Applications of Discrete Markov Chains to Predicting Baseball Game Outcomes

Abstract

There are two fundamental notions which justify the use of Markov chains in the analysis of baseball game outcomes. The first is simply a statement of the consequences of the rules of Baseball itself-- namely that for any batter, exactly one of three possible outcomes will have occurred by the time their turn at bat is finished. They will have scored a run, or they will find themselves on base, or they will be “out”. As a consequence, the evolving state of a game of baseball can be entirely characterized in terms of batters in sequence moving from their turn at
bat into the appropriate variation of one of these three foundational “states”. In particular, from the beginning of a half-inning to its conclusion, every single possible configuration of bases occupied, number of outs, and number of points scored can therefore be arranged sequentially and, as I will demonstrate, the discrete Markov chain is the perhaps the most natural framework in which to do this.

Discipline: Mathematics

Faculty Mentor: Dr. Cristina Anton