Adjusted Plus/Minus (RAPM) is undoubtedly the most influential one-number metric in basketball analytics today, boosted by its unbiased nature (relatively speaking) which is impartial to playstyle and craftily captures both offense and defense on a fair playing field. Despite it strengths, however, RAPM has lots of intervening weaknesses which affect the metric’s ability to be interpreted. These include:
- The inability to assign credit based on performance.
- The “multicollinearity” problem from consistent lineup shuffling.
- The sampling issues caused by the penalization term.
For those unfamiliar, RAPM uses a “ridge” regression primarily because it is designed to introduce a slight bias in the model to offset the variability that would otherwise decrease the statistical significance of the coefficient estimates. To do this, most modern models use a penalization term (lambda) which tends to draw the coefficients closer to zero (or a prior, in the case of some hybrids) which is obtained via cross-validation, a process in which a data set is continually split into training and testing groups to find an ideal value.
Seems good enough, right? It’s certainly more “objective” than plotting the coefficients by lambda and picking a visually-appealing spot somewhere on the graph. But alas, there is one particular issue to address which hinders RAPM’s ability to be easily interpreted from season-to-season: a lambda value by cross-validation is chosen because it appeases the fit of the model. (And we know the caveats of what happens when solely prioritizing model fit.) So what happens when the model isn’t necessarily right?
The results can either “expand” or “compress” to an undesirable degree! Specifically, we’re dealing with the imperfections in how the metric deviates from its mean, meaning a lambda too high can excessively compress the coefficients and a lambda too low can excessively expand the results! So how do we solve this problem? At least today, by scaling the results based on external criteria. (This is not an original idea; Ben Taylor of Back Picks devised a variant of RAPM in which the coefficients were normalized to the same mean and standard deviation and Twitter user @SamphaStan applied the principle to multiple versions of RAPM.)
Similarly, I believed this to be a route worth exploring when scaling RAPM. But rather than standardizing the deviation of the metric from season-to-season, I considered something else: accounting for the natural fluctuations in offensive and defensive impact by proxy of results at the team level. (Remember, the NBA in 1997 is different than the NBA in 2022, and the evolutions of coaching and playstyle will affect how much individual players can impact the game.) But what exactly constitutes the “league” deviation in offense and defense?
By treating the team’s results (offensive and defensive efficiency) as the whole of the sum of its players, we can estimate the “actual” deviation by assigning equal credit to the “average” five players on the court. Doing this created a (seemingly) more “natural” curve that was unrelated to appeasing RAPM’s model fit, which is exactly what we wanted!
Notice how both offensive and defensive variation tapers off in the mid-2010s? This was an observation that intuitively felt inaccurate, as both qual and quant research tends to agree offensive superstars are creating more separation from their competitors than ever in the modern game. Interestingly enough, the scaled version of RAPM seems to “correct” this, as the “actual” highest deviation in offense for 2019 was the highest since 2008.
To summarize of the calculation process:
- Calculate the (weighted) means for both offense and defense; set new means to zero
- Calculate the (weighted) standard deviations for both offense and defense for RAPM and teams; adjust for team so it reflects the individual players
- Re-calculate RAPM so that the (weighted) standard deviation equals the estimated “actual” (weighted) standard deviation; do for both offense and defense
Listed below are the top-250 seasons on record from 1997 to 2019 among players with more than 3,000 possessions during the regular season (more or less 1,500 minutes played).
Player | Year | Team | G | Poss | ORAPM | DRAPM | RAPM |
---|---|---|---|---|---|---|---|
Kevin Garnett | 2004 | MIN | 82 | 6150 | 4.59 | 4.38 | 8.97 |
Kevin Garnett | 2009 | BOS | 57 | 3416 | 2.10 | 5.80 | 7.90 |
LeBron James | 2010 | CLE | 76 | 5728 | 5.96 | 1.77 | 7.73 |
LeBron James | 2011 | MIA | 79 | 5916 | 4.77 | 2.89 | 7.66 |
Kevin Garnett | 2008 | BOS | 71 | 4468 | 2.48 | 4.99 | 7.48 |
LeBron James | 2015 | CLE | 69 | 4889 | 6.04 | 1.42 | 7.46 |
Giannis Antetokounmpo | 2019 | MIL | 72 | 5174 | 3.57 | 3.81 | 7.38 |
Tim Duncan | 2003 | SAS | 81 | 6058 | 3.48 | 3.89 | 7.37 |
LeBron James | 2012 | MIA | 62 | 4533 | 4.84 | 2.51 | 7.35 |
Tim Duncan | 2004 | SAS | 69 | 4741 | 2.23 | 4.83 | 7.06 |
Draymond Green | 2016 | GSW | 81 | 5974 | 2.98 | 4.05 | 7.03 |
LeBron James | 2016 | CLE | 76 | 5344 | 4.29 | 2.53 | 6.82 |
Tim Duncan | 2005 | SAS | 66 | 4134 | 2.43 | 4.38 | 6.81 |
Shaquille O’Neal | 1998 | LAL | 60 | 4273 | 4.55 | 2.23 | 6.78 |
Shaquille O’Neal | 2004 | LAL | 67 | 4830 | 3.96 | 2.66 | 6.63 |
Kevin Garnett | 2005 | MIN | 82 | 5946 | 3.73 | 2.87 | 6.60 |
Kevin Garnett | 2003 | MIN | 82 | 6480 | 3.90 | 2.67 | 6.57 |
LeBron James | 2009 | CLE | 81 | 5769 | 4.30 | 2.23 | 6.52 |
Kevin Durant | 2019 | GSW | 78 | 5813 | 5.50 | 0.89 | 6.39 |
Shaquille O’Neal | 2001 | LAL | 74 | 5655 | 5.73 | 0.63 | 6.36 |
Dirk Nowitzki | 2004 | DAL | 77 | 5792 | 4.85 | 1.41 | 6.26 |
Alonzo Mourning | 1999 | MIA | 46 | 3152 | 1.95 | 4.30 | 6.25 |
Stephen Curry | 2017 | GSW | 79 | 5690 | 5.20 | 1.03 | 6.23 |
Dirk Nowitzki | 2003 | DAL | 80 | 6118 | 4.28 | 1.84 | 6.11 |
Alonzo Mourning | 1998 | MIA | 58 | 3542 | 2.31 | 3.78 | 6.10 |
Rasheed Wallace | 2004 | DET | 68 | 4428 | 1.81 | 4.26 | 6.07 |
Shaquille O’Neal | 2000 | LAL | 79 | 6211 | 4.12 | 1.89 | 6.01 |
Tim Duncan | 2001 | SAS | 82 | 6039 | 2.19 | 3.81 | 6.00 |
Stephen Curry | 2015 | GSW | 80 | 5534 | 5.22 | 0.76 | 5.98 |
Dwyane Wade | 2010 | MIA | 77 | 5390 | 5.71 | 0.26 | 5.97 |
Steve Nash | 2008 | PHX | 81 | 5710 | 6.00 | -0.04 | 5.96 |
Chris Paul | 2015 | LAC | 82 | 5729 | 4.37 | 1.43 | 5.80 |
Jrue Holiday | 2019 | NOP | 67 | 5212 | 4.33 | 1.47 | 5.80 |
Mookie Blaylock | 1998 | ATL | 70 | 5003 | 3.75 | 2.00 | 5.75 |
Shaquille O’Neal | 1999 | LAL | 49 | 3286 | 4.23 | 1.50 | 5.73 |
Kevin Garnett | 2012 | BOS | 60 | 3583 | 1.40 | 4.32 | 5.72 |
Dirk Nowitzki | 2012 | DAL | 62 | 4057 | 3.55 | 2.13 | 5.68 |
Shaquille O’Neal | 2003 | LAL | 67 | 4991 | 4.79 | 0.87 | 5.66 |
Dirk Nowitzki | 2011 | DAL | 73 | 4909 | 3.21 | 2.36 | 5.57 |
Stephen Curry | 2019 | GSW | 69 | 5058 | 3.95 | 1.61 | 5.56 |
Manu Ginobili | 2008 | SAS | 74 | 4470 | 3.52 | 1.98 | 5.50 |
Michael Jordan | 1998 | CHI | 82 | 6010 | 3.93 | 1.53 | 5.46 |
Dirk Nowitzki | 2013 | DAL | 53 | 3332 | 3.73 | 1.73 | 5.46 |
Kobe Bryant | 2008 | LAL | 82 | 6488 | 5.20 | 0.25 | 5.45 |
Shaquille O’Neal | 2002 | LAL | 67 | 4728 | 3.47 | 1.98 | 5.45 |
Paul George | 2019 | OKC | 77 | 6172 | 3.67 | 1.77 | 5.44 |
Metta World Peace | 2006 | SAC | 56 | 4255 | 1.33 | 4.10 | 5.43 |
Tim Duncan | 2002 | SAS | 82 | 6324 | 1.87 | 3.51 | 5.39 |
Christian Laettner | 1997 | ATL | 82 | 5703 | 2.47 | 2.91 | 5.38 |
Manu Ginobili | 2005 | SAS | 74 | 4191 | 3.06 | 2.30 | 5.37 |
LeBron James | 2014 | MIA | 77 | 5634 | 5.07 | 0.26 | 5.33 |
LeBron James | 2013 | MIA | 76 | 5549 | 4.80 | 0.48 | 5.28 |
LeBron James | 2008 | CLE | 75 | 5831 | 4.19 | 1.07 | 5.26 |
Steve Nash | 2010 | PHX | 81 | 5426 | 5.62 | -0.36 | 5.26 |
Tim Duncan | 2007 | SAS | 80 | 5125 | 1.71 | 3.55 | 5.26 |
John Stockton | 2001 | UTA | 82 | 4621 | 3.08 | 2.17 | 5.25 |
Manu Ginobili | 2007 | SAS | 75 | 4006 | 3.08 | 2.17 | 5.25 |
Bo Outlaw | 1997 | LAC | 82 | 4224 | 0.79 | 4.45 | 5.24 |
Chris Paul | 2014 | LAC | 62 | 4453 | 3.45 | 1.77 | 5.22 |
Kevin Garnett | 2010 | BOS | 69 | 4041 | 0.29 | 4.84 | 5.13 |
Jason Kidd | 2005 | NJN | 66 | 4710 | 3.37 | 1.74 | 5.11 |
Kevin Garnett | 2000 | MIN | 81 | 6293 | 2.20 | 2.89 | 5.10 |
Chris Paul | 2013 | LAC | 70 | 4489 | 4.56 | 0.53 | 5.10 |
Shaquille O’Neal | 2005 | MIA | 73 | 4815 | 3.41 | 1.68 | 5.09 |
Kevin Garnett | 2011 | BOS | 71 | 4276 | -0.03 | 5.11 | 5.08 |
Michael Jordan | 1997 | CHI | 82 | 5928 | 3.22 | 1.83 | 5.06 |
Mike Conley | 2013 | MEM | 80 | 5168 | 3.29 | 1.77 | 5.06 |
Andre Iguodala | 2014 | GSW | 63 | 4145 | 1.20 | 3.86 | 5.06 |
Tim Hardaway | 1997 | MIA | 81 | 5917 | 2.67 | 2.38 | 5.05 |
LeBron James | 2017 | CLE | 74 | 5655 | 4.79 | 0.25 | 5.05 |
Mookie Blaylock | 1999 | ATL | 48 | 3224 | 2.87 | 2.18 | 5.05 |
Terry Mills | 1997 | DET | 79 | 3592 | 3.31 | 1.72 | 5.03 |
Tim Duncan | 2006 | SAS | 80 | 5188 | 0.99 | 4.03 | 5.02 |
Andrei Kirilenko | 2004 | UTA | 78 | 5363 | 3.08 | 1.92 | 5.00 |
Dwight Howard | 2011 | ORL | 78 | 5630 | 1.08 | 3.91 | 4.99 |
Tim Hardaway | 1998 | MIA | 81 | 5705 | 4.00 | 0.97 | 4.97 |
John Stockton | 2002 | UTA | 82 | 4957 | 2.68 | 2.27 | 4.95 |
Dirk Nowitzki | 2002 | DAL | 76 | 5731 | 3.99 | 0.93 | 4.93 |
Kevin Garnett | 2013 | BOS | 68 | 3899 | -0.40 | 5.31 | 4.90 |
Kevin Garnett | 1999 | MIN | 47 | 3467 | 1.49 | 3.40 | 4.89 |
Dwight Howard | 2010 | ORL | 82 | 5469 | 2.43 | 2.42 | 4.84 |
Kevin Garnett | 1998 | MIN | 82 | 6313 | 1.85 | 2.96 | 4.81 |
Alonzo Mourning | 1997 | MIA | 66 | 4332 | 0.88 | 3.93 | 4.81 |
Dikembe Mutombo | 2000 | ATL | 82 | 5750 | -0.18 | 4.97 | 4.78 |
Karl Malone | 1998 | UTA | 81 | 5788 | 4.66 | 0.11 | 4.77 |
Manu Ginobili | 2006 | SAS | 65 | 3510 | 3.12 | 1.65 | 4.77 |
Davis Bertans | 2019 | SAS | 76 | 3454 | 3.04 | 1.73 | 4.77 |
Dirk Nowitzki | 2005 | DAL | 78 | 5962 | 2.95 | 1.76 | 4.71 |
Andrei Kirilenko | 2006 | UTA | 69 | 4826 | 2.13 | 2.59 | 4.71 |
Stephen Curry | 2018 | GSW | 51 | 3575 | 4.83 | -0.12 | 4.71 |
Ben Wallace | 2004 | DET | 81 | 5726 | -0.13 | 4.80 | 4.67 |
Dirk Nowitzki | 2008 | DAL | 77 | 5319 | 3.99 | 0.67 | 4.66 |
Dwight Howard | 2012 | ORL | 54 | 3891 | 0.82 | 3.83 | 4.65 |
Draymond Green | 2015 | GSW | 79 | 5220 | 1.57 | 3.06 | 4.64 |
Scottie Pippen | 1997 | CHI | 82 | 5924 | 3.38 | 1.25 | 4.63 |
David Robinson | 2000 | SAS | 80 | 4867 | 1.14 | 3.48 | 4.62 |
Steve Nash | 2011 | PHX | 75 | 5030 | 4.22 | 0.38 | 4.61 |
LaMarcus Aldridge | 2018 | SAS | 75 | 4955 | 2.05 | 2.56 | 4.61 |
John Stockton | 1998 | UTA | 64 | 3525 | 2.80 | 1.82 | 4.61 |
Nikola Jokic | 2016 | DEN | 80 | 3488 | 0.25 | 4.36 | 4.61 |
Nick Collison | 2013 | OKC | 81 | 3150 | 2.15 | 2.46 | 4.61 |
Joel Embiid | 2019 | PHI | 64 | 4672 | 2.30 | 2.29 | 4.60 |
Lamar Odom | 2009 | LAL | 78 | 4640 | 1.45 | 3.15 | 4.59 |
Amir Johnson | 2013 | TOR | 81 | 4439 | 2.67 | 1.91 | 4.57 |
Vlade Divac | 1999 | SAC | 50 | 3594 | 1.29 | 3.25 | 4.54 |
Ray Allen | 2003 | SEA | 76 | 5456 | 3.74 | 0.79 | 4.53 |
Kyle Lowry | 2016 | TOR | 77 | 5571 | 2.63 | 1.88 | 4.50 |
Mike Conley | 2019 | MEM | 70 | 4782 | 3.36 | 1.14 | 4.50 |
Mookie Blaylock | 1997 | ATL | 78 | 5603 | 3.36 | 1.13 | 4.49 |
Chris Paul | 2011 | NOH | 80 | 5438 | 3.27 | 1.22 | 4.49 |
Eddie Jones | 2000 | CHH | 72 | 5581 | 1.94 | 2.53 | 4.48 |
Jason Kidd | 2004 | NJN | 67 | 4765 | 2.80 | 1.68 | 4.48 |
Dwyane Wade | 2012 | MIA | 49 | 3184 | 3.99 | 0.47 | 4.47 |
Chris Paul | 2012 | LAC | 60 | 4116 | 3.34 | 1.12 | 4.46 |
Russell Westbrook | 2016 | OKC | 80 | 5661 | 3.88 | 0.57 | 4.45 |
Steve Nash | 2009 | PHX | 74 | 5109 | 4.86 | -0.41 | 4.45 |
Damian Lillard | 2019 | POR | 80 | 5984 | 3.99 | 0.45 | 4.44 |
Vlade Divac | 1998 | CHH | 64 | 3441 | 1.56 | 2.89 | 4.44 |
Grant Hill | 1998 | DET | 81 | 6193 | 2.74 | 1.69 | 4.43 |
Danny Green | 2019 | TOR | 80 | 4667 | 3.23 | 1.19 | 4.43 |
Lamar Odom | 2011 | LAL | 82 | 5077 | 1.59 | 2.83 | 4.42 |
Gary Payton | 2000 | SEA | 82 | 6769 | 3.58 | 0.82 | 4.40 |
Kobe Bryant | 2010 | LAL | 73 | 5570 | 3.69 | 0.71 | 4.40 |
Lamar Odom | 2010 | LAL | 82 | 5127 | 1.05 | 3.34 | 4.40 |
John Stockton | 2003 | UTA | 82 | 4337 | 2.69 | 1.72 | 4.40 |
Kevin Garnett | 2006 | MIN | 76 | 5537 | 2.03 | 2.36 | 4.39 |
Rasheed Wallace | 2000 | POR | 81 | 5436 | 1.01 | 3.38 | 4.39 |
Jeff Hornacek | 1998 | UTA | 80 | 4754 | 3.77 | 0.61 | 4.39 |
John Stockton | 2000 | UTA | 82 | 4696 | 1.94 | 2.43 | 4.37 |
Reggie Miller | 1998 | IND | 81 | 5293 | 3.81 | 0.52 | 4.33 |
Tim Duncan | 2008 | SAS | 78 | 4940 | 1.65 | 2.68 | 4.33 |
Paul Millsap | 2012 | UTA | 64 | 4060 | 2.14 | 2.19 | 4.33 |
Grant Hill | 1999 | DET | 50 | 3444 | 3.11 | 1.21 | 4.33 |
Nikola Jokic | 2018 | DEN | 75 | 4983 | 2.95 | 1.37 | 4.32 |
Tim Duncan | 2012 | SAS | 58 | 3192 | 0.48 | 3.82 | 4.30 |
Metta World Peace | 2008 | SAC | 57 | 4376 | 1.62 | 2.67 | 4.29 |
Shawn Bradley | 2001 | DAL | 82 | 3860 | -0.46 | 4.75 | 4.29 |
Patrick Ewing | 1997 | NYK | 78 | 5586 | 0.50 | 3.78 | 4.28 |
Kawhi Leonard | 2015 | SAS | 64 | 4029 | 1.57 | 2.71 | 4.28 |
Chris Paul | 2016 | LAC | 74 | 4935 | 3.19 | 1.08 | 4.27 |
James Harden | 2015 | HOU | 81 | 6056 | 5.13 | -0.88 | 4.25 |
LeBron James | 2019 | LAL | 55 | 4218 | 2.59 | 1.66 | 4.25 |
Chris Paul | 2017 | LAC | 61 | 3889 | 2.61 | 1.64 | 4.25 |
Dwyane Wade | 2011 | MIA | 76 | 5471 | 3.98 | 0.26 | 4.24 |
Tracy McGrady | 2003 | ORL | 75 | 5868 | 3.84 | 0.38 | 4.23 |
Steve Nash | 2007 | PHX | 76 | 5452 | 4.31 | -0.10 | 4.22 |
Shane Battier | 2006 | MEM | 81 | 5196 | 0.41 | 3.81 | 4.22 |
Jeff Hornacek | 1997 | UTA | 82 | 5007 | 3.00 | 1.21 | 4.21 |
Toni Kukoc | 1998 | CHI | 74 | 4249 | 1.42 | 2.78 | 4.20 |
Kevin Garnett | 1997 | MIN | 77 | 5742 | 0.24 | 3.95 | 4.19 |
Robert Horry | 1998 | LAL | 72 | 4337 | 2.32 | 1.86 | 4.19 |
Dwyane Wade | 2008 | MIA | 51 | 3836 | 4.03 | 0.16 | 4.19 |
Eric Gordon | 2019 | HOU | 68 | 4483 | 3.92 | 0.25 | 4.18 |
Vlade Divac | 2000 | SAC | 82 | 5104 | 0.85 | 3.32 | 4.17 |
Kyle Korver | 2015 | ATL | 75 | 4866 | 3.12 | 1.04 | 4.17 |
Rashard Lewis | 2009 | ORL | 79 | 5609 | 2.16 | 2.00 | 4.16 |
Tim Duncan | 2013 | SAS | 69 | 4124 | 0.91 | 3.25 | 4.15 |
Detlef Schrempf | 1998 | SEA | 78 | 5199 | 3.64 | 0.50 | 4.14 |
Ray Allen | 2010 | BOS | 80 | 5481 | 3.28 | 0.85 | 4.13 |
Ray Allen | 2001 | MIL | 82 | 6164 | 3.67 | 0.46 | 4.12 |
Russell Westbrook | 2015 | OKC | 67 | 4769 | 4.50 | -0.40 | 4.11 |
Shane Battier | 2007 | HOU | 82 | 5767 | 0.28 | 3.82 | 4.10 |
Steve Nash | 2012 | PHX | 62 | 3898 | 3.72 | 0.37 | 4.09 |
Nene | 2011 | DEN | 75 | 4550 | 0.69 | 3.38 | 4.08 |
Andre Iguodala | 2015 | GSW | 77 | 4283 | 1.66 | 2.41 | 4.08 |
Stephen Curry | 2014 | GSW | 78 | 5826 | 3.62 | 0.45 | 4.07 |
Dirk Nowitzki | 2014 | DAL | 80 | 5292 | 3.02 | 1.05 | 4.07 |
Alonzo Mourning | 2000 | MIA | 79 | 5268 | 1.10 | 2.96 | 4.06 |
Manu Ginobili | 2014 | SAS | 68 | 3199 | 3.51 | 0.54 | 4.05 |
Paul Pierce | 2008 | BOS | 80 | 5524 | 3.39 | 0.65 | 4.04 |
Chris Paul | 2010 | NOH | 45 | 3303 | 3.87 | 0.17 | 4.04 |
Baron Davis | 2005 | GSW | 46 | 3062 | 2.85 | 1.19 | 4.04 |
Paul Pierce | 2005 | BOS | 82 | 5874 | 2.60 | 1.44 | 4.03 |
Dikembe Mutombo | 1999 | ATL | 50 | 3314 | -1.66 | 5.70 | 4.03 |
Ben Wallace | 2006 | DET | 82 | 5299 | -0.13 | 4.14 | 4.01 |
Rudy Gobert | 2017 | UTA | 81 | 5246 | 1.53 | 2.47 | 4.00 |
Kobe Bryant | 2001 | LAL | 68 | 5460 | 3.18 | 0.80 | 3.97 |
Eric Bledsoe | 2019 | MIL | 78 | 4948 | 1.43 | 2.53 | 3.96 |
Steve Nash | 2005 | PHX | 75 | 5228 | 4.92 | -0.99 | 3.94 |
Arvydas Sabonis | 1998 | POR | 73 | 4475 | 1.67 | 2.27 | 3.94 |
Dirk Nowitzki | 2006 | DAL | 81 | 5774 | 2.76 | 1.16 | 3.93 |
Rasheed Wallace | 2005 | DET | 79 | 4979 | 1.14 | 2.79 | 3.93 |
Kawhi Leonard | 2016 | SAS | 72 | 4702 | 0.74 | 3.19 | 3.93 |
Robert Covington | 2018 | PHI | 80 | 5347 | 0.98 | 2.93 | 3.92 |
Baron Davis | 2004 | NOH | 67 | 5097 | 2.09 | 1.83 | 3.92 |
Grant Hill | 2000 | DET | 74 | 5697 | 3.60 | 0.31 | 3.91 |
Tim Duncan | 1999 | SAS | 50 | 3686 | 1.83 | 2.08 | 3.91 |
Dirk Nowitzki | 2010 | DAL | 81 | 5954 | 2.49 | 1.41 | 3.90 |
Kyle Lowry | 2017 | TOR | 60 | 4469 | 2.00 | 1.90 | 3.90 |
Dirk Nowitzki | 2001 | DAL | 82 | 6192 | 1.80 | 2.09 | 3.89 |
Scottie Pippen | 1998 | CHI | 44 | 3166 | 3.60 | 0.29 | 3.89 |
Dwyane Wade | 2007 | MIA | 51 | 3767 | 3.10 | 0.78 | 3.88 |
Greg Ostertag | 1997 | UTA | 77 | 3425 | 1.11 | 2.77 | 3.88 |
Tracy McGrady | 2002 | ORL | 76 | 5729 | 2.72 | 1.15 | 3.87 |
Gary Payton | 1998 | SEA | 82 | 5982 | 3.16 | 0.70 | 3.86 |
Tim Duncan | 2009 | SAS | 75 | 4712 | 1.19 | 2.67 | 3.86 |
Vince Carter | 2013 | DAL | 81 | 4250 | 2.22 | 1.62 | 3.85 |
Kevin Garnett | 2007 | MIN | 76 | 5745 | 1.76 | 2.08 | 3.84 |
Manu Ginobili | 2015 | SAS | 70 | 3217 | 2.45 | 1.38 | 3.83 |
Rasheed Wallace | 2006 | DET | 80 | 5060 | 1.18 | 2.63 | 3.81 |
Patrick Beverley | 2014 | HOU | 56 | 3500 | 2.30 | 1.51 | 3.81 |
Baron Davis | 2008 | GSW | 82 | 6701 | 2.96 | 0.84 | 3.80 |
Rasheed Wallace | 1998 | POR | 77 | 5505 | 0.81 | 2.99 | 3.80 |
Victor Oladipo | 2018 | IND | 75 | 5225 | 1.44 | 2.36 | 3.80 |
Paul Millsap | 2013 | UTA | 78 | 4560 | 1.69 | 2.11 | 3.80 |
Chauncey Billups | 2009 | DEN | 79 | 5506 | 3.40 | 0.39 | 3.79 |
Vlade Divac | 1997 | CHH | 81 | 5290 | 1.79 | 2.00 | 3.79 |
Steve Nash | 2006 | PHX | 79 | 5679 | 4.27 | -0.50 | 3.77 |
Kemba Walker | 2018 | CHA | 80 | 5669 | 2.25 | 1.52 | 3.77 |
Bo Outlaw | 2000 | ORL | 82 | 4718 | 0.04 | 3.73 | 3.77 |
Robert Horry | 2000 | LAL | 76 | 3410 | 1.38 | 2.38 | 3.77 |
Chris Paul | 2018 | HOU | 58 | 3792 | 2.61 | 1.15 | 3.76 |
Kyle Korver | 2018 | CLE | 73 | 3313 | 2.52 | 1.23 | 3.75 |
Rasheed Wallace | 2002 | POR | 79 | 5623 | 1.29 | 2.45 | 3.74 |
Josh Smith | 2012 | ATL | 66 | 4441 | 0.64 | 3.10 | 3.74 |
Derek Fisher | 2002 | LAL | 70 | 3887 | 2.54 | 1.20 | 3.74 |
Dirk Nowitzki | 2009 | DAL | 81 | 5972 | 2.95 | 0.78 | 3.73 |
Tim Duncan | 1998 | SAS | 82 | 5959 | 2.13 | 1.60 | 3.73 |
Shawn Marion | 2001 | PHX | 79 | 5625 | 0.52 | 3.20 | 3.73 |
Gheorghe Muresan | 1997 | WAS | 73 | 3568 | 0.46 | 3.28 | 3.73 |
LeBron James | 2007 | CLE | 78 | 6124 | 2.30 | 1.42 | 3.72 |
Kevin Durant | 2014 | OKC | 81 | 6342 | 3.50 | 0.20 | 3.71 |
Shaquille O’Neal | 2006 | MIA | 59 | 3507 | 1.66 | 2.05 | 3.71 |
Paul Pierce | 2004 | BOS | 80 | 6131 | 2.04 | 1.66 | 3.70 |
Andre Miller | 2001 | CLE | 82 | 5516 | 2.39 | 1.31 | 3.70 |
Dirk Nowitzki | 2007 | DAL | 78 | 5343 | 2.95 | 0.75 | 3.69 |
Dwyane Wade | 2006 | MIA | 75 | 5643 | 2.74 | 0.94 | 3.68 |
Bo Outlaw | 1998 | ORL | 82 | 5422 | 0.51 | 3.16 | 3.68 |
Omer Asik | 2013 | HOU | 82 | 4983 | 0.09 | 3.59 | 3.68 |
Andrew Bogut | 2011 | MIL | 65 | 4296 | -0.87 | 4.54 | 3.67 |
Rasheed Wallace | 2003 | POR | 74 | 5088 | 1.34 | 2.32 | 3.66 |
Jusuf Nurkic | 2019 | POR | 72 | 4171 | 1.53 | 2.13 | 3.66 |
Andre Miller | 2012 | DEN | 66 | 3569 | 2.93 | 0.72 | 3.66 |
Damian Lillard | 2017 | POR | 75 | 5511 | 4.65 | -1.00 | 3.65 |
Kawhi Leonard | 2017 | SAS | 74 | 4891 | 3.05 | 0.60 | 3.65 |
- Data is from NBA.com; RAPM is prior-informed (regresses toward prior year’s data point rather than zero)