In a previous post, I showed the results for regressions of ezPM against 1-yr and 3-yr RAPM (regularized adjusted +/-). Now, let’s take a look at how the offensive and defensive components of ezPM correlate with their RAPM counterparts. If you are familiar with ezPM, then you know I typically calculate three separate components: O100, D100, and REB100. To enable comparison with RAPM data, I folded the REB100 into O100 and D100, to give total offense and defense components (i.e. that include offensive and defensive rebounding, respectively). Just as a quick refresher, I re-ran the regression for the overall metric comparison, this time weighting by possession number, and focusing only on the 3-yr data set:
Call:lm(formula = RAPM ~ EZPM100, data = tot, weights = POSS) Residuals: Min 1Q Median 3Q Max -528.06 -84.51 -7.21 64.96 613.87 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.81273 0.09070 8.96 <2e-16 *** EZPM100 0.60519 0.03686 16.42 <2e-16 *** ---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 142.3 on 381 degrees of freedom Multiple R-squared: 0.4144, Adjusted R-squared: 0.4129 F-statistic: 269.6 on 1 and 381 DF, p-value: < 2.2e-16RAPM as a function of EZPM (3-YR).
You can see that there is a slight increase in R^2 to 0.41 from 0.37 previously. Now, let’s look at the regression results for the offense:
lm(formula = OFF_RAPM ~ O100, data = off, weights = POSS) Residuals: Min 1Q Median 3Q Max -399.88 -84.15 -19.91 45.38 564.10 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.23537 0.09328 -2.523 0.0120 * O100 0.57146 0.04266 13.395 <2e-16 *** ---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 117.6 on 381 degrees of freedom Multiple R-squared: 0.3201, Adjusted R-squared: 0.3184 F-statistic: 179.4 on 1 and 381 DF, p-value: < 2.2e-16RAPM vs. EZPM (3-YR Offense)
The correlation between the individual offensive components of ezPM and RAPM is significant (p<2.2e-16) and R^2=0.32. As I did last time, I want to give a table showing the best and worst players according to an average of the two metrics (note Warriors guard Charlie Bell shows up on the Bottom 20):
Top 20 Offensive Players (> 5000 Possessions)
| RANK | NAME | OFF_RAPM | OFF_ezPM | AVG |
| 1 | LeBron James | 6.90 | 7.19 | 7.05 |
| 2 | Steve Nash | 7.80 | 5.56 | 6.68 |
| 3 | Dwyane Wade | 6.30 | 6.05 | 6.18 |
| 4 | Chris Paul | 5.10 | 7.07 | 6.09 |
| 5 | Dwight Howard | 3.60 | 6.37 | 4.99 |
| 6 | Deron Williams | 4.50 | 4.60 | 4.55 |
| 7 | Chauncey Billups | 3.90 | 4.72 | 4.31 |
| 8 | Pau Gasol | 2.90 | 5.67 | 4.29 |
| 9 | Dirk Nowitzki | 5.10 | 3.16 | 4.13 |
| 10 | Manu Ginobili | 4.20 | 3.74 | 3.97 |
| 11 | Kobe Bryant | 4.30 | 3.60 | 3.95 |
| 12 | Brandon Roy | 3.60 | 3.88 | 3.74 |
| 13 | Chris Bosh | 3.10 | 4.05 | 3.58 |
| 14 | Kevin Martin | 3.50 | 2.84 | 3.17 |
| 15 | Joe Johnson | 3.90 | 2.38 | 3.14 |
| 16 | Nene Hilario | 2.00 | 4.25 | 3.13 |
| 17 | Amare Stoudemire | 2.40 | 3.59 | 3.00 |
| 18 | Ty Lawson | 2.10 | 3.85 | 2.98 |
| 19 | Carmelo Anthony | 3.30 | 2.58 | 2.94 |
| 20 | Kevin Love | 0.90 | 4.94 | 2.92 |
Bottom 20 Offensive Players (> 5000 Possessions)
| RANK | NAME | OFF_RAPM | OFF_ezPM | AVG |
| 237 | Donte Greene | -1.10 | -2.76 | -1.93 |
| 236 | Chris Kaman | -2.80 | -0.83 | -1.82 |
| 235 | Rasual Butler | -2.10 | -1.41 | -1.76 |
| 234 | Yi Jianlian | -1.60 | -1.87 | -1.74 |
| 233 | J.J. Hickson | -3.30 | -0.07 | -1.69 |
| 232 | Jonny Flynn | -0.60 | -2.55 | -1.58 |
| 231 | Corey Brewer | -0.80 | -2.21 | -1.51 |
| 230 | Brandon Rush | -1.70 | -1.31 | -1.51 |
| 229 | Dahntay Jones | -2.90 | 0.08 | -1.41 |
| 228 | Darko Milicic | -2.10 | -0.45 | -1.28 |
| 227 | Jason Kapono | -0.80 | -1.74 | -1.27 |
| 226 | Tyrus Thomas | -2.30 | 0.14 | -1.08 |
| 225 | Andray Blatche | -1.70 | -0.41 | -1.06 |
| 224 | Spencer Hawes | -0.90 | -1.08 | -0.99 |
| 223 | Joel Anthony | -3.30 | 1.34 | -0.98 |
| 222 | Charlie Bell | -0.90 | -0.98 | -0.94 |
| 221 | Rafer Alston | -0.80 | -0.98 | -0.89 |
| 220 | Trevor Ariza | -1.40 | -0.32 | -0.86 |
| 219 | Tyreke Evans | -2.10 | 0.46 | -0.82 |
| 218 | Kurt Thomas | -1.80 | 0.18 | -0.81 |
Here are the results for the defense:
lm(formula = DEF_RAPM ~ D100, data = def, weights = POSS) Residuals: Min 1Q Median 3Q Max -392.80 -51.15 6.09 61.18 372.19 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.02475 0.08468 12.10 <2e-16 *** D100 0.54199 0.04460 12.15 <2e-16 *** ---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 102.1 on 381 degrees of freedom Multiple R-squared: 0.2793, Adjusted R-squared: 0.2774 F-statistic: 147.7 on 1 and 381 DF, p-value: < 2.2e-16ezPM vs. RAPM (3-YR Defense)
Once again the results are statistically significant (p<2.2e-16) and, perhaps, somewhat surprisingly, the R^2 value of 0.28 is only slightly lower than for the offense. This tells me that we are capturing quite a bit of the same defensive contributions as RAPM. To wrap it up, here are tables for the top and bottom players averaged by the two metrics (unfortunately, you will notice that $15M-man David Lee shows up on the less preferred of the two lists):
Top 20 Defensive Players (> 5000 possessions)
| RANK | NAME | DEF_RAPM | DEF_ezPM | AVG |
| 1 | Kevin Garnett | 6.2 | 2.49 | 4.35 |
| 2 | Dwight Howard | 3.0 | 3.87 | 3.44 |
| 3 | LeBron James | 3.8 | 2.6 | 3.20 |
| 4 | Andrew Bogut | 4.1 | 1.88 | 2.99 |
| 5 | Tim Duncan | 3.5 | 2.08 | 2.79 |
| 6 | Josh Smith | 3.8 | 1.48 | 2.64 |
| 7 | Gerald Wallace | 3.0 | 2.19 | 2.60 |
| 8 | Marcus Camby | 3.0 | 2.03 | 2.52 |
| 9 | Andrei Kirilenko | 2.6 | 1.88 | 2.24 |
| 10 | Ron Artest | 3.2 | 0.88 | 2.04 |
| 11 | Ben Wallace | 2.5 | 1.37 | 1.94 |
| 12 | Lamar Odom | 3.4 | 0.38 | 1.89 |
| 13 | Thabo Sefolosha | 2.5 | 0.98 | 1.74 |
| 14 | Kurt Thomas | 2.6 | 0.85 | 1.73 |
| 15 | Luol Deng | 2.8 | 0.61 | 1.71 |
| 16 | Trevor Ariza | 2.1 | 1.29 | 1.70 |
| 17 | Manu Ginobili | 1.6 | 1.53 | 1.57 |
| 18 | Tyrus Thomas | 1.6 | 1.43 | 1.52 |
| 19 | Anderson Varejao | 2.1 | 0.81 | 1.46 |
| 20 | James Harden | 2.0 | 0.9 | 1.45 |
Bottom 20 Defensive Players (> 5000 Possessions)
| RANK | NAME | DEF_RAPM | D100 | AVG |
| 237 | Andrea Bargnani | -3.1 | -3.07 | -3.09 |
| 236 | Aaron Brooks | -1.8 | -3.86 | -2.83 |
| 235 | Charlie Villanueva | -2.8 | -2.75 | -2.78 |
| 234 | Kevin Martin | -3.8 | -1.72 | -2.76 |
| 233 | Will Bynum | -1.8 | -3.47 | -2.64 |
| 232 | Jason Kapono | -1.1 | -3.98 | -2.54 |
| 231 | D.J. Augustin | -0.8 | -4.15 | -2.48 |
| 230 | JaVale McGee | -1.7 | -3.17 | -2.44 |
| 229 | Jason Maxiell | -1.2 | -3.61 | -2.41 |
| 228 | Spencer Hawes | -1.6 | -3.01 | -2.31 |
| 227 | Goran Dragic | -1.1 | -3.5 | -2.30 |
| 226 | Jose Calderon | -1.7 | -2.67 | -2.19 |
| 225 | Jeff Green | -2.2 | -2.14 | -2.17 |
| 223 | Antoine Wright | -0.9 | -3.43 | -2.17 |
| 224 | Jonny Flynn | -1.6 | -2.73 | -2.17 |
| 222 | J.J. Hickson | -2.6 | -1.63 | -2.12 |
| 221 | David Lee | -1.9 | -2.31 | -2.11 |
| 220 | Devin Harris | -1.1 | -3.03 | -2.07 |
| 219 | Ben Gordon | -2.1 | -1.99 | -2.05 |
| 218 | Maurice Evans | -1.5 | -2.52 | -2.01 |
The City 
Very few guys under 25 yrs on either top 20 while more than half of the bottom 20s are taken by them.
I thought Hickson had some promise but the advanced metrics of EZPM and Adjusted +/- both rate his performance as poor. So far the numbers suggest he is better at C but they play him more at PF.
Hi
Your league wide analyses are interesting but I’d like you to return to being the “Warriors Centric” blog. The hope is to find some keys to why they are not more successful. One question in my mind is should they or should they not play Jeremy Lin more.
Thanks
Norm, they should probably play Lin more, so he can develop. Then again, they should have let Brandan Wright do the same, so I’m not sure developing talent is their goal right now (unfortunately).
You want to know why the team is not winning more? I think the number one factor is that the talent is simply not there. Curry, Ellis, and D. Wright could probably all be pieces of winning teams. I’m not sure that the same could be said for Lee and Biedrins at this point. My opinion is that Lacob needs to seriously consider breaking this team up in the off-season and re-building from the ground up, perhaps, keeping only Curry, Williams, Udoh, and D. Wright who are each cheap and talented. Ellis and Biedrins should be traded for first round draft picks, if possible, or expiring contracts and multiple second round picks, at the very least. We’re stuck with Lee, because his contract is probably untradeable. I know it sounds gloomy, but it’s better to be realistic than to pretend there is something here that can legitimately contend for a title (or even a playoff spot).
Pingback: PSAMS Regressed on ORAPM: A New Variant of Statistical +/- for Offense | The City