Machine learning modeling of hit-into-play probability and quantification of pitch sequence effects

Abstract

Although pitch sequencing is a central topic in baseball analytics, previous studies have primarily focused on improving predictive performance for the final pitch or its outcome, leaving the role of preceding pitches insufficiently examined. To address this issue, this study conducted a machine learning-based analysis to quantify the importance of preceding pitches and deepen the tactical understanding of baseball. A Transformer-based model was designed and trained to predict whether a target pitch would result in an in-play outcome or a swinging strikeout. Based on the model, two analyses were conducted, including an ablation study to evaluate the contribution of preceding pitches to predictive performance and a replacement analysis to quantify changes in predicted in-play probability when individual pitches were replaced by alternatives. The results suggest that hitting outcome predictions mostly depended on the attributes of the final pitch, and including preceding pitches had little effect on the predictive performance of the model. Moreover, the effect of pitch replacement on the probability output of the model for preceding pitches ranged from approximately 0.11 to 0.14, compared with approximately 0.44 to 0.45 for the final pitch. Therefore, preceding pitches played only a supplementary role in slightly modifying the predicted probability of a ball being put into play, although even such small effects may be practically meaningful.