Tag Archives: TrueHoop

The NBA, referees, Malcolm Gladwell, and race

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Henry Abbott at Truehoop reexamines an issue that emerged a few years ago with a paper written by several economists: do NBA officials exhibit implicit race bias when calling fouls? Here is Abbott’s take on the findings and implications of the original study:

Basically, the more black referees on the court, the better the calls for black players. And the reverse is true for white players. The entire combined effect is fairly limited, around 4 percent, but the pattern is certainly there.

All of this means not all that much about NBA referees, other than that they’re human. The research was about human decision making in the workplace, and the referees were just a handy group to study.

And nothing about these findings do much to undermine the NBA’s position as one of the most successfully race-blind organizations on the planet.

Abbott writes that the NBA essentially lost the scientific battle as experts pored over the economists’ paper as well as the NBA’s study and found the NBA’s study to be lacking. (It is also interesting to note that the economists made all of their data available online, making it open for scrutiny from others.)

Malcolm Gladwell enters the picture because of his book Blink where he looks at how people make quick decisions. In instances where race matters, such as calling fouls or making a decision about whether a suspect is about to pull a gun, a person making a decision nearly instantaneously makes judgments based on knowledge or associations they make about different races. Abbott sums up this research on race and judgments (read more about it at the Project Implicit website):

The lesson Gladwell, Winfrey, Harvard researchers and others took from this was about environment: We may have reached a point where a lot of explicit racism (the kinds of things we’d associate with hate speech, the klan, segregation and the like) is largely behind us. But our brains are still bombarded with images of “bad” black people and “good” white ones, which affects our quick reactions to white and black faces.

More broadly, this lines up with sociological thinkers who have suggested that in recent decades, racism and discrimination has become less overt and more covert. But just because racism appears less present doesn’t mean that the problem has been solved or that we have entered into a color-blind world. Gladwell and others suggest that it is even built into our snap judgments.

As Abbott suggests, how the NBA responds to this remains to be seen. The initial response of strongly denying the economists’ research appears no longer tenable. For a league that aspires to become global (involving even more ethnicities and races) and also wants to gain a larger audience in America (fighting football and baseball as the big sports), recognizing that this issue exists and also demonstrating a willingness to work at reducing the effect may matter quite a bit.

The presence of error in statistics as illustrated by basketball predictions

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TrueHoop has an interesting paragraph from this afternoon illustrating how there is always error in even complicated statistical models:

A Laker fan wrings his hands over the fact that advanced stats prefer the Heat and LeBron James to the Lakers and Kobe Bryant. It’s pitched as an intuition vs. machine debate, but I don’t see the stats movement that way at all. Instead, I think everyone agrees the only contest that matters takes place in June. In the meantime, the question is, in clumsily predicting what will happen then (and stats or no, all such predictions are clumsy) do you want to use all of the best available information, or not? That’s the debate about stats in the NBA, if there still is one.

By suggesting that predictions are clumsy, Abbott is highlighting an important fact about statistics and statistical analysis: there is always some room for error. Even with the best statistical models, there is always a chance that a different outcome could result. There are anomalies that pop up, such as a player who has an unexpected breakout year or a young star who suffers an unfortunate injury early in the season. Or perhaps an issue like “chemistry,” something that I imagine is difficult to model, plays a role. The better the model, meaning the better the input data and the better the statistical techniques, the more accurate the predictions.

But in the short term, there are plenty of analysts (and fans) who want some way to think about the outcome of the 2010-2011 NBA season. Some predictions are simply made on intuition and basketball knowledge. Other predictions are made based on some statistical model. But all of these predictions will serve as talking points during the NBA season to help provide some overarching framework to understand the game by game results. Ultimately, as Gregg Easterbrook has pointed out in his TMQ column during the NFL off-season, many of the predictions are wrong – though the makers of the predictions are not often punished for poor results.