Computing an Optimal Pitching Strategy in a Baseball At-Bat. (arXiv:2110.04321v1 [cs.GT])

The field of quantitative analytics has transformed the world of sports over
the last decade. To date, these analytic approaches are statistical at their
core, characterizing what is and what was, while using this information to
drive decisions about what to do in the future. However, as we often view team
sports, such as soccer, hockey, and baseball, as pairwise win-lose encounters,
it seems natural to model these as zero-sum games. We propose such a model for
one important class of sports encounters: a baseball at-bat, which is a matchup
between a pitcher and a batter. Specifically, we propose a novel model of this
encounter as a zero-sum stochastic game, in which the goal of the batter is to
get on base, an outcome the pitcher aims to prevent. The value of this game is
the on-base percentage (i.e., the probability that the batter gets on base). In
principle, this stochastic game can be solved using classical approaches. The
main technical challenges lie in predicting the distribution of pitch locations
as a function of pitcher intention, predicting the distribution of outcomes if
the batter decides to swing at a pitch, and characterizing the level of
patience of a particular batter. We address these challenges by proposing novel
pitcher and batter representations as well as a novel deep neural network
architecture for outcome prediction. Our experiments using Kaggle data from the
2015 to 2018 Major League Baseball seasons demonstrate the efficacy of the
proposed approach.



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