The work of Justin Wolfers has been a frequent topic in this forum. Stacey offered two posts examining Wolfers study of point shaving in NCAA men’s basketball.
And I offered four posts examining the Joe Price–Justin Wolfers study of implicit bias in the NBA.
Each of these studies demonstrates an ability to uncover the unseen – and unexpected – in large data sets. Of course playing with data is not the only skill in Wolfers arsenal. He is also a very talented economist (which means he can do more than just play with spreadsheets). Yesterday he demonstrated his ability to use economics to solve problems with a wonderful op-ed in the New York Times.
In Blow the Whistle on Betting Scandals, Wolfers offers a unique perspective and solution to the negative impact gambling can have on sports. Basically Wolfers argues we need more gambling – with respect to certain aspects of sports – which will reduce gambling on other aspects of sports and hence reduce the likelihood of scandals. If that last sentence didn’t make sense – and I’m not sure it did– go read the column (or read Henry Abbot’s take on this at True Hoop).
Price-Wolfers Explains Win Score
And for more from Wolfers (or is it Joe Price?)…three weeks ago I posted on the Western Economic Association meetings, which has become the annual meeting place for sports economists from around the world. The first paper presented in our first session was the aforementioned Price-Wolfers study. My sense is that this paper has typically been presented in forums where the presenter is given more than 30 minutes to discuss their work. At the WEA, though, you only have 15-20 minutes to explain your paper, consequently Joe Price had to leave out some of the material that Wolfers or he would include in a longer discussion.
Fortunately, though, I was given a copy of their entire power point presentation, and hence I got to see the whole story that they normally tell. Part of this story is a new look at Win Score.
The Win Score formula is as follows:
Win Score = Points + Rebounds + Steals + ½Assists + ½Blocked Shots – Field Goal Attempts – Turnovers – ½Free Throw Attempts – ½Personal Fouls
In the Price-Wolfers power point presentation this basic model was presented as follows:
Points + Possession gained (rebounds, steals) – Possession lost (turnovers, field goal shots, ½ free throws) + ½ Offensive help (assists) + ½ Defensive help (blocks) – ½ Help opponent (fouls)
Let me write this out in words. Basically a player’s value is determined by points scored, possessions gained, possessions lost, help on offense, help on defense, and helping of opponents.
The Price-Wolfers explanation of Win Score seems a bit more intuitive than what we said in The Wages of Wins. As noted in the book, the Win Score model is designed to be a simple (and accurate) measure of player performance, much like OPS in baseball. The intuition of Price-Wolfers, I think, makes the Win Score model easier to understand.
One should note that Win Score was developed to ease research in economics that utilized performance data from the NBA. The Price-Wolfers study demonstrated this aspect of the model. Their work looked at the impact the race of the referee (and the player) had on player performance. Much has been made of the impact uncovered with respect to personal fouls. But Price-Wolfers found much more. This study also found that the race of the referee impacted other aspects of player performance like scoring and turnovers. Utilizing Win Score they were able to estimate the overall impact of a “race-normed refereeing crew.”
Quoting from the paper… consider a game involving five black starters against four blacks and one white. Thus any team-level differences will be driven by the differential treatment of the fifth player, who is black for the home team, and white for their rival. The coefficients in Table 4 suggest that race-norming the refereeing crew would lead the black player to commit around 0.1 fewer fouls per 48 minutes (relative to the change for the white player). Table 5 suggests that he would also score around 0.2 more points and earn 0.05 extra turnovers. Alternatively, using Berri, Schmidt and Brook’s (2006) “Win Score” metric, the black player’s overall contribution to the team winning margin will rise by about one-quarter of a point under a race-normed refereeing crew (relative to his white rival’s contribution). These individual-level estimates are consistent with the estimates of the “direct” effects measured in Table 6. But recall that Table 6 showed that these “direct” effects on fouls committed and points scored are roughly matched by an equal-sized (and opposite signed) “indirect” effect on fouls awarded, turnovers gained, and points conceded. That is, the away team’s boxscore statistics also change in a way that leads further extends the home team’s winning margin by another quarter point.
Much of the discussion of this paper focused solely on the impact on personal fouls. But the finding that players altered other aspects of performance was also quite interesting.
Smush Parker Signs with the Heat
Let me close today’s post with a quick comment on Smush Parker signing with the Miami Heat. This is how this signing was described by Pat Riley:
“We feel very good about acquiring Smush,” coach Pat Riley said in a statement. “He brings size, shooting and defense to our backcourt. He has been improving every year, and we feel that this could be his best year yet.”
When we look at Parker’s Win Score per minute, we see that he was at 0.140 in 2005-06 and at 0.090 in 2006-07. Average for a point guard is 0.128, so Parker went from being above average to below average. In other words, just looking at Win Score gives us the impression that Riley is a bit off in his assessment.
If we look deeper into the numbers, though, this signing may make some sense. Parker is above average with respect to turnovers (he commits fewer than an average point guard) and shooting efficiency. Where he declined in 2006-07 is with respect to rebounds and assists. If Riley can change those two aspects of Parker’s performance, he might have acquired an average point guard. And as we all know, average ain’t “bad.”
I bring up this story to emphasize a point made in The Wages of Wins about Win Score and Wins Produced. Looking at the numbers is not the end of analysis. It’s just the beginning. Coaches have to look into why a player achieved those particular numbers. In the case of Parker, the common problem for guards – turnovers and shooting efficiency – are not the problem. If Riley and his staff can fix the problem areas (rebounds and assists), it looks like this signing could pay off. Of course, if they can’t, then the Heat have simply acquired a below average point guard who is not going to help Alonzo Mourning get back to the NBA Finals.