Pitching strategy evaluation via stratified analysis using propensity score
Hiroshi Nakahara, Kazuya Takeda, Keisuke Fujii

TL;DR
This paper uses stratified analysis with propensity scores to evaluate the effect of pitching location in baseball, revealing outside pitches are generally more effective in minimizing runs, accounting for confounders.
Contribution
It introduces a causal inference framework using stratified propensity score analysis to assess pitching strategies with high-resolution location data.
Findings
Outside pitches reduce allowed runs more effectively.
Outside pitching is consistently advantageous regardless of batter ability.
Stratified analysis accounts for confounders in pitching strategy evaluation.
Abstract
Recent measurement technologies enable us to analyze baseball at higher levels. There are, however, still many unclear points around the pitching strategy. The two elements make it difficult to measure the effect of 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 disturbing factors. We used a pitch-by-pitch dataset of Japanese professional baseball games held in 2014-2019, which includes location data where the catcher…
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Taxonomy
TopicsSports Analytics and Performance · Sports Dynamics and Biomechanics
