We develop matchup models for the probability of a ground ball and a ground ball hit using twelve years of major league baseball play-by-play data. The models are based on player descriptors that can be estimated reliably from small samples which facilitates the use of the models for prediction. The model for ground ball probability is obtained by generalizing the log5 model to include both ground ball and strikeout rates for the batter and pitcher. A strikeout rate cross term is shown to be significant in this model which leads to regions of the matchup space, termed matched and mismatched Krate configurations, where either the batter or pitcher is favored relative to the log5 prediction. We also build a model for the probability that a ground ball becomes a hit which separates the contributions of the batter, pitcher, and defense. We show that this probability has a strong dependence on the pitchers ground ball and strikeout rates and that the structure of this dependence changes with the platoon configuration. We give a physical justification for the model and provide examples of pitchers with characteristics that significantly lower or raise their expected ground ball hit rates. The new models for the probability of a ground ball and a ground ball hit are tested on out-of-sample data and shown to provide more accurate predictions than alternative models.
© Copyright 2017 Journal of Sports Analytics. IOS Press. All rights reserved.
|Subjects:||baseball modelling prognosis relation|
|Notations:||sport games technical and natural sciences|
|Published in:||Journal of Sports Analytics|