Machine learning modeling of hit-into-play probability and quantification of pitch sequence effects
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 pr...
| Authors | Ryota Takamido, Hiroki Nakamoto |
|---|---|
| Primary category | cs.LG |
| Published | 15 June 2026 |
| Last updated at source | 5 October 2026 |
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