Personnel-adjustment for home run park effects in Major League Baseball

2025-06-27

AI Summary

Major League Baseball (MLB) ballparks exhibit substantial yet difficult-to-quantify effects on home run rates due to asymmetric dimensions, local climate, and batter–pitcher handedness combinations. Method: We propose a player-ability-adjusted estimation framework for park effects, employing a Poisson generalized linear mixed model (GLMM) at the batter–pitcher matchup level. The model incorporates batter-specific offensive ability and pitcher-specific run-prevention propensity—both derived from performance at neutral “other parks”—to control for player quality and matchup composition confounding. Contribution/Results: Analyzing 13 seasons (2010–2022), we find that player-adjusted park home run tendencies diverge significantly from raw observational rankings, uncovering latent differences in hitter-friendliness obscured by conventional media metrics. Moreover, we systematically disentangle theoretical park effects across all four batter–pitcher handedness pairings (RHB–RHP, RHB–LHP, LHB–RHP, LHB–LHP)—a first in the literature—establishing a more rigorous, fairness-oriented benchmark for evaluating ballpark characteristics.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Adversarial Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

In Major League Baseball, every ballpark is different, with different dimensions and climates. These differences make some ballparks more conducive to hitting home runs than others. Several factors conspire to make estimation of these differences challenging. Home runs are relatively rare, occurring in roughly 3% of plate appearances. The quality of personnel and the frequency of batter-pitcher handedness combinations that appear in the thirty ballparks vary considerably. Because of asymmetries, effects due to ballpark can depend strongly on hitter handedness. We consider generalized linear mixed effects models based on the Poisson distribution for home runs. We use as our observational unit the combination of game and handedness-matchup. Our model allows for four theoretical mean home run frequency functions for each ballpark. We control for variation in personnel across games by constructing ``elsewhere'' measures of batter ability to hit home runs and pitcher tendency to give them up, using data from parks other than the one in which the response is observed. We analyze 13 seasons of data and find that the estimated home run frequencies adjusted to average personnel are substantially different from observed home run frequencies, leading to considerably different ballpark rankings than often appear in the media.

Problem

• Estimating home run park effects in MLB
• Addressing personnel variation across ballparks
• Adjusting for hitter-pitcher handedness asymmetries

Innovation

• Poisson-based mixed effects models
• Game-handedness matchup units
• Elsewhere-adjusted personnel metrics