The Effects of Major League Baseball's Ban on Infield Shifts: A Quasi-Experimental Analysis

TL;DR

This paper analyzes the impact of Major League Baseball's 2023 shift ban on player performance using quasi-experimental methods, showing modest league-wide effects and significant individual improvements for some players.

Contribution

It demonstrates the application of difference-in-differences and synthetic control methods to assess rule change impacts in sports analytics, providing a framework for causal inference in this domain.

Findings

Shift ban increased batting average and on-base percentage for left-handed batters by nine points.

Several players experienced substantial offensive performance improvements (over 70 points).

The methods used are valuable for causal analysis in sports analytics.

Abstract

From 2020 to 2023, Major League Baseball changed rules affecting team composition, player positioning, and game time. Understanding the effects of these rules is crucial for leagues, teams, players, and other relevant parties to assess their impact and to advocate either for further changes or undoing previous ones. Panel data and quasi-experimental methods provide useful tools for causal inference in these settings. I demonstrate this potential by analyzing the effect of the 2023 shift ban at both the league-wide and player-specific levels. Using difference-in-differences analysis, I show that the policy increased batting average on balls in play and on-base percentage for left-handed batters by a modest amount (nine points). For individual players, synthetic control analyses identify several players whose offensive performance (on-base percentage, on-base plus slugging percentage, and weighted on-base average) improved substantially (over 70 points in several cases) because of the rule change, and other players with previously high shift rates for whom it had little effect. This article both estimates the impact of this specific rule change and demonstrates how these methods for causal inference are potentially valuable for sports analytics -- at the player, team, and league levels -- more broadly.