motion_avg_time_to_throw <- mean(no_motion_plays$timeToThrow, na.rm = TRUE)
motion_completion_rate <- mean(motion_plays$passResult == "C", na.rm = TRUE) # 'C' for complete pass
no_motion_completion_rate <- mean(no_motion_plays$passResult == "C", na.rm = TRUE)
motion_avg_yards_gained <- mean(motion_plays$yardsGained, na.rm = TRUE)
no_motion_avg_yards_gained <- mean(no_motion_plays$yardsGained, na.rm = TRUE)
# t-tests) to check if differences are significant
# timeToThrow difference
ttest_time_to_throw <- t.test(motion_plays$timeToThrow, no_motion_plays$timeToThrow, na.rm = TRUE)
cat("T-test for timeToThrow: p-value =", ttest_time_to_throw$p.value, "\n")## T-test for timeToThrow: p-value = 0.5300795#statisticallly insignificant
# yardsGained difference
ttest_yards_gained <- t.test(motion_plays$yardsGained, no_motion_plays$yardsGained, na.rm = TRUE)
cat("T-test for yardsGained: p-value =", ttest_yards_gained$p.value, "\n")## T-test for yardsGained: p-value = 0.426491#statisticallly insignificant
# Completion percentage for motion plays
motion_completion_rate <- motion_plays %>%
summarize(completion_rate = mean(passResult == "C", na.rm = TRUE))
# Completion percentage for no-motion plays
no_motion_completion_rate <- no_motion_plays %>%
summarize(completion_rate = mean(passResult == "C", na.rm = TRUE))
# Count completions and total plays for each group
motion_completion_count <- sum(motion_plays$passResult == "C", na.rm = TRUE)
motion_total <- nrow(motion_plays)
no_motion_completion_count <- sum(no_motion_plays$passResult == "C", na.rm = TRUE)
no_motion_total <- nrow(no_motion_plays)
# Perform a proportion test
prop_test <- prop.test(
x = c(motion_completion_count, no_motion_completion_count),
n = c(motion_total, no_motion_total)
)
# Output p-value
cat("Proportion Test p-value:", prop_test$p.value, "\n")## Proportion Test p-value: 0.001181421#the data provides strong evidence that pre-snap motion impacts whether a pass is completed or not.
motion_completion_rate <- motion_completion_count / motion_total
no_motion_completion_rate <- no_motion_completion_count / no_motion_total
# Create a data frame with the completion rates
completion_data <- data.frame(
Motion = rep(c("With Motion", "Without Motion"), times = c(motion_total, no_motion_total)),
CompletionRate = c(
motion_plays$passResult == "C",
no_motion_plays$passResult == "C"
)
)#visualize_single_play(df %>% filter(playId == 1642, gameId == 2022091107),
# highlight_players_in_motion = FALSE, show_targetXY = FALSE)
#possible scatterplot to visualzie time to throw
ggplot(critical_plays, aes(x = as.factor(inMotionAtBallSnap), y = timeToThrow, color = as.factor(inMotionAtBallSnap))) +
geom_jitter(width = 0.2, height = 0, size = 2, alpha = 0.7) + # Use jitter to avoid overplotting
labs(x = "Pre-Snap Motion", y = "Time to Throw (seconds)",
title = "Time to Throw with vs Without Pre-Snap Motion") +
scale_x_discrete(labels = c("Without Motion", "With Motion")) +
theme_minimal() # Legend will be automatically included
#interpretation: more plays and higher distribution of plays with no motion
# plays with motion have less observations, less distribution, and consolidation around the median
#interpretation A lot of outliers, plays without motion have longer times on average and more distribution/spread
# plays with motion have slightly less distribution and less outliers, more values around the mean
#violin plot to visualize density
ggplot(completion_data, aes(x = Motion, y = CompletionRate, fill = Motion)) +
geom_violin(trim = FALSE) + # trim = FALSE to show full distribution
labs(x = "Pre-Snap Motion", y = "left = incomplete, right = complete)", title = "Violin Plot of Completion Rate by Pre-Snap Motion") +
scale_y_discrete(labels = c("Incomplete", "Complete")) + # Use scale_y_discrete
scale_fill_manual(values = c("lightblue", "lightgreen")) +
theme_minimal()
dev.off()## null device
## 1Conclusion
To summarize, the hypothesis that plays with pre snap motion affect quarterback performance is slightly supported by this analysis, with the completion rate and its proportions support the hypothesis. On the contrary, the time to throw and yards gained were not in consensus with this notion. Plays with pre snap motion did not influence either of these two variables, rejecting the claim that Quarterback Performance is affected by plays with pre snap motion Yards gained, start time, red zone plays, and absolute yard line are significant factors that influence whether a touchdown is successful. Creating more space on the field can enhance a player’s ability to score. I believe my analysis offers valuable insights that can help NFL teams improve their on-field performance. By implementing strategies such as focusing on player speed or optimizing space before the snap, teams can increase their chances of success. getorkornoo@loyola.edu and href=“mailto:uiokoro@loyola.edu”>uiokoro@loyola.edu
```