Durant was voted Rookie of the Year by the media and to the All-Rookie First Team by the coaches. John Hollinger’s Player Efficiency Rating – which Abbott argued was “the most respected single number to express a player’s total contributions”— says that Durant was above average his rookie season and ranked him as the 20th best player last season.
So which story is correct?
Three Perspectives on Durant
The answer to this question begins with the observation that PER is probably not “the most respected” measure of performance in the NBA. One only has to read Wayne Winston’s Mathletics to see the problems with Hollinger’s performance measure (or one can read what was said in this forum three years ago). The reason Durant has such a lofty PER is because the takes a large number of shots. A player only has to exceed very low levels of shooting efficiency for his PER to rise with more field goal attempts. Hence, top scorers tend to look good according to PER, even if – as Durant was his rookie season – the player can’t shoot very well.
Okay, PER has problems. So does adjusted plus-minus paint a better picture? Adjusted plus-minus ignores the box score statistics and simply looks at how a team does when a player is on the court. The “adjusted” part involves attempts to control for the impact of the player’s teammates and the team’s opponent. Theoretically, everything a player does – and that includes on-the-ball defense – should be incorporated in his adjusted plus-minus number. Hence, this measure should tell us more than what we see in the box score.
As every proponent of this measure notes, though, adjusted plus-minus is a very “noisy” measure. What does this mean? Typically there is a large standard error associated with the number assigned to any player. Hence, it is difficult to be sure whether many players have a positive or negative impact on outcomes. Furthermore, there’s very little consistency – at least relative to the box score numbers – in a player’s adjusted plus-minus numbers across time. As we very briefly discuss in our next book (due out next March), a player’s adjusted plus-minus value in this season is not highly correlated with what he did last year.
JC Bradbury notes that a performance metric should be both correlated with outcomes (i.e. typically wins in sports) and consistent over time. The former is important because we wish to know if a player is having a positive or negative impact on outcomes. The latter is important because inconsistent measure suggests you are not accurately capturing a player’s impact. PER fails the first test (it’s not highly correlated with team wins). And adjusted plus-minus has a problem on the second count.
And that brings us to Wins Produced. This measure is both connected to team wins and relatively consistent across time. Durant’s Wins Produced for his rookie season confirms the adjusted plus-minus story. In 2007-08, Durant produced 0.7 Wins Produced and posted a 0.012 WP48 [Wins Produced per 48 minutes]. Because Durant could score, though, his PER was above average and he was named Rookie of the Year. Wins Produced, though, indicated that Durant was not the best rookie. In fact, he wasn’t even one of the better players on a bad Thunder team.
In 2008-09, though, the Wins Produced story changed. As is often the case, young players get better. And Durant went from being below average to being the most productive player on the Thunder (yes, Wins Produced is relatively consistent across time, but young players – as noted – can get better).
Once again, the box score does not capture an individual player’s on-the-ball defense. If a player is a better defender than his teammates, his Wins Produced will understate his value. And if he is a worse defender, then Wins Produced will overstate his value.
With respect to Durant, adjusted plus-minus insists that he is a poor player. So it’s possible that Durant is just a really bad defensive player. Of course, it’s possible that the noise in the model is also producing this result (although Winston says that in this case, that doesn’t seem likely).
So is it defense or noise? Should we believe Wins Produced or adjusted plus-minus? Well, Winston and I definitely agree we shouldn’t believe PER. But on the “true” value of Durant, I think Winston and I disagree (somewhat). At least, I am not convinced Durant’s supposed problems on defense trump his box score numbers.
Reviewing the Thunder
Let me close this post by noting that there is more to the Thunder than Durant. Here is the team’s potential depth chart (Wins Produced and WP48 numbers from 2008-09, unless noted otherwise):
Potential First String
PG: Russell Westbrook [3.7 Wins Produced, 0.066 WP48]
SG: James Harden [rookie]
SF: Kevin Durant [10.5 Wins Produced, 0.175 WP48]
PF: Jeff Green [1.8 Wins Produced, 0.031 WP48]
C: Nenad Krstic [-1.0 Wins Produced, -0.042 WP48]
Potential Second String
PG: Shaun Livingston [0.5 Wins Produced, 0.107 WP48; 0.111 WP48 in 2006-07]
SG: Kyle Weaver [2.4 Wins Produced, 0.097 WP48]
SF: Thabo Sefolosha [4.3 Wins Produced, 0.144 WP48]
PF: Nick Collison [5.8 Wins Produced, 0.151 WP48]
C: Etan Thomas [17.3 Wins Produced, 0.125 WP48 for career]
Again, the Wins Produced numbers say Durant is the team’s most productive player. And he is the only above average performer on the first string. Off the bench, though, Oklahoma City has four players who have been above average and one player that is quite close (average WP48 is 0.100). This suggests the Thunder’s rotation is backwards and wins are going to be left on the bench in 2009-10.
One should note, though, that however this team did its rotation, in the Western Conference the Thunder will probably miss the playoffs. But if the bench players saw the court more often, it’s possible the Thunder could come closer to the post-season. At least, that’s the Wins Produced story.
The WoW Journal Comments Policy
Our research on the NBA was summarized HERE.
Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:
Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.