This past weekend – in the words of Henry Abbott – Michael Lewis (author of Moneyball) brought the Moneyball story to the NBA. On the pages of New York Times Magazine, Lewis told the story of the No-Stats All-Star, Shane Battier.
As Lewis notes, “Here we have a basketball mystery: a player is widely regarded inside the N.B.A. as, at best, a replaceable cog in a machine driven by superstars. And yet every team he has ever played on has acquired some magical ability to win.”
Lewis contends that the traditional basketball metrics tell us that Battier is not an effective player. Yet Battier’s teams tend to win. To explain the magic that is Battier, Lewis turns to Daryl Morey (the young general manager of the Houston Rockets). Morey argues that by looking past the box score – to a player’s plus-minus – the value of Battier can be measured. And that measurement tells us Battier is a good player.
In sum, the traditional box score in the NBA is simply inadequate if we wish to measure a player’s value. Only the “advance” approach of plus-minus can tell us which players are “good” or “bad”.
Repeating the Story
The story Lewis tells is well-written and quite interesting. It’s also not entirely original.
Of interest to any fan of this blog will be Michael Lewis’ long New York Times Magazine article about Shane Battier, in which Battier becomes a jumping-off point for some discussion of analytic approaches to basketball. One thing about the article that bothered me probably had nothing to do with Lewis, but the piece has been given the headline “The No-Stats All-Star.” The implication being that statistics can’t measure Battier’s important contributions.
On the contrary, as Dave Berri observed in response to a similar claim back in November 2007 if you understand the statistics correctly they say Battier is very good. Stats say that Battier is an efficient scorer with his modest number of shots, and that his net possessions numbers resulting from steals and turnovers are very good. Battier also appears to be, as best as one can tell, an excellent on-the-ball perimeter defender. This last bit really is an aspect of the game that conventional statistics don’t do a good job of capturing, but certain statistical systems-including Wins Produced-indicate that Battier is a valuable player.
As Yglesias notes, the story told by Lewis has been told before. The original author was Jason Freidman, who wrote Rocket Science: Daryl Morey Brings Hard-Core Statistical Analysis to the NBA in October of 2007.
When Friedman’s story appeared I posted the following:
This post built upon these two stories:
Walking in Memphis Back to the Playoffs (September 19, 2007)
Walking in Memphis (April 5, 2007)
Each of these columns detailed Battier’s contribution to the Memphis Grizzlies and Houston Rockets. And those details – based solely on the NBA’s box score – revealed that Battier is indeed “good”.
The past few days a number of people have contacted me – via e-mail and the comments section in this forum – to comment on the Lewis story. What I said back in 2007, though, is essentially what I would say today. So my comment is going to consist of an updated version of my earlier post. After this post, though, I will link to a few more stories on Battier and Plus-Minus (and also offer a few more thoughts).
The Shane Battier Story Via the Standard Box Score View
Let’ start with a bit of background on Shane Battier. With Battier the Grizzlies averaged 48 wins from 2003-04 to 2005-06. After Battier departed, the Grizzlies became the worst team in the NBA. This suggests that Battier might have had some value.
But when we look at the box score data, it’s hard to find this value. At least, that’s what we are being told.
Let’s start with scoring, the box score statistic that is most frequently cited when discussing an NBA player. The average small forward will score 19.9 points per 48 minutes. The best Battier has ever done in his career is 17.4, and that was in his rookie season. For his career he only averages 14.5 points per 48 minutes. So Battier is a below average scorer. And for many, that makes him a below average player.
Of course there is more to the box score than just scoring. Let’s turn to a summary measure like NBA Efficiency. The average small forward will post a per 48 minutes NBA Efficiency mark of 20.3. For his career Battier’s per 48 minute mark is 16.9. So NBA Efficiency says Battier is below average also.
Okay, NBA Efficiency is too simple. Let’s look at John Hollinger’s Player Efficiency Rating, a more complicated measure of player performance. The average player has a PER mark of 15.0. For his career, though, Battier only posted a PER of 13.6. Again he is below average.
If this were all you looked at with respect to the box score, I guess you would have to conclude the box score data is pretty worthless. Clearly we need to go beyond the box score data to figure out a player’s value.
A Different Approach
Well, maybe not. Let’s take a different approach. What we could do is regress team wins on offensive and defensive efficiency. Such a regression tells us that 94% of wins are explained by a team’s efficiency marks. To put that in perspective, only about 90% of team wins in baseball are explained by runs scored and runs allowed. In other words, the link between the current stats and current wins is a bit stronger in basketball than it is in baseball.
In a moment I will return to the comparison between basketball and baseball. But for now, I want to note that from our analysis of the link between the efficiency metrics and wins we can derive the value – in wins – of each of the box score statistics. And those values are used to construct the two measures cited in The Wages of Wins – Wins Produced and Win Score.
What do we learn when we look at Battier’s Win Score? For the answer we turn to Table One.
Per 48 minutes the average small forward posts a Win Score of 7.3. Except for Battier’s rookie season he has bested this average his entire career. Let me repeat this point. Win Score, a measure based entirely on box score statistics, tells us that Battier is above average.
If we delve into the numbers we can see why. First of all, although Battier doesn’t shoot much, he is an efficient scorer. And the aforementioned regression is quite clear on this point. Shooting efficiency matters in the NBA. Or to put it another way, inefficient shooting definitely hurts a team’s chances to win (the valuation of shooting efficiency by NBA Efficiency and PER is a point I have made before in more detail).
Battier’s value, though, goes beyond shooting efficiency. When we look at steals and turnovers we see another area where Battier helps. For a typical small forward, if we subtract turnovers from steals we get -1.1. In other words, typically a player will commit more turnovers than he will get steals. Battier, though, is not a typical player. Steals minus turnovers for Battier in his career is 0.2. This is a 1.3 swing in possessions for Battier in his career. It’s important to note that Battier is not just a below average producer of points (in terms of totals, not efficiency) and a below average rebounder. But his ability to hit shots efficiently, generate steals, and avoid turnovers – all stats found in the box score – tell us that he is an above average player.
As we can see in Table One, the story we tell about Battier from the box score depends on how we view the data. When we rely on scoring – or scoring dominated metrics like NBA Efficiency and PER – we see a below average player. But when we consider Battier in terms of efficiency, we see a player that is above average and a key player in the success the Grizzlies had from 2003-04 to 2005-06 (and as Morey notes, the success of the Rockets today).
Back to Baseball
In closing this discussion of Battier I want to make two more comments about baseball data. As noted, the link between current stats and current wins is a bit stronger in basketball. It’s also the case that the box score statistics in basketball have a stronger predictive power than box score data from baseball. The year-to-year correlation in Win Score per minute in basketball is 0.82. In baseball the year-to-year correlation in a metric like OPS is only 0.57 (a similar story is told for linear weights).
Of course the box score data in basketball doesn’t capture all a player does on defense. But the same charge can be made against baseball data. On-base-percentage, slugging percentage, OPS, and linear weights don’t tell us anything about a baseball player’s defense. But these various baseball metrics still tell us a great deal about a baseball player’s value.
Summarizing the Story
So here’s the story I am telling (and it is the same story I told in 2007). Box score data in basketball is at least as good – and I think it’s better – than data in baseball. The problem is that box score data in basketball is not well understood. Too often the only stat people look at is scoring. And scoring, by itself, doesn’t explain much of wins.
Metrics like NBA Efficiency consider more statistics, but this measure is dominated by a player’s scoring. Inefficient scorers can increase their NBA Efficiency value by simply taking more shots. A similar story can be told about PER. When we look at the box score statistics via the measures, again we can be misled.
Faced with this problem we are told to ignore the box score statistics. But a simpler solution is to simply heed the lesson we have learned about wins and efficiency. It’s well understood that wins are determined by offensive and defensive efficiency. If we simply take this relationship and apply it to the analysis of individual players we can see that players like Battier are truly valuable. And we can see this in the very box score statistics reported in every newspaper.
More on Battier and Plus Minus
And that is where my original post ended. Although re-runs are easy, I feel compelled to say something new. That something new will begin with the words of Carl Bialik.
Bialik – the Numbers Guy at WSJ.com – recently offered two columns on both the Michael Lewis story and the plus-minus system.
Bialik interviewed Justin Kubatko and some economist from Southern Utah University (that would be me). Although we employ different methods, each of us told Bialik that the box score data does indicate that Battier is a good player.
Bialik also offered a few more details on plus-minus. Earlier in the month Mark Cuban – owner of the Dallas Mavericks – posted a plus-minus evaluation of a number of NBA players. Bialik sent these numbers to Roland Beech – of 82games.com – and received this reaction:
Beech told me he considered plus/minus ratings, as adjusted by regression analysis, “one of the most over-hyped player rating systems.” He pointed to the incongruous finding that Telfair is more valuable than Nowitzki, and blamed other questionable results – San Antonio Spurs point guard Tony Parker as a below-average player – on a paucity of data on top players’ performance without their star teammates. Beech also argued that there is too much noise in the system, because a player’s value is determined by coaching schemes, injuries and their assigned roles.
Let me note that the adjusted plus-minus evaluation of Sebastian Telfair does not tell us about the merits of the system. Good analysis begins with evidence and moves to a conclusion. In other words, we don’t start with a conclusion (Sebastian Telfair is not good) and move back to an evaluation of a model.
That being said, Beech does raise an important point. Adjusted plus-minus does have some serious shortcomings. Still, as Wayne Winston (the creator of this system) notes, the system can give us an evaluation of a player’s defense. So it can provide some insights.
About two years ago I made some effort to incorporate what plus-minus said about a player’s defense in calculating Wins Produced. Ty at Buck’s Diary has also made an effort to marry plus-minus analysis with a Wages of Wins metric (in Ty’s case, Win Score).
Such efforts are interesting. But they don’t appear to change the fundamental story told in The Wages of Wins. Although scorers are often celebrated by fans, the media, and the NBA, the non-scoring aspects of the game matter. And one can see this simply by looking at the entire box score and relating what is seen to wins. In sum, the NBA box score – like Shane Battier – is quite good (even if neither is very magical).
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.