Exploring Baseball Hitting Data: What about those Breakdown Statistics?

Abstract

During a broadcast of a baseball game, a fan hears how baseball hitters perform in various situations, such as at home and on the road, on grass and on turf, in clutch situations, and ahead and behind in the count. From this discussion by the media, fans get the misleading impression that much of the variability in players' hitting performance can be explained by one or more of these situational variables. For example, an announcer may state that a particular player struck out because he was behind in the count and was facing a left-handed pitcher. In baseball one can now investigate the effect of various situations, as hitting data is recorded in very fine detail. This article looks at the hitting performance of major league regulars during the 1992 baseball season to see which situational variables are “real” in the sense that they explain a significant amount of the variation in hitting of the group of players. Bayesian hierarchical models are used in measuring the size of a particular situational effect and in identifying players whose hitting performance is very different in a particular situation. Important situational variables are identified together with outstanding players who make the most of a given situation.

Key Words:

• Hierarchical modeling
• Outliers
• Situational variables