In the fine tradition of economists testing utility maximisation using data from professional sports, Kovash and Levitt have a new working paper based on "high stakes, real world settings that are data rich: choice of pitch type in Major League Baseball and whether to run or pass in the National Football League."
We observe more than three million pitches in baseball and 125,000 play choices for football. We find systematic deviations from minimax play in both data sets. Pitchers appear to throw too many fastballs; football teams pass less than they should. In both sports, there is negative serial correlation in play calling. Back of the envelope calculations suggest that correcting these decision making errors could be worth as many as two additional victories a year to a Major League Baseball franchise, and more than a half win per season for a professional football team.
2 comments:
Is it really rational to be entirely unpredictable?
Does this not depend on whether there are costs to switching? I am right-footed so it is easier for me to kick the ball to the left (& more accurate). So while I may want to left my opponent guessing, I don't think it follows that I should kick randomly to the left or right.
In chess for example grandmasters will try surprise an opponent with an opening that they haven't used before (Fischer playing Alekhine's against Spasski for example) but mostly they use one's that they do know.
I think a conclusion along the lines of "These people know what they're doing and do not behave as von Neumann would predict" would be better than "We reckon von Neumann would win more games."
That's the main critique I have of Levitt's work:
Very inventive,
Clever econometrics.
Miss the feckin' point.
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