Pitching strategy evaluation via stratified analysis using propensity score

Nakahara Hiroshi (), Takeda Kazuya () and Fujii Keisuke ()
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Nakahara Hiroshi: Graduate School of Informatics, Nagoya University, Nagoya, Japan
Takeda Kazuya: Graduate School of Informatics, Nagoya University, Nagoya, Japan
Fujii Keisuke: Graduate School of Informatics, Nagoya University, Nagoya, Japan

Journal of Quantitative Analysis in Sports, 2023, vol. 19, issue 2, 91-102

Abstract: Recent measurement technologies enable us to analyze baseball at higher levels of complexity. There are, however, still many unclear points around pitching strategy. There are two elements that make it difficult to measure the effect of a pitching strategy. First, most public datasets do not include location data where the catcher demands a ball, which is essential information to obtain the battery’s intent. Second, there are many confounders associated with pitching/batting results when evaluating pitching strategy. We here clarify the effect of pitching attempts to a specific location, e.g., inside or outside. We employ a causal inference framework called stratified analysis using a propensity score to evaluate the effects while removing the effect of confounding factors. We use a pitch-by-pitch dataset of Japanese professional baseball games held in 2014–2019, which includes location data where the catcher demands a ball. The results reveal that an outside pitching attempt is more effective than an inside one to minimize allowed run average. In addition, the stratified analysis shows that the outside pitching attempt is effective regardless of the magnitude of the estimated batter’s ability, and the proportion of pitched inside for pitcher/batter. Our analysis provides practical insights into selecting a pitching strategy to minimize allowed runs.

Keywords: baseball; causal inference; pitching strategy; propensity score (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/jqas-2021-0060

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