A Prediction Model of Runs Allowed Based on Latent-Class Markov Chain for Starters of Professional Baseball Pitchers

抄録

In professional baseball games, the timing of changing the starting pitcher is an important factor in winning a game. The exploitation of various sources of data was recently enabled. Thus, it is desirable to develop an effective analytical model for team management that learns from past data. In this study, we propose a statistical model to estimate the expected runs for each inning and support decision-making on changing starting pitchers. Specifically, we developed a Markov chain model with a transition probability matrix for states defined by the combination of out counts and occupation of the bases. Note that the transition probability between the pitcher and batter can vary depending on the match. However, even if the transition probabilities are estimated for each match, the number of combinations becomes very high, and the estimation accuracy is reduced. Therefore, we introduce a latent-class model to estimate the transition probabilities while grouping the combinations into a small number of latent variables. Using the proposed model, it is possible to estimate the expected runs accurately by using the transition probabilities estimated for each latent class. To verify the effectiveness of the proposed model, we conducted experiments using actual Japanese professional baseball data.