IN THE late 1990s Billy Beane, the general manager of the Oakland Athletics, a baseball club, made a radical change to the club’s recruiting methods: he decided to apply some science to them. Instead of relying on the instincts of scouts, he and his deputy Paul DePodesta, a statistically astute Harvard economics graduate, crunched candidate players’ numbers. Baseball is awash with such numbers, and the two men applied simple statistics to them to identify valuable players whom scouts had rejected, and who could thus be hired cheaply. The result transformed the club’s performance. Despite having one of the league’s lowest payrolls, it qualified for the post-season tournament run by Major League Baseball (MLB), the professional game’s organiser, for four years running.
This success, which was the subject of a book (and later a film) called “Moneyball”, caused others to copy the method. Before long, teams had extracted all the information they could from the game’s traditional statistics. To produce even more accurate predictions, they would need better data. And the means to gather those numbers have...
from The Economist: Science and technology http://ift.tt/1Alv6lC
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