I saw this afternoon an interesting talk by Lawrence Brown on baseball statistics, as part of the UCI statistics seminar.

The title of the talk is "In-Season Prediction of Batting Averages: A Field-test of Simple Empirical Bayes and Bayes Methodologies". In simpler words, the problem Brown considers is the following: at a certain point in the season, your favorite player has accumulated a certain batting average (ratio of the number of hits he made to the number of times he was at bat). What should you predict as the probability of a hit for his next at-bat?

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Dominated Outs. Silent4 explains why an understanding of algorithm asymptotics can be important to poker players — the same key idea, analyzing things by focusing on the dominant term and ignoring the rest, applies in both areas.

More sports mathematics, via Kevin Drum: David Romer of Berkeley analyzes Football. Specifically, the decision about when to go for another play instead of punting or attempting a field goal on a fourth down. According to his math, one should go for it much more frequently than the coaches actually do. The chart on Drum's post describes a very simple deterministic algorithm: go for it if the yardage needed for the first down is below some crazy function of the field position. But presumably one could do a more tailored analysis for each game based on the offensive and defensive strengths of the two teams...

A great example from college sports of misleading statistics.

Apparently, especially athletic college freshmen are scored on a 10-point scale by their ability (where these numbers come from and whether they mean anything: unknown to me) and then colleges are ranked by comparing the vector of scores of their incoming frosh. But the comparison is done by the average of the nonzero scores, so a vector like (6,0,0,0,...) (average: 6) ranks better than a vector that completely dominates it like (9,9,8,6,4,2,2,1,1,1,1,1,1,1,1,1,1,1) (average: 2.83). And this actually happens and skews the rankings...