A Markov chain approach to optimal pinch hitting strategies in a designated hitter rule baseball game

Abstract

A baseball game between teams consisting of non-identical players is modeled using a Markov chain, taking into account the number of runs by which the home team leads. Using the Markov model the probability of winning in any state in the course of a game is calculated directly by solving a set of over one million simultaneous equations. This approach makes it possible to obtain the optimal pinch hitting strategy under the 'Designated Hitter' rule by applying dynamic programming to this model. We demonstrate this method using a match based on the line-ups of the Anaheim Angels and the Oakland Athletics in the American League of Major League Baseball. We show how this approach may help to determine when to use a pinch hitter and how much the probability of winning increases if the optimal strategy is followed.