Strategy in baseball used to be a fairly straightforward matter. A few strategy rules – a right handed pitcher was more successful against a right handed batter, lefty against lefty, no left handers at infield positions except for first base, don’t hold the runner at first with two outs and a left handed batter, and sacrifice bunt to move a runner at first with less than two outs- were taken as gospel and practiced by the community.
It was baseball’s version of the 10 commandments, written in stone and for the first century of baseball, unchangeable.
The world changed, though few knew it, in 1946 when Cleveland manager Lou Boudreau moved his shortstop to the right of second base against the legendary dead pull hitter Ted Williams of the Boston Red Sox.
However, like many innovations, it is only with the advent of large data sets that the revolution that started that July day in Cleveland impacted day to day strategy.
A players position on the field is no longer the result of the manager’s intuitive hunch, or even the result of consulting a written document of the past several encounters between a particular pitcher and a particular batter- a scatter gram of where this batter is likely to hit the ball. Instead, major league teams are increasingly relying on sophisticated, large data sets that are housed on remote servers.
These data sets run complex algorithms predicting the best solution for a particular ecosystem- elements of which include – batter, pitcher and all the defensive players and their particular gifts, skills and tendencies- and even the weather and time of day.
