The impact of the automated ball-strike system (ABS) on baseball: A deep learning based counterfactual approach

Abstract

This study investigated the impact of an automated ball-strike system (ABS) on baseball game dynamics and pitcher performance using a deep learning-based counterfactual approach. We developed deep neural network (DNN) models to predict game duration, walks and hits per inning pitched (WHIP), walks, and hits allowed using game-level data from the 2021–2023 Korea Baseball Organization seasons. Through a comparative analysis of three widely adopted machine learning algorithms, this study validated the suitability of DNN models for counterfactual evaluations of policy interventions in professional baseball. The model evaluation demonstrated high predictive accuracy, with mean absolute error values of 9.62 min for game duration, 0.205 for WHIP, 0.866 for walks, and 1.001 for hits allowed. Counterfactual predictions for the 2024 season, compared with observed outcomes, revealed statistically significant differences associated with ABS implementation. Game duration decreased, reflecting improved efficiency, whereas WHIP, walks, and hits allowed increased, suggesting a more challenging environment for pitchers. These findings highlight the dual impact of ABS: a shortened game duration and altered pitcher-batter dynamics. This study introduces a deep learning-based approach for evaluating policy changes in professional sports, demonstrating its utility in quantifying the effects of ABS technology on game outcomes. The results emphasize the need for further research on the long-term implications of ABS and its potential influence on strategic decision-making in baseball.