Is reducing workload an effective way to increase value?
Tim Duncan averaged over 39 minutes per game in his career before the age of 26. Since then, his minutes have steadily decreased, reaching a career low of 31.3 MPG last year. Despite this decrease in minutes, his production per minute is quite comparable before and after age 26. Clearly, much of this reduction in minutes can be largely associated with age and the wear and tear often associated with aging.
Duncan also averaged over 37 MPG in the playoffs last year, so this reduction in minutes also includes some level of preservation. Soon the Spurs will need to weigh their level of wear and tear for not only Duncan, but Parker, Ginobili and even the Spurs young talent as they balance current contributions with long term conservation.
The issue of measuring career preservation is a tricky task to tackle. In the parallel issue of pitch counts and pitcher burnout in baseball, the debate has often been heated. Over 10 years ago, Rany Jazayerli created a system introducing Pitcher Abuse Points (PAP) in which he suggests weights of abuse on pitchers when they throw over 100 pitches in a game (he seems to focus on young pitchers). Many major league teams appear to make decisions roughly in line with these guidelines.
However, individuals such as Steve Treder have expressed doubts regarding the current pitch count system in place. Even Bill James chimes in, saying that Jazayerli and Woolner’s “research is so flawed that it is virtually useless.” Although it seems logical to assume that fatigued pitchers are more likely to encounter injury problems, there is not a consensus whether or not this tendency should be a significant factor considering taking starting pitchers out of a game.
The problem with analyzing the impact of fatigue on career longevity is that the best players tend to play more games, play longer in each game and have longer careers. This means that players who play more games and minutes (or pitches) also tend to have longer careers, so player ability must be considered at the same time as player “abuse”, which can be easier said than done.
The Method - Predicting Longevity
In order to try to account for this problem, I selected players with at least 2,000 minutes from the ages 28 and 29 and predicted Mean Expected Wins Added (MEWA) beyond age 29 (Mean Expected Championships Added was too variable to yield significant results) using regression analysis.
In this projection, I used an estimate of value per minute (basic statistical plus/minus or SPM), an estimate of perceived value and health (MPG) and a combination of the both (MEWA/gm). The formula I used to predict MEWA after 29 was 85*MEWA/GP + 0.72*MPG + 3.05*SPM +0.95*Max(0,SPM)^2; not below 0.
MEWA, MPG and SPM were cumulative for age 28 and 29. I summarized the data, excluded all players who were expected to have fewer than 10 MEWA after 29 and included only players who turned 30 after 1980 and “retired” before the 2010 season.
Whose Longevity Defied Expectation?
The following table lists MP during ages 28 and 29, predicted Mean Expected Wins Added after 29 (based on performances at 28 and 29), actual Mean Expected Wins Added after 29 and the respective ratio of [Actual Wins/[Expected Wins] entitled “Longevity Ratio”. Games played before 28 and MPG before 28 are also included.
NBA MEWA vs Expected after 29
| Player | MP 28,29 | Predicted MEWinA30+ | Actual MEWinA30+ | Longevity Ratio | GPpre28 | MPGpre28 |
|---|---|---|---|---|---|---|
| Charles Oakley | 5048 | 17 | 56 | 326% | 384 | 32.2 |
| Eddie Johnson | 4634 | 14 | 45 | 322% | 402 | 32.1 |
| Kevin Willis | 4646 | 17 | 56 | 321% | 320 | 27.5 |
| Brent Barry | 3907 | 27 | 85 | 310% | 155 | 21.0 |
| Sam Perkins | 5528 | 30 | 94 | 309% | 317 | 32.0 |
| Vinnie Johnson | 4002 | 15 | 42 | 290% | 208 | 25.2 |
| Dikembe Mutombo | 5953 | 41 | 112 | 272% | 153 | 37.5 |
| Mark Jackson | 5828 | 27 | 73 | 267% | 389 | 31.4 |
| Buck Williams | 5083 | 23 | 60 | 261% | 491 | 36.7 |
| Avery Johnson | 4362 | 21 | 53 | 255% | 137 | 11.3 |
| Dell Curry | 4114 | 22 | 53 | 245% | 337 | 18.8 |
| Antoine Carr | 2291 | 10 | 25 | 243% | 224 | 16.2 |
| Anthony Mason | 4399 | 23 | 55 | 243% | 187 | 25.7 |
| Karl Malone | 6356 | 95 | 223 | 236% | 407 | 36.3 |
| Tim Hardaway | 2321 | 38 | 88 | 232% | 308 | 38.4 |
| Reggie Miller | 5592 | 62 | 143 | 231% | 402 | 34.0 |
| Sam Cassell | 3004 | 38 | 84 | 222% | 209 | 22.4 |
| Bobby Jackson | 3398 | 22 | 48 | 222% | 191 | 21.0 |
| Olden Polynice | 3133 | 13 | 28 | 218% | 289 | 13.7 |
| Ricky Pierce | 3470 | 29 | 62 | 217% | 233 | 19.6 |
| Dennis Rodman | 4585 | 35 | 75 | 213% | 159 | 20.8 |
| Clifford Robinson | 5578 | 41 | 86 | 213% | 328 | 25.0 |
| Michael Cage | 4736 | 23 | 49 | 211% | 385 | 29.3 |
| Tom Chambers | 5698 | 26 | 55 | 207% | 389 | 33.1 |
| Doug Christie | 4707 | 27 | 56 | 207% | 204 | 25.8 |
| Bill Laimbeer | 5783 | 40 | 83 | 206% | 295 | 30.8 |
| Detlef Schrempf | 5237 | 39 | 80 | 204% | 342 | 22.5 |
| P.J. Brown | 4954 | 31 | 63 | 202% | 240 | 30.7 |
| Mitch Richmond | 4625 | 28 | 55 | 200% | 314 | 37.1 |
| Danny Ferry | 2255 | 14 | 27 | 199% | 225 | 18.0 |
| Johnny Newman | 4128 | 13 | 25 | 198% | 297 | 23.0 |
| Antonio Davis | 4427 | 18 | 36 | 197% | 125 | 22.1 |
| Robert Horry | 2936 | 33 | 64 | 195% | 317 | 31.8 |
| Steve Kerr | 2077 | 24 | 47 | 191% | 209 | 17.1 |
| Dale Davis | 4763 | 29 | 55 | 188% | 364 | 29.7 |
| Eric Piatkowski | 2982 | 17 | 31 | 186% | 211 | 13.0 |
| John Stockton | 6018 | 114 | 210 | 184% | 410 | 27.6 |
| George Lynch | 2802 | 15 | 27 | 181% | 244 | 19.6 |
| Armen Gilliam | 4513 | 19 | 34 | 180% | 170 | 30.3 |
| Patrick Ewing | 6269 | 69 | 120 | 174% | 275 | 34.3 |
| Vlade Divac | 5310 | 35 | 60 | 172% | 441 | 29.3 |
| Jeff Hornacek | 5811 | 57 | 96 | 169% | 307 | 27.9 |
| A.c. Green | 5066 | 29 | 49 | 169% | 407 | 28.6 |
| Scottie Pippen | 5882 | 72 | 120 | 166% | 398 | 33.6 |
| Anthony Peeler | 2493 | 17 | 28 | 164% | 253 | 22.7 |
| Sedale Threatt | 2701 | 24 | 39 | 162% | 251 | 18.1 |
| Frank Brickowski | 2310 | 16 | 25 | 160% | 118 | 12.1 |
| Tyrone Corbin | 6207 | 28 | 45 | 160% | 155 | 18.4 |
| Hersey Hawkins | 5383 | 36 | 58 | 159% | 403 | 36.1 |
| Dale Ellis | 5980 | 41 | 65 | 158% | 293 | 22.3 |
| Bob Sura | 3462 | 10 | 16 | 157% | 330 | 22.5 |
| James Donaldson | 2833 | 18 | 27 | 154% | 314 | 22.3 |
| Kurt Rambis | 3087 | 17 | 26 | 153% | 271 | 19.5 |
| Ruben Patterson | 3305 | 11 | 17 | 151% | 256 | 23.7 |
| Mark West | 2934 | 22 | 33 | 150% | 180 | 15.1 |
| Chuck Person | 5908 | 16 | 24 | 150% | 398 | 35.3 |
| Otis Thorpe | 5986 | 30 | 46 | 149% | 403 | 31.7 |
| Ron Harper | 6114 | 33 | 50 | 148% | 267 | 35.2 |
| David Wesley | 4693 | 31 | 45 | 144% | 267 | 26.3 |
| Antonio Daniels | 2384 | 23 | 33 | 143% | 350 | 22.9 |
| Dominique Wilkins | 5945 | 54 | 77 | 142% | 401 | 36.7 |
| Gary Payton | 6375 | 84 | 118 | 141% | 409 | 32.4 |
| Hakeem Olajuwon | 4698 | 93 | 129 | 139% | 468 | 36.6 |
| Donyell Marshall | 4078 | 41 | 56 | 137% | 348 | 25.6 |
| Aaron Williams | 2003 | 15 | 21 | 136% | 87 | 9.8 |
| Mark Eaton | 5364 | 24 | 33 | 135% | 163 | 22.5 |
| Dan Majerle | 6406 | 46 | 63 | 135% | 286 | 30.5 |
| Hotrod Williams | 4069 | 35 | 47 | 134% | 239 | 29.1 |
| Joe Dumars | 6238 | 33 | 44 | 133% | 387 | 31.3 |
| Kevin Mchale | 5050 | 57 | 76 | 133% | 328 | 27.1 |
| Craig Ehlo | 4761 | 27 | 36 | 133% | 211 | 14.5 |
| Nick Van Exel | 4752 | 21 | 28 | 132% | 378 | 34.8 |
| Cuttino Mobley | 6159 | 29 | 38 | 131% | 209 | 33.3 |
| Danny Ainge | 5517 | 44 | 56 | 129% | 359 | 24.3 |
| Gerald Henderson | 4736 | 20 | 25 | 127% | 322 | 18.8 |
| Horace Grant | 5315 | 52 | 66 | 126% | 399 | 32.3 |
| Clarence Weatherspoon | 2075 | 26 | 33 | 126% | 400 | 37.1 |
| Byron Scott | 5198 | 35 | 43 | 125% | 394 | 30.2 |
| John Starks | 4534 | 40 | 50 | 124% | 179 | 20.2 |
| John Paxson | 3626 | 15 | 18 | 124% | 284 | 21.0 |
| Trent Tucker | 2939 | 20 | 24 | 122% | 295 | 22.6 |
| Jerome Kersey | 5202 | 37 | 45 | 120% | 393 | 25.1 |
| Clyde Drexler | 5535 | 79 | 94 | 119% | 478 | 33.0 |
| Derrick Coleman | 2397 | 30 | 36 | 118% | 292 | 35.4 |
| Stacey Augmon | 2586 | 18 | 21 | 118% | 313 | 30.6 |
| Rod Strickland | 5156 | 50 | 58 | 117% | 325 | 27.6 |
| Eddie Jones | 3531 | 76 | 88 | 115% | 294 | 34.3 |
| Danny Schayes | 3722 | 23 | 26 | 115% | 332 | 17.7 |
| Larry Nance | 3475 | 65 | 75 | 115% | 378 | 30.9 |
| Brian Shaw | 3873 | 11 | 12 | 114% | 246 | 28.9 |
| Greg Buckner | 2225 | 12 | 14 | 114% | 204 | 20.3 |
| Elden Campbell | 5215 | 37 | 42 | 114% | 361 | 22.5 |
| David Robinson | 6452 | 135 | 152 | 113% | 232 | 37.3 |
| Lorenzen Wright | 3656 | 13 | 14 | 113% | 383 | 25.0 |
| Jimmy Jackson | 2433 | 13 | 14 | 112% | 274 | 36.4 |
| Greg Anthony | 3039 | 30 | 33 | 112% | 232 | 22.4 |
| Charlie Ward | 3873 | 33 | 37 | 112% | 151 | 17.2 |
| Glen Rice | 6156 | 46 | 51 | 111% | 396 | 35.5 |
| Charles Barkley | 5379 | 97 | 106 | 110% | 468 | 37.1 |
| Derek Harper | 5975 | 51 | 55 | 109% | 402 | 29.0 |
| Aaron McKie | 4346 | 23 | 25 | 108% | 241 | 22.0 |
| Danny Manning | 2435 | 27 | 29 | 108% | 331 | 33.5 |
| Chris Whitney | 2068 | 16 | 17 | 107% | 250 | 12.2 |
| Matt Harpring | 3718 | 21 | 22 | 107% | 269 | 29.3 |
| Wesley Person | 3398 | 20 | 21 | 106% | 322 | 30.8 |
| Larry Bird | 6189 | 105 | 112 | 106% | 320 | 37.8 |
| Rod Higgins | 4075 | 23 | 25 | 106% | 303 | 20.6 |
| Steve Smith | 5675 | 42 | 45 | 106% | 269 | 33.9 |
| Greg Ostertag | 2601 | 13 | 13 | 102% | 326 | 20.6 |
| Bryon Russell | 3989 | 38 | 38 | 101% | 270 | 18.8 |
| Rodney Rogers | 3254 | 26 | 26 | 100% | 383 | 27.4 |
| Allan Houston | 4984 | 24 | 24 | 99% | 400 | 30.3 |
| Damon Jones | 4592 | 32 | 32 | 98% | 211 | 16.8 |
| Bo Outlaw | 3187 | 29 | 28 | 96% | 362 | 23.9 |
| Terry Porter | 5449 | 76 | 72 | 95% | 402 | 31.8 |
| Gerald Wilkins | 4508 | 14 | 13 | 95% | 405 | 30.9 |
| Dudley Bradley | 2074 | 11 | 11 | 95% | 286 | 19.3 |
| Chris Mullin | 6661 | 56 | 53 | 95% | 357 | 32.8 |
| Fred Hoiberg | 3092 | 28 | 27 | 95% | 139 | 11.6 |
| Terry Mills | 4170 | 20 | 18 | 94% | 260 | 26.7 |
| Nate Mcmillan | 3629 | 37 | 35 | 93% | 388 | 27.2 |
| Rik Smits | 4494 | 32 | 30 | 93% | 395 | 25.3 |
| Terry Cummings | 5645 | 40 | 37 | 93% | 470 | 34.5 |
| Alton Lister | 4100 | 21 | 19 | 92% | 323 | 22.0 |
| Jeff Malone | 4985 | 19 | 17 | 91% | 397 | 32.7 |
| Muggsy Bogues | 5579 | 33 | 30 | 90% | 402 | 27.9 |
| Orlando Woolridge | 3260 | 23 | 21 | 89% | 354 | 29.4 |
| Vinny Delnegro | 4309 | 28 | 24 | 88% | 229 | 21.6 |
| Rick Fox | 5359 | 27 | 23 | 88% | 368 | 22.7 |
| Mike Gminski | 3466 | 30 | 26 | 88% | 444 | 22.9 |
| Christian Laettner | 5422 | 38 | 32 | 85% | 262 | 34.3 |
| Adonal Foyle | 2359 | 16 | 13 | 84% | 312 | 18.8 |
| Mookie Blaylock | 5962 | 71 | 59 | 83% | 355 | 34.2 |
| Grant Long | 2263 | 31 | 26 | 83% | 401 | 31.4 |
| Howard Eisley | 4522 | 17 | 14 | 82% | 294 | 16.5 |
| Damon Stoudamire | 5453 | 31 | 25 | 81% | 301 | 36.6 |
| Pooh Richardson | 3885 | 14 | 11 | 80% | 320 | 34.5 |
| Larry Smith | 4815 | 18 | 15 | 80% | 360 | 30.0 |
| Alonzo Mourning | 3692 | 60 | 48 | 80% | 351 | 35.9 |
| Thurl Bailey | 5360 | 20 | 16 | 80% | 406 | 29.1 |
| Chris Gatling | 2035 | 30 | 24 | 80% | 206 | 15.3 |
| George Mccloud | 3648 | 29 | 23 | 80% | 247 | 15.7 |
| Sherman Douglas | 4837 | 18 | 14 | 79% | 238 | 29.7 |
| Spud Webb | 4921 | 25 | 20 | 77% | 357 | 18.2 |
| Corliss Williamson | 2500 | 18 | 14 | 75% | 337 | 25.2 |
| Sam Bowie | 2619 | 19 | 14 | 75% | 119 | 29.5 |
| Reggie Williams | 5345 | 29 | 21 | 73% | 130 | 19.9 |
| Tyrone Hill | 3511 | 28 | 20 | 72% | 357 | 25.2 |
| Magic Johnson | 5541 | 100 | 72 | 72% | 487 | 36.9 |
| Dee Brown | 2113 | 18 | 13 | 72% | 349 | 30.8 |
| Jason Williams | 3290 | 16 | 11 | 72% | 259 | 12.2 |
| Doc Rivers | 3988 | 56 | 40 | 71% | 365 | 29.4 |
| Kelvin Cato | 3167 | 20 | 14 | 70% | 217 | 17.3 |
| Randy Breuer | 2771 | 16 | 11 | 69% | 293 | 16.4 |
| Derrick Mckey | 5423 | 27 | 19 | 69% | 446 | 31.3 |
| Rolando Blackman | 5338 | 29 | 20 | 69% | 401 | 32.4 |
| Kendall Gill | 3617 | 38 | 26 | 68% | 382 | 31.0 |
| Eric Snow | 3967 | 28 | 19 | 68% | 257 | 22.6 |
| Herb Williams | 5296 | 25 | 17 | 68% | 304 | 31.7 |
| Shawn Bradley | 3902 | 30 | 21 | 68% | 289 | 28.6 |
| Toni Kukoc | 3713 | 73 | 49 | 68% | 156 | 28.2 |
| Brian Grant | 4093 | 22 | 15 | 67% | 291 | 30.0 |
| Terrell Brandon | 2289 | 49 | 32 | 67% | 457 | 26.6 |
| Harvey Grant | 4779 | 21 | 14 | 66% | 293 | 28.2 |
| Jerome Williams | 2017 | 25 | 17 | 66% | 242 | 19.6 |
| Dana Barros | 5646 | 38 | 25 | 65% | 372 | 20.1 |
| Scott Brooks | 2741 | 18 | 11 | 64% | 316 | 14.0 |
| Mark Blount | 2920 | 17 | 10 | 63% | 108 | 14.0 |
| Mark Aguirre | 5273 | 45 | 29 | 63% | 365 | 33.8 |
| Michael Jordan | 6136 | 201 | 126 | 63% | 427 | 39.0 |
| John Salley | 3196 | 17 | 10 | 62% | 387 | 21.9 |
| Antoine Walker | 3667 | 26 | 16 | 62% | 528 | 39.4 |
| Larry Johnson | 5025 | 26 | 16 | 62% | 377 | 38.8 |
| Kevin Johnson | 3801 | 45 | 27 | 61% | 387 | 36.6 |
| Kevin Gamble | 4421 | 21 | 12 | 60% | 288 | 22.9 |
| Tom Gugliotta | 4713 | 36 | 22 | 60% | 243 | 35.6 |
| Xavier Mcdaniel | 2873 | 20 | 12 | 58% | 393 | 33.7 |
| Adrian Griffin | 2753 | 19 | 11 | 58% | 116 | 19.9 |
| Vernon Maxwell | 4822 | 28 | 16 | 57% | 271 | 31.4 |
| Paul Pressey | 4761 | 46 | 25 | 56% | 240 | 25.6 |
| Nick Anderson | 4880 | 48 | 25 | 53% | 447 | 31.9 |
| Kiki Vandeweghe | 5820 | 37 | 19 | 51% | 365 | 33.7 |
| Isiah Thomas | 5917 | 37 | 19 | 51% | 555 | 36.7 |
| Rex Chapman | 3323 | 32 | 16 | 50% | 320 | 29.5 |
| Jalen Rose | 6097 | 26 | 13 | 49% | 438 | 25.2 |
| Ed Pinckney | 3082 | 33 | 16 | 49% | 345 | 19.7 |
| James Worthy | 5920 | 49 | 23 | 48% | 471 | 31.9 |
| Chris Morris | 3480 | 30 | 14 | 48% | 389 | 30.3 |
| Kenny Smith | 4631 | 29 | 14 | 47% | 334 | 34.2 |
| Jamal Mashburn | 5817 | 35 | 16 | 46% | 286 | 35.3 |
| Chris Webber | 4910 | 75 | 35 | 46% | 405 | 37.7 |
| Sleepy Floyd | 3468 | 38 | 17 | 44% | 366 | 32.1 |
| Shawn Kemp | 5519 | 36 | 16 | 44% | 544 | 29.2 |
| Vern Fleming | 4805 | 26 | 11 | 44% | 398 | 33.1 |
| Wally Szczerbiak | 3733 | 47 | 20 | 42% | 317 | 33.4 |
| Derek Anderson | 4418 | 36 | 15 | 42% | 250 | 31.5 |
| Travis Best | 4148 | 28 | 11 | 40% | 266 | 19.6 |
| Sean Elliott | 4294 | 31 | 12 | 39% | 469 | 34.3 |
| Mark Price | 4518 | 80 | 31 | 39% | 311 | 31.7 |
| Shareef Abdur-Rahim | 2596 | 30 | 11 | 37% | 533 | 38.1 |
| Lafayette Lever | 5806 | 72 | 26 | 36% | 404 | 30.3 |
| Alvin Robertson | 5197 | 46 | 16 | 35% | 389 | 32.2 |
| Michael Adams | 5141 | 43 | 14 | 33% | 319 | 30.4 |
| Jay Humphries | 5544 | 39 | 12 | 30% | 335 | 29.4 |
| Sidney Moncrief | 5301 | 59 | 17 | 28% | 392 | 32.5 |
| Anfernee Hardaway | 4197 | 43 | 12 | 27% | 319 | 36.9 |
| Kerry Kittles | 4550 | 49 | 13 | 26% | 267 | 34.8 |
| Stephon Marbury | 5475 | 43 | 10 | 24% | 505 | 38.4 |
The upper part of this list is littered with names that came into the NBA with little expectations and went on to have long, successful careers. Perhaps a late start tends to produce a later than expected finish. Could this be driven by matured players who enter the NBA with fundamentals that last late in their NBA lifetime?
The bottom of the list is full of players with injury-riddled careers. Note that I excluded players with fewer than 10 actual MEWA after 29. Many such players either returned to Europe before their skills or health greatly diminished. Others saw their careers end under tragic non-basketball related circumstances. I accounted for neither of these factors explicitly in my projection of MEWA after 29 and foreign players could especially skew the results. Increasing the minimum actual MEWA effectively eliminated these types of individuals.
Observations
The following is a graph displaying the averages of MP during ages 28 and 29 and Games Played and MPG before 28 for the top and bottom halves of the initial list (labeled “exceed” and “burnout”):
Notice that although average minutes during ages 28 and 29 are higher for those who exceed expectations, cumulative career MPG AND GP up to age 27 are actually LOWER by a statistically significant margin. In fact, I estimate that saving a season’s worth of games before 28 corresponds with an increase of about 12% career effectiveness after 29 (using regression analysis weighted by expected MEWA). Similarly, saving 5 MPG before 28 typically results in an increase in career effectiveness of roughly 6% after 29.
However, considering both of these variables simultaneously reduces these variables to about 10% and 2%. To try to put this in perspective, I did a quick estimate and found that the typical 82 games “saved” before age 28 tend to add, over the course of their career, about 1/4 of the wins contributed by that player for an 82 game season.
Clearly, resting the second of back-to back road games or games when nursing an injury would be expected to have a more significant impact on career longevity. Most games in which such a player is not required to rest, but considered for rest by the team or player, fit these circumstances. Therefore, the above estimates really suggest that a season’s worth of borderline, “Should I play or should I rest?” games before age 28 tend to improve career effectiveness by 12% for the age of 30 and beyond.
Additionally, one might suspect that as players age, the 12% improvement in career effectiveness increases; but since the time available to make up the lost player contributions for a season are lessened, these players might “only” be expected to add 1/4 or less of one season’s wins contributed over the course of their career. (See the end of last week’s post for a basic expectation of the Spurs player’s future contributions.)
The aforementioned decrease of the impact of MPG when combined with GP in the regression model indicates the correlation of MPG and GP. The fact that GP is less affected suggests to me that MPG might be largely reduced because of injury, when minutes are most significantly reduced in line with a reduction in games played. Maybe players who give injuries more time to recover instead of pushing through last longer. Perhaps these players just know how to take care of their bodies. The tendency for players recovering from injury to perform at lower levels adds to the list of reasons to not rush a return.
From a coaching or organizational perspective, much else must be considered in addition to games and minutes played for each individual. Just to name a few, factors such as how a player is feeling, team need, medical advise all play significant roles. Unfortunately the available data doesn’t seem good enough to shed any more light on potential factors causing the results in my study.
Reducing Player Minutes
You might be thinking “Ok, great. So now there is support for the widely accepted theory that saving players prolongs careers, now what? Sitting the best players hurts the team for the current game and season.” There is at least one more important factor must be considered. Not all minutes are created equal.
Clearly, playing Duncan at the end of a 30-point blowout does more harm than good, so in order to maximize his impact given his “allotted minutes” (not that there should be a hard number), he should play in as many high leverage situations that fit his skill set as possible. My article on Clutch Adjusted Plus/Minus displays the comeback probabilities given the deficit and time remaining (see Comeback Probability Table).
Weighing minutes played by the impact of 3 points on comeback probability, we do find that the best players typically play a higher percentage of the team’s minutes. The following chart shows the differences between the Spurs player’s percentages of team minutes and clutch-weighted minutes:
And finally, here is a table of some select players:
| Player | Tm | GP | MP | %MP | %Clutch Wt MP | Diff |
|---|---|---|---|---|---|---|
| Beno Udrih | SAC | 79 | 2477 | 65% | 80% | 16% |
| Jason Terry | DAL | 77 | 2540 | 68% | 82% | 14% |
| Jason Kidd | DAL | 80 | 2881 | 74% | 87% | 13% |
| Kevin Garnett | BOS | 69 | 2060 | 62% | 70% | 9% |
| LeBron James | CLE | 76 | 2966 | 81% | 89% | 8% |
| Tim Duncan | SAS | 78 | 2438 | 65% | 73% | 8% |
| Dwyane Wade | MIA | 77 | 2792 | 75% | 83% | 8% |
| Steve Nash | PHO | 81 | 2660 | 68% | 75% | 7% |
| Monta Ellis | GSW | 64 | 2647 | 86% | 92% | 6% |
| Kobe Bryant | LAL | 73 | 2835 | 80% | 86% | 6% |
| Kevin Durant | OKC | 82 | 3239 | 82% | 88% | 6% |
| Dwight Howard | ORL | 82 | 2843 | 72% | 77% | 5% |
| Manu Ginobili | SAS | 75 | 2150 | 59% | 64% | 5% |
| Andre Iguodala | PHI | 82 | 3192 | 81% | 85% | 4% |
| Rudy Gay | MEM | 80 | 3175 | 82% | 86% | 4% |
| Richard Jefferson | SAS | 81 | 2520 | 64% | 68% | 3% |
| George Hill | SAS | 78 | 2276 | 60% | 63% | 3% |
| Gerald Wallace | CHR | 76 | 3119 | 85% | 87% | 3% |
| Tony Parker | SAS | 56 | 1728 | 64% | 66% | 2% |
| Antonio McDyess | SAS | 77 | 1617 | 44% | 43% | 0% |
| Malik Hairston | SAS | 47 | 317 | 14% | 11% | -3% |
| Keith Bogans | SAS | 79 | 1559 | 41% | 38% | -3% |
| Matt Bonner | SAS | 65 | 1161 | 37% | 34% | -3% |
| Aaron Brooks | HOU | 82 | 2919 | 73% | 70% | -3% |
| Roger Mason | SAS | 79 | 1515 | 40% | 36% | -4% |
| Luis Scola | HOU | 82 | 2671 | 67% | 62% | -5% |
| Marcus Haislip | SAS | 10 | 44 | 9% | 4% | -5% |
| Ian Mahinmi | SAS | 26 | 165 | 13% | 5% | -8% |
| DeJuan Blair | SAS | 82 | 1494 | 38% | 29% | -8% |
| Mike Miller | WAS | 54 | 1805 | 69% | 59% | -10% |
Notice how the better and/or older often are at the top of the [clutch-weighted minutes – actual minutes] list. It is also interesting to note that two of the most historically statistically-oriented teams are in opposite ends of the spectrum. The Mavs are arguably the team whose best player’s clutch-weighted minutes increase the most. Meanwhile, the Rockets best player’s clutch-weighted minutes seem to increase the least (or even decrease).
This could be somehow related to Daryl Morey’s comments on last year’s Sloan Sports Conference that the Rockets don’t make decisions accounting for clutch performance, to which Mark Cuban chimed in saying that his team has accounted for this factor in the past. Or it could just be because the Rockets have more role players who don’t always fit in “clutch†situations while the Mavs have older players on a more top-heavy team.
In summary, the Spurs best players can be effectively utilized over the long haul if Coach Popovich adequately focuses on resting key players during injury-related times, typically coinciding with events that have a low probability of impacting the Spurs’ 2011 results. I’m sure Coach Pop would not be surprised at all by this by this effect, but attaching some magnitude to this result, even a vague magnitude, can help with adjusting or assuring instinctive decision making. With the future in mind, perhaps Duncan, Ginobili and Parker shouldn’t play elite minutes for the Spurs this year, but still play elite “effective minutes†if healthy.


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