While Moneyball is not writer Michael Lewis’s best book (that honor goes to Liar’s Poker, followed closely by the underappreciated Trail Fever), it certainly has been the Lewis book that has stimulated the most discussion. The gist: baseball manager Billy Bean analyzed oodles of (under-used) statistical data to discover winning could be had more cheaply than other managers thought. Specifically, such things as hitters with high on-base percentage and pitchers who get lots of ground outs were under-valued, at least with respect to said players’ relative compensation.
In this data-drenched age, that sort of things gets people thinking about data-mining. Where else, as the folks at Marginal Revolution ask today, might you apply Moneyball-style spelunking tactics?
Folks might have a look at a book about … jai-alai. Two years before Lewis’s Moneyball came out there was a wildly quirky book from a Computer Science professor at Stony Brook, New York, who wrote about how in one year of betting jai-alai he had increased his initial stake by 500%. How? By simply doing a better job of analyzing historical data. It is fascinating, and what’s more, for folks interested in trying that sort of data spelunking themselves — whether in the stock market or in sports — it is much more applicable, for obvious reasons, than Lewis’s book.