Can a Draft Regression Outpredict NBA Experts?—A Commentary by Ian Ayres ’86
The following commentary was published in The New York Times on July 1, 2008.
Can a Draft Regression Outpredict NBA Experts?
By Ian Ayres ’86
The NBA draft this year provides a vivid real world test of whether very simple regressions can out-predict experts on a central business decision — the NBA draft.
Chris Doughty, a 2008 industrial and operations engineering graduate of the University of Michigan, pointed me to a cool regression analysis of the great John Hollinger. Using data from current NBA players, Hollinger sees how well the players’ college stats explain their subsequent performance in the NBA.
For example, here’s his analysis of the 2006 draft ranked in descending order of predicted play:
Hollinger’s regression analysis suggests that Rudy Gay, Marcus Williams, and newly minted NBA champion Rajon Rondo should have been the top three picks — where as the actual top three were Adam Morrison (who Hollinger rates as 14th best player), Brandon Roy, and Randy Foye.
Comparing the NBA success of his system’s predictions to the actual draft, Hollinger concludes, “while the system isn’t perfect, it’s a clear improvement on what actually took place.” Even armed with this statistical analysis, many teams continue to go with their gut.
But Hollinger goes further and applies his regression results to the current crop of players in the draft. Here are the regression’s rankings for this year’s draft:
A natural experiment is for us to wait a few years and see whether (once again?) the equation beats the expert.
It’s particularly interesting to see what happens to players like Darrell Arthur who had very different Hollinger and actual draft ratings. Hollinger ranked Arthur 3rd in projected PER (player efficiency rating) but Arthur was taken 27th. (Donte Greene, Kosta Koufus, Roy Hibbert, and Marreese Speights were also undervalued by the regression’s lights).
Instead of predicting the 3rd year PER, I’d also be interested in seeing how college stats predict the amount the player gets paid in his second NBA contract.
Regressions can also help assess which humans are the best prognosticators. Hollinger could also add the predictions of various long-time scouts into his regression and see which scouts best predict future NBA success of draft prospects.