Playing Moneyball in the NBA
getwd()[1] "/cloud/project"In Class Activity 12
# VIDEO 1
# Read in the data
NBA = read.csv("NBA_train.csv")
str(NBA)'data.frame': 835 obs. of 20 variables:
$ SeasonEnd: int 1980 1980 1980 1980 1980 1980 1980 1980 1980 1980 ...
$ Team : chr "Atlanta Hawks" "Boston Celtics" "Chicago Bulls" "Cleveland Cavaliers" ...
$ Playoffs : int 1 1 0 0 0 0 0 1 0 1 ...
$ W : int 50 61 30 37 30 16 24 41 37 47 ...
$ PTS : int 8573 9303 8813 9360 8878 8933 8493 9084 9119 8860 ...
$ oppPTS : int 8334 8664 9035 9332 9240 9609 8853 9070 9176 8603 ...
$ FG : int 3261 3617 3362 3811 3462 3643 3527 3599 3639 3582 ...
$ FGA : int 7027 7387 6943 8041 7470 7596 7318 7496 7689 7489 ...
$ X2P : int 3248 3455 3292 3775 3379 3586 3500 3495 3551 3557 ...
$ X2PA : int 6952 6965 6668 7854 7215 7377 7197 7117 7375 7375 ...
$ X3P : int 13 162 70 36 83 57 27 104 88 25 ...
$ X3PA : int 75 422 275 187 255 219 121 379 314 114 ...
$ FT : int 2038 1907 2019 1702 1871 1590 1412 1782 1753 1671 ...
$ FTA : int 2645 2449 2592 2205 2539 2149 1914 2326 2333 2250 ...
$ ORB : int 1369 1227 1115 1307 1311 1226 1155 1394 1398 1187 ...
$ DRB : int 2406 2457 2465 2381 2524 2415 2437 2217 2326 2429 ...
$ AST : int 1913 2198 2152 2108 2079 1950 2028 2149 2148 2123 ...
$ STL : int 782 809 704 764 746 783 779 782 900 863 ...
$ BLK : int 539 308 392 342 404 562 339 373 530 356 ...
$ TOV : int 1495 1539 1684 1370 1533 1742 1492 1565 1517 1439 ...View(NBA)# VIDEO 2
# How many wins to make the playoffs?
# Teams should win atleast 38 games to make it to the playoffs
table(NBA$W, NBA$Playoffs)
0 1
11 2 0
12 2 0
13 2 0
14 2 0
15 10 0
16 2 0
17 11 0
18 5 0
19 10 0
20 10 0
21 12 0
22 11 0
23 11 0
24 18 0
25 11 0
26 17 0
27 10 0
28 18 0
29 12 0
30 19 1
31 15 1
32 12 0
33 17 0
34 16 0
35 13 3
36 17 4
37 15 4
38 8 7
39 10 10
40 9 13
41 11 26
42 8 29
43 2 18
44 2 27
45 3 22
46 1 15
47 0 28
48 1 14
49 0 17
50 0 32
51 0 12
52 0 20
53 0 17
54 0 18
55 0 24
56 0 16
57 0 23
58 0 13
59 0 14
60 0 8
61 0 10
62 0 13
63 0 7
64 0 3
65 0 3
66 0 2
67 0 4
69 0 1
72 0 1# Compute Points Difference
NBA$PTSdiff = NBA$PTS - NBA$oppPTS
print(NBA$PTSdiff) [1] 239 639 -222 28 -362 -676 -360 14 -57 257 484 323 -96 -94 346 295 -67 -31
[19] -340 382 -493 -209 -254 482 162 -398 -689 -39 -518 -97 27 115 1 315 596 -494
[37] 133 644 452 73 236 -130 -135 -483 1 44 523 -166 -698 -363 43 -75 85 -3
[55] -153 -254 399 449 56 -219 470 283 49 189 -606 339 -464 72 -78 439 -401 -610
[73] -34 51 -33 -297 -951 -480 119 455 361 227 201 629 415 175 300 -396 263 -344
[91] -11 -106 538 -425 -349 35 -90 294 -279 -253 -395 -121 308 341 90 315 180 73
[109] 291 -22 -267 -14 93 -237 -119 545 -69 -226 178 200 204 -602 141 -509 -219 -368
[127] 603 562 19 -380 336 -173 279 75 -446 -9 -22 194 772 -312 -235 90 107 96
[145] -283 214 -273 -568 635 741 -163 -460 193 -245 87 -252 -152 -8 -30 -148 593 545
[163] 73 -313 517 -71 282 -192 82 -53 -937 763 321 -414 -514 -16 -200 257 -264 -418
[181] 38 33 -112 295 487 279 62 358 334 424 -682 114 -65 -846 479 44 -665 -40
[199] -118 -367 371 -472 -400 169 302 -63 403 95 -699 118 623 -99 139 476 -25 78
[217] -342 -813 588 -921 296 -521 309 123 631 120 -455 -597 238 412 -177 84 327 -641
[235] 267 -25 6 116 499 -257 120 13 -278 556 -797 -64 -347 -645 116 -731 409 582
[253] 518 -415 286 85 393 -177 63 477 -430 746 -202 -375 -895 268 134 287 -32 -283
[271] 553 -491 203 -322 -370 -19 -326 -15 537 712 -556 370 101 273 -408 -123 297 -320
[289] 856 447 -627 -645 167 320 -141 155 88 -90 -345 -140 -578 -139 319 -567 -104 487
[307] 596 -497 272 154 524 -366 -71 73 -20 516 529 -1246 -143 -114 -81 347 139 29
[325] -104 -94 -306 -638 103 505 108 -473 546 255 -260 219 580 178 -577 432 -351 -18
[343] 253 330 -713 122 -638 143 354 283 -469 -352 219 -531 -568 180 573 319 -625 397
[361] 216 -473 431 745 346 -605 105 -154 269 396 51 -238 69 -598 -444 174 303 -751
[379] -18 -134 -358 -738 -257 255 579 -416 318 313 -82 489 671 655 -459 100 -279 -47
[397] 1004 212 -402 -222 205 -119 143 266 -295 365 117 -435 -439 -347 190 456 -820 27
[415] 193 -222 517 639 -616 540 -818 87 446 -601 153 886 151 -521 -515 430 -386 367
[433] 80 -193 350 450 -155 -121 -374 256 -29 -536 54 342 -277 -646 630 -256 721 -839
[451] 133 288 -215 164 583 224 -501 -966 129 -748 -62 499 -604 635 404 -157 58 129
[469] 202 -88 -196 425 115 -458 327 588 -760 536 -599 48 -441 -62 219 -771 -287 -47
[487] -174 118 -678 -71 377 -945 701 255 18 207 -85 120 56 110 428 525 239 487
[505] 78 -13 366 -461 -269 -427 -175 172 -746 -348 351 -202 -139 -742 188 -16 -237 277
[523] 188 318 111 -414 216 81 351 181 344 477 636 2 185 385 -470 -547 -347 181
[541] 79 -700 -273 349 -477 175 -443 -401 22 -38 584 -610 -126 -18 277 341 -327 129
[559] 131 -56 245 624 509 248 -36 73 -115 -292 -32 -421 -789 638 -679 302 -93 121
[577] 286 -338 191 -265 -414 19 170 428 173 -111 11 189 93 214 533 444 -10 -481
[595] 196 -83 -381 -119 -521 -215 364 88 479 -60 142 472 -376 320 200 43 87 450
[613] 205 -8 -121 -576 -204 -307 -104 411 592 -52 -247 -100 -462 -796 71 -491 87 65
[631] 471 166 317 -177 331 62 -63 -243 188 535 -245 119 -123 -580 -200 -184 -61 584
[649] -328 177 640 188 -133 -350 -27 -390 -126 -328 52 183 498 19 547 -111 -130 155
[667] 129 205 303 317 -85 -154 112 -231 -525 -88 -160 455 -775 126 559 -248 -245 -216
[685] 152 -390 -280 -307 411 314 592 133 341 -28 398 -200 -38 -6 -422 -75 -359 -301
[703] -63 -129 -234 64 -248 599 -352 -148 691 -237 81 235 -42 -149 841 -359 -253 -29
[721] 372 304 606 181 384 -115 -596 595 -509 -709 -565 -556 -415 433 -542 448 34 414
[739] -80 -185 393 -718 238 564 -27 129 616 -104 -23 732 162 280 -40 -307 328 -91
[757] -719 628 -448 21 -89 -403 -200 127 -214 -500 549 6 158 438 -718 308 -231 217
[775] -612 382 300 120 -134 535 223 335 -419 -295 -30 -247 -521 387 -124 187 139 -787
[793] -748 -202 -313 286 614 -320 402 271 -358 417 -146 438 -392 -67 440 -328 600 -739
[811] 347 390 -295 -191 179 -88 -257 501 192 612 -69 -544 -512 73 64 311 448 123
[829] -73 125 -438 468 -515 -150 -607# Check for linear relationship
plot(NBA$PTSdiff, NBA$W)
# Linear regression model for wins
WinsReg = lm(W ~ PTSdiff, data=NBA)
summary(WinsReg)
Call:
lm(formula = W ~ PTSdiff, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-9.7393 -2.1018 -0.0672 2.0265 10.6026
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.100e+01 1.059e-01 387.0 <2e-16 ***
PTSdiff 3.259e-02 2.793e-04 116.7 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.061 on 833 degrees of freedom
Multiple R-squared: 0.9423, Adjusted R-squared: 0.9423
F-statistic: 1.361e+04 on 1 and 833 DF, p-value: < 2.2e-16# VIDEO 3
# Linear regression model for points scored
PointsReg = lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + TOV + STL + BLK, data=NBA)
summary(PointsReg)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + TOV +
STL + BLK, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-527.40 -119.83 7.83 120.67 564.71
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.051e+03 2.035e+02 -10.078 <2e-16 ***
X2PA 1.043e+00 2.957e-02 35.274 <2e-16 ***
X3PA 1.259e+00 3.843e-02 32.747 <2e-16 ***
FTA 1.128e+00 3.373e-02 33.440 <2e-16 ***
AST 8.858e-01 4.396e-02 20.150 <2e-16 ***
ORB -9.554e-01 7.792e-02 -12.261 <2e-16 ***
DRB 3.883e-02 6.157e-02 0.631 0.5285
TOV -2.475e-02 6.118e-02 -0.405 0.6859
STL -1.992e-01 9.181e-02 -2.169 0.0303 *
BLK -5.576e-02 8.782e-02 -0.635 0.5256
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.5 on 825 degrees of freedom
Multiple R-squared: 0.8992, Adjusted R-squared: 0.8981
F-statistic: 817.3 on 9 and 825 DF, p-value: < 2.2e-16# Sum of Squared Errors
PointsReg$residuals 1 2 3 4 5 6 7 8 9
38.5722713 142.8720040 -92.8957180 -8.3913473 -258.4705615 171.4608325 150.4081623 169.3811429 40.7756197
10 11 12 13 14 15 16 17 18
-75.3256614 444.9088743 94.3864704 -205.6809050 113.5969040 64.1993998 -76.5711999 249.4888007 28.0363236
19 20 21 22 23 24 25 26 27
329.4487991 96.3248342 349.2067913 -284.3765225 196.1611379 198.2493104 445.4100295 93.8946072 -316.2962802
28 29 30 31 32 33 34 35 36
-166.1909668 -5.8446359 211.2301997 155.7426615 -23.9248929 -77.9070033 218.9449693 164.1368602 -177.6479438
37 38 39 40 41 42 43 44 45
66.9205988 162.7892553 23.5961895 93.9839603 185.7015113 -50.2507837 -90.1181969 139.6866673 -231.1772776
46 47 48 49 50 51 52 53 54
111.2200135 185.9069491 210.6753018 -47.9420913 -257.8213675 225.7399197 70.4925628 432.6468031 187.4169561
55 56 57 58 59 60 61 62 63
-34.3947653 112.9305359 334.4717296 222.4169937 17.6755711 165.4512882 207.9970351 56.8277093 214.6051983
64 65 66 67 68 69 70 71 72
-23.0235142 341.7509536 -48.3807695 304.9203623 -36.7878762 -31.0357805 61.8847883 -153.0322403 121.7423324
73 74 75 76 77 78 79 80 81
-61.1581185 -47.9906548 -120.3599484 245.7621368 -264.3876116 161.1110819 87.3192423 426.2098591 -4.7790973
82 83 84 85 86 87 88 89 90
126.8613801 -97.5009340 329.9773912 -16.2338716 7.8513505 191.9280982 87.0090318 -142.5397602 -216.2264974
91 92 93 94 95 96 97 98 99
-199.6293933 71.0810742 257.3751407 -227.1203824 -61.4866232 71.3329444 -233.2637272 -34.7860771 84.9503466
100 101 102 103 104 105 106 107 108
108.6553543 -84.8168235 -90.0423121 341.2144522 52.8507112 47.8978397 181.0574099 160.7203318 237.0174702
109 110 111 112 113 114 115 116 117
314.9609845 51.9650831 300.2035074 -148.0931149 -13.3592416 -161.6184704 82.1172789 277.6080699 233.4334153
118 119 120 121 122 123 124 125 126
-225.7299932 69.0259972 37.3407430 18.2709681 121.8125335 217.9464858 -74.8210467 36.2611001 356.2366230
127 128 129 130 131 132 133 134 135
439.4127892 111.0266627 72.1377278 -6.1141295 331.6249450 -158.3642350 94.9048994 151.3242943 -284.7768411
136 137 138 139 140 141 142 143 144
-184.0287416 -103.9972773 54.1758237 139.3176593 125.3796164 -71.4407602 83.4742245 -131.6383234 -33.5752771
145 146 147 148 149 150 151 152 153
98.9460909 -59.8760139 -116.6711077 -110.4055752 290.8888709 38.5758792 -6.8265554 -284.8106013 149.5419209
154 155 156 157 158 159 160 161 162
-185.9270381 -13.5712897 -90.2301662 21.0080300 14.5295957 -346.4091267 -54.7198161 87.6823846 203.7903006
163 164 165 166 167 168 169 170 171
-30.7131853 -153.9699795 194.6791232 -357.4466727 133.8696823 -21.6271760 -220.4987354 -153.7269937 -383.7168614
172 173 174 175 176 177 178 179 180
212.2104185 -100.3118791 -30.5085767 -57.7910608 205.9463003 -124.1358862 -61.2169391 -93.9538879 -135.6180284
181 182 183 184 185 186 187 188 189
69.1245169 -435.5355494 -47.8153585 115.1051439 222.5411686 104.6516380 7.8335700 178.0759383 -185.3383423
190 191 192 193 194 195 196 197 198
122.0537263 -29.4729351 27.1344203 189.2078833 -429.5919872 57.2397301 -170.2701567 -14.0836520 21.0147294
199 200 201 202 203 204 205 206 207
49.6548689 -127.4633821 -87.4084020 -77.6940715 -155.2913076 8.4930328 -232.7210528 35.3384277 151.1394532
208 209 210 211 212 213 214 215 216
119.4563308 -416.3088878 134.8599211 33.3825347 48.4541197 -269.8021487 214.9045443 88.1318416 -24.0318730
217 218 219 220 221 222 223 224 225
188.2281015 -249.1537666 157.9872056 -146.6803006 72.9077663 31.1747176 337.2185582 69.7227713 -2.7440511
226 227 228 229 230 231 232 233 234
-55.2845827 -84.6255409 -151.4858821 234.7432200 -165.3909069 -172.9288404 386.6402387 34.4884530 -368.0387956
235 236 237 238 239 240 241 242 243
304.8349400 -173.0591889 168.9365987 -327.6509605 95.0370278 -75.5698743 -74.9702316 290.0371682 -21.8628806
244 245 246 247 248 249 250 251 252
72.5362398 -144.3565453 -44.7765529 -155.4752429 -114.0232742 82.8841506 -306.5759686 256.9630856 75.4312937
253 254 255 256 257 258 259 260 261
-108.9852622 -160.6985087 -1.0708625 389.4834173 48.4039145 -173.2376267 102.4859575 564.7127452 -135.6781765
262 263 264 265 266 267 268 269 270
435.5847710 -238.8763852 93.4120332 -346.4790813 84.2266238 124.2627684 157.9013909 90.9742388 -319.7738668
271 272 273 274 275 276 277 278 279
111.6330940 -136.0189613 179.6895020 -139.8481361 -60.2214721 21.1448936 -102.4930752 87.4261255 -2.2833983
280 281 282 283 284 285 286 287 288
-33.1839059 -313.4181662 -9.7903234 365.0041757 -170.9089658 -203.2682115 -59.0783300 344.4592952 -177.2934555
289 290 291 292 293 294 295 296 297
278.4424923 31.1539516 -19.4217087 146.9309508 49.6437593 323.4485389 47.1034178 3.9718411 -111.0589062
298 299 300 301 302 303 304 305 306
-40.0036081 187.1994351 134.5701059 -130.3795390 227.3624370 16.4481298 -91.2556101 215.9887998 70.7747666
307 308 309 310 311 312 313 314 315
50.5357552 -86.7616664 66.3006293 348.5847817 69.7928527 -144.9174008 48.2485248 262.5189212 -11.0182067
316 317 318 319 320 321 322 323 324
276.2567984 40.2609782 -235.0009787 91.8230888 -36.7029055 66.1862316 127.1446887 34.6306466 -89.1508242
325 326 327 328 329 330 331 332 333
-38.0350890 74.6959695 -24.6713632 -139.6322463 120.5781319 -256.3194253 35.3325803 -238.1863124 204.2701943
334 335 336 337 338 339 340 341 342
-231.4333870 -242.0178081 27.3589769 442.7697537 -90.3428846 -252.6536092 31.2460678 -24.0030042 -113.6697991
343 344 345 346 347 348 349 350 351
74.2030422 -63.3601223 13.1314540 -58.4065092 16.5093336 -26.4233092 -49.9197611 102.5295504 -276.0762358
352 353 354 355 356 357 358 359 360
-171.2605451 235.4118705 -295.3696087 -259.1915277 -209.8493128 -60.3803252 40.8738668 -162.3559100 -3.1584146
361 362 363 364 365 366 367 368 369
-252.6683460 -359.6072976 219.8480950 107.9177034 -228.4285961 77.5838841 77.6092501 176.9728823 21.0277939
370 371 372 373 374 375 376 377 378
225.7947949 90.6177409 -95.0387148 243.8004275 63.7765295 -135.7112041 127.9942080 208.5134149 -226.2507886
379 380 381 382 383 384 385 386 387
-27.4427262 215.5791874 70.0554598 -220.3324085 -252.5213694 -117.0224660 36.9146043 188.5932206 -12.6241171
388 389 390 391 392 393 394 395 396
24.1401960 39.4113815 130.8261623 194.8028770 140.1603242 100.4917058 367.8120506 -77.1138759 190.1907177
397 398 399 400 401 402 403 404 405
430.4505906 243.1092461 -220.7690501 -135.3500281 182.9169784 58.1314347 -10.3705665 134.0505987 333.4363828
406 407 408 409 410 411 412 413 414
110.9704334 37.1431301 188.8559358 -88.4445131 -165.3268990 148.8624801 -4.7914163 -114.6045335 -90.1562962
415 416 417 418 419 420 421 422 423
-65.1353805 9.9207366 -20.2393315 147.7163583 153.4474395 95.5889698 -329.6439893 323.3019593 345.3838501
424 425 426 427 428 429 430 431 432
-148.5288812 166.9648145 277.3541861 162.6383840 -78.9033000 -176.7932426 365.3962572 132.7242544 85.6582953
433 434 435 436 437 438 439 440 441
-19.3417988 95.4767236 -102.8199452 111.8183778 299.2808339 -124.0889739 -37.3805041 118.5055640 38.2173450
442 443 444 445 446 447 448 449 450
-122.8141423 -84.3447659 154.5643586 42.6355711 54.7178397 102.9846564 32.6861086 112.7943954 -163.3563028
451 452 453 454 455 456 457 458 459
150.7521084 217.5877806 -96.7133626 13.7243484 -33.1690450 -112.2550008 -15.7083565 -224.4198990 18.2593593
460 461 462 463 464 465 466 467 468
-393.0403979 49.2945267 52.0947949 43.2496203 -149.1223107 75.6856970 170.8878792 -257.6364448 51.6854016
469 470 471 472 473 474 475 476 477
11.8121415 -176.9048352 -149.5317630 -64.1990241 -71.3105611 -317.9190063 -65.8451642 97.8497015 -103.1692986
478 479 480 481 482 483 484 485 486
3.0848318 -104.6823532 -234.7534874 50.5295490 -75.4835788 -526.1468848 -393.9784124 -360.8366411 116.7193515
487 488 489 490 491 492 493 494 495
-321.3756304 -28.1090479 -508.3250405 -39.9958738 67.9854387 -97.4641720 -268.8364479 -26.0249946 188.1881640
496 497 498 499 500 501 502 503 504
-127.9366821 -86.3440758 133.8144538 29.4480488 -292.9821609 -124.9408024 101.3655240 -186.5181083 -63.5389375
505 506 507 508 509 510 511 512 513
-212.2015589 -323.1476886 -125.6610320 56.9083106 -39.0559074 -1.9339391 -319.9727619 -433.1243358 -431.1346590
514 515 516 517 518 519 520 521 522
-95.8909016 120.6089792 -409.7409083 -352.9341830 -527.3988939 110.6694955 -193.5043557 -92.6385367 -143.5858243
523 524 525 526 527 528 529 530 531
-189.7838251 172.1977457 -80.8020663 -342.9141699 124.8700974 -226.9524006 -73.5173798 -388.4868649 82.9536394
532 533 534 535 536 537 538 539 540
-96.7444961 -114.0835553 60.0566113 -332.3804023 -175.5276633 -338.7116370 -148.1422366 -45.2258816 -270.5159099
541 542 543 544 545 546 547 548 549
-159.8389177 -420.4637398 -133.0466450 183.8988039 -267.0297916 -5.2562902 -228.0471046 -11.6818058 -255.6786897
550 551 552 553 554 555 556 557 558
-7.7244412 -115.5357863 -298.4118693 -122.2961876 90.2924072 111.3930340 -245.4519945 -164.6445508 -29.3651223
559 560 561 562 563 564 565 566 567
-41.9781581 33.4260937 15.1663563 -29.4557965 44.0659204 247.9836928 -57.4318280 -238.6989443 -8.7249850
568 569 570 571 572 573 574 575 576
30.9454288 -343.6175905 -207.4418486 -306.4223254 157.4538406 -502.4785715 -126.1415717 48.8616098 143.9835801
577 578 579 580 581 582 583 584 585
-344.7694076 -116.5012114 -142.7898454 -127.9612584 -226.7659179 67.1679765 -94.0443422 -326.2414346 -84.6517620
586 587 588 589 590 591 592 593 594
4.5942017 -89.9757406 -97.0958454 -34.6927947 40.9701699 -88.3066869 126.5679875 -128.7529512 -166.6757304
595 596 597 598 599 600 601 602 603
-208.2444446 -105.4053449 -69.9961388 -104.0297252 -475.1678378 -290.6421238 195.4801727 -116.0865727 -136.0505114
604 605 606 607 608 609 610 611 612
-118.3811054 125.8235124 -145.2484421 -144.5655628 -435.6270621 -230.6201428 -112.7403208 -243.8883351 13.9124625
613 614 615 616 617 618 619 620 621
-392.1393056 -233.5727670 88.6125994 -203.7574893 -207.3393547 36.7326516 71.7237279 -110.6124268 -151.5524839
622 623 624 625 626 627 628 629 630
95.2365977 -227.3589026 -98.5962165 -210.8715081 -53.6787512 33.2644764 -380.2334407 -217.0512157 -135.7283167
631 632 633 634 635 636 637 638 639
208.5947156 -198.2473902 -147.6362401 -282.5390059 -55.4726214 3.0618526 -118.7764165 -15.9756605 1.5396468
640 641 642 643 644 645 646 647 648
2.2068206 -78.5559489 20.5194552 -376.9064555 -367.5790965 78.4730898 88.0528050 -178.9859105 283.6342652
649 650 651 652 653 654 655 656 657
18.0639226 1.4275017 -22.1910648 334.1581029 -44.6704981 -166.2133428 -112.8182784 175.7515262 60.9355144
658 659 660 661 662 663 664 665 666
-331.2815975 -175.1322112 34.9727118 430.8913232 -260.7815266 -99.5985786 -306.5331420 -144.2463445 -71.9561309
667 668 669 670 671 672 673 674 675
40.4095734 -9.9170555 9.7141807 72.8730721 -61.2840291 -51.9936086 -452.8596863 -81.9437393 69.2906290
676 677 678 679 680 681 682 683 684
254.7395766 -22.9459505 215.8931262 -16.9537293 -107.9068394 202.3017464 287.5765859 180.7757394 -305.5932029
685 686 687 688 689 690 691 692 693
56.2240459 4.5320328 -44.0648823 -278.0391307 -13.3280981 -112.7276708 422.1750569 -131.0023955 51.4971549
694 695 696 697 698 699 700 701 702
-86.9745423 28.8396258 -107.9302127 -55.3683153 -16.7225380 60.3453436 3.3520616 140.9429255 -17.9219329
703 704 705 706 707 708 709 710 711
-296.8381962 136.2394242 106.7244264 168.2861008 26.7860625 339.8954937 187.8922770 -202.6392008 148.7995083
712 713 714 715 716 717 718 719 720
268.8921528 0.6597544 -119.2916116 -23.0549542 -28.1758366 206.7679556 -138.5838793 -210.7824121 -29.6626073
721 722 723 724 725 726 727 728 729
210.3268820 -212.8798945 88.1962039 129.1032851 11.9530477 -166.3796048 -372.3297260 67.5130804 1.7122210
730 731 732 733 734 735 736 737 738
-179.0745146 -28.4404659 151.2765881 -425.3360446 344.3671825 -47.2592021 136.9801455 63.4427397 203.2044716
739 740 741 742 743 744 745 746 747
27.7908779 251.4279736 84.5817590 -155.6577645 150.3787715 138.7921016 198.4699948 101.8590582 345.8144412
748 749 750 751 752 753 754 755 756
35.1336113 169.1641149 354.9998851 251.7571721 47.8412497 77.9677328 66.2799291 216.7990909 155.1577399
757 758 759 760 761 762 763 764 765
-131.2437994 230.2449071 218.7156645 116.0349148 -78.5937100 -23.1321308 99.7713990 280.2227149 40.8527845
766 767 768 769 770 771 772 773 774
19.4188914 72.9388151 120.7266716 439.1035137 456.0100354 47.3239201 186.1096824 31.7505381 -54.0912550
775 776 777 778 779 780 781 782 783
73.0035369 234.4761589 27.9146721 -21.6493313 -75.0167664 148.4251726 106.3308316 76.0196340 37.3592068
784 785 786 787 788 789 790 791 792
56.5562663 -41.8917486 -200.7598142 -55.5159544 109.1518868 321.3239680 219.8866600 -73.6034103 3.1961900
793 794 795 796 797 798 799 800 801
-171.1408177 190.8979178 101.1845265 253.1734885 263.7840087 199.5924560 463.8379676 219.1540922 52.3032317
802 803 804 805 806 807 808 809 810
140.7498122 195.8267787 -55.3103142 153.8564182 61.1275837 92.8158603 -108.8302808 73.3423661 -360.6001538
811 812 813 814 815 816 817 818 819
134.1518035 73.3435884 141.0017271 272.8259956 -33.1611977 19.7818711 -149.9998706 190.0065593 261.3992751
820 821 822 823 824 825 826 827 828
308.7602526 -135.4172110 108.2677094 -171.3410196 102.4439076 156.0829202 210.0521687 109.4908936 -20.5354175
829 830 831 832 833 834 835
59.2845716 175.9235274 30.6531825 262.6728011 70.0671862 -17.5789419 -8.3393046 SSE = sum(PointsReg$residuals^2)
SSE[1] 28394314summary(NBA$PTS) Min. 1st Qu. Median Mean 3rd Qu. Max.
6901 7934 8312 8370 8784 10371 # Root mean squared error
RMSE = sqrt(SSE/nrow(NBA))
RMSE[1] 184.4049# Average number of points in a season
mean(NBA$PTS)[1] 8370.24# Remove insignificant variables
summary(PointsReg)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + TOV +
STL + BLK, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-527.40 -119.83 7.83 120.67 564.71
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.051e+03 2.035e+02 -10.078 <2e-16 ***
X2PA 1.043e+00 2.957e-02 35.274 <2e-16 ***
X3PA 1.259e+00 3.843e-02 32.747 <2e-16 ***
FTA 1.128e+00 3.373e-02 33.440 <2e-16 ***
AST 8.858e-01 4.396e-02 20.150 <2e-16 ***
ORB -9.554e-01 7.792e-02 -12.261 <2e-16 ***
DRB 3.883e-02 6.157e-02 0.631 0.5285
TOV -2.475e-02 6.118e-02 -0.405 0.6859
STL -1.992e-01 9.181e-02 -2.169 0.0303 *
BLK -5.576e-02 8.782e-02 -0.635 0.5256
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.5 on 825 degrees of freedom
Multiple R-squared: 0.8992, Adjusted R-squared: 0.8981
F-statistic: 817.3 on 9 and 825 DF, p-value: < 2.2e-16# TOV were dropped - not significant
PointsReg2 = lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + STL + BLK, data=NBA)
summary(PointsReg2)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + STL +
BLK, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-526.79 -121.09 6.37 120.74 565.94
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.077e+03 1.931e+02 -10.755 <2e-16 ***
X2PA 1.044e+00 2.951e-02 35.366 <2e-16 ***
X3PA 1.263e+00 3.703e-02 34.099 <2e-16 ***
FTA 1.125e+00 3.308e-02 34.023 <2e-16 ***
AST 8.861e-01 4.393e-02 20.173 <2e-16 ***
ORB -9.581e-01 7.758e-02 -12.350 <2e-16 ***
DRB 3.892e-02 6.154e-02 0.632 0.5273
STL -2.068e-01 8.984e-02 -2.301 0.0216 *
BLK -5.863e-02 8.749e-02 -0.670 0.5029
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.4 on 826 degrees of freedom
Multiple R-squared: 0.8991, Adjusted R-squared: 0.8982
F-statistic: 920.4 on 8 and 826 DF, p-value: < 2.2e-16# DRB were dropped - not significant
PointsReg3 = lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + STL + BLK, data=NBA)
summary(PointsReg3)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + STL + BLK,
data = NBA)
Residuals:
Min 1Q Median 3Q Max
-523.79 -121.64 6.07 120.81 573.64
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.015e+03 1.670e+02 -12.068 < 2e-16 ***
X2PA 1.048e+00 2.852e-02 36.753 < 2e-16 ***
X3PA 1.271e+00 3.475e-02 36.568 < 2e-16 ***
FTA 1.128e+00 3.270e-02 34.506 < 2e-16 ***
AST 8.909e-01 4.326e-02 20.597 < 2e-16 ***
ORB -9.702e-01 7.519e-02 -12.903 < 2e-16 ***
STL -2.276e-01 8.356e-02 -2.724 0.00659 **
BLK -3.882e-02 8.165e-02 -0.475 0.63462
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.4 on 827 degrees of freedom
Multiple R-squared: 0.8991, Adjusted R-squared: 0.8982
F-statistic: 1053 on 7 and 827 DF, p-value: < 2.2e-16# BLK were dropped - not significant
PointsReg4 = lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + STL, data=NBA)
summary(PointsReg4)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + STL, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-523.33 -122.02 6.93 120.68 568.26
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.033e+03 1.629e+02 -12.475 < 2e-16 ***
X2PA 1.050e+00 2.829e-02 37.117 < 2e-16 ***
X3PA 1.273e+00 3.441e-02 37.001 < 2e-16 ***
FTA 1.127e+00 3.260e-02 34.581 < 2e-16 ***
AST 8.884e-01 4.292e-02 20.701 < 2e-16 ***
ORB -9.743e-01 7.465e-02 -13.051 < 2e-16 ***
STL -2.268e-01 8.350e-02 -2.717 0.00673 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.3 on 828 degrees of freedom
Multiple R-squared: 0.8991, Adjusted R-squared: 0.8983
F-statistic: 1229 on 6 and 828 DF, p-value: < 2.2e-16# Compute SSE and RMSE for new model
SSE_4 = sum(PointsReg4$residuals^2)
RMSE_4 = sqrt(SSE_4/nrow(NBA))
SSE_4[1] 28421465RMSE_4[1] 184.493# VIDEO 4
# Read in test set
NBA_test = read.csv("NBA_test.csv")
str(NBA_test)'data.frame': 28 obs. of 20 variables:
$ SeasonEnd: int 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
$ Team : chr "Atlanta Hawks" "Brooklyn Nets" "Charlotte Bobcats" "Chicago Bulls" ...
$ Playoffs : int 1 1 0 1 0 0 1 0 1 1 ...
$ W : int 44 49 21 45 24 41 57 29 47 45 ...
$ PTS : int 8032 7944 7661 7641 7913 8293 8704 7778 8296 8688 ...
$ oppPTS : int 7999 7798 8418 7615 8297 8342 8287 8105 8223 8403 ...
$ FG : int 3084 2942 2823 2926 2993 3182 3339 2979 3130 3124 ...
$ FGA : int 6644 6544 6649 6698 6901 6892 6983 6638 6840 6782 ...
$ X2P : int 2378 2314 2354 2480 2446 2576 2818 2466 2472 2257 ...
$ X2PA : int 4743 4784 5250 5433 5320 5264 5465 5198 5208 4413 ...
$ X3P : int 706 628 469 446 547 606 521 513 658 867 ...
$ X3PA : int 1901 1760 1399 1265 1581 1628 1518 1440 1632 2369 ...
$ FT : int 1158 1432 1546 1343 1380 1323 1505 1307 1378 1573 ...
$ FTA : int 1619 1958 2060 1738 1826 1669 2148 1870 1744 2087 ...
$ ORB : int 758 1047 917 1026 1004 767 1092 991 885 909 ...
$ DRB : int 2593 2460 2389 2514 2359 2670 2601 2463 2801 2652 ...
$ AST : int 2007 1668 1587 1886 1694 1906 2002 1742 1845 1902 ...
$ STL : int 664 599 591 588 647 648 762 574 567 679 ...
$ BLK : int 369 391 479 417 334 454 533 400 346 359 ...
$ TOV : int 1219 1206 1153 1171 1149 1144 1253 1241 1236 1348 ...# Make predictions on test set
PointsPredictions = predict(PointsReg4, newdata=NBA_test)
PointsPredictions 1 2 3 4 5 6 7 8 9 10 11 12 13
8086.446 7764.143 7965.348 7784.034 8004.349 8247.427 8601.200 7818.223 8127.482 8619.523 8072.525 8535.753 7527.693
14 15 16 17 18 19 20 21 22 23 24 25 26
8022.760 8283.675 8159.595 7507.084 7851.878 8197.481 7766.547 7727.529 7942.718 7947.870 8144.708 8335.840 8006.388
27 28
7975.788 7873.656 # Compute out-of-sample R^2
SSE = sum((PointsPredictions - NBA_test$PTS)^2)
SST = sum((mean(NBA$PTS) - NBA_test$PTS)^2)
R2 = 1 - SSE/SST
R2[1] 0.8127142# Compute the RMSE
RMSE = sqrt(SSE/nrow(NBA_test))
RMSE [1] 196.3723In Class Activity 13
##Our data shows that a team with 49 wins has never missed the playoffs. What is the expected points difference for a team to make it to the postseason? Use the lecture solution file and more specifically the WingsReg model.
##
#WinsReg = lm(W ~ PTSdiff, data=NBA)
#49=41+.0326*(x)
x_1=(49-41)/0.0326
x_1[1] 245.3988##Answer:245.3988 is the expected points difference for a team to make it to the postseason**In Class Activity 14 the three-point attempts
Threepts_made<-c(4, 5, 3, 6, 7)
Threepts_attmpt<-c(9, 10, 8, 11, 12)
#Calculate the three-point shooting percentage for each game
Three_pts_pct=Threepts_made/Threepts_attmpt
Three_pts_pct[1] 0.4444444 0.5000000 0.3750000 0.5454545 0.5833333#select the correct average three-point shooting percentage for the five games.
mean(Three_pts_pct)[1] 0.4896465# correct average three-point shooting percentage for the five games is 48.96%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