When the Strike Zone Becomes Algorithmic: Umpire Judgment and Player Challenge Decisions under AI Review

2026-09-21

AI Summary

研究通过分析2015-2026年4,114,256个投球和2026年的8,447次挑战，探讨了AI审查系统如何影响裁判判决和球员行为。

Abstract

The Automated Ball-Strike challenge system that Major League Baseball adopted in 2026 offers a distinctive setting for studying human AI interaction in which umpires make every ball and strike call, while players can selectively ask an automated system to publicly overturn those decisions. We analyze 4,114,256 called pitches from 2015 through 2026 and 8,447 challenges from the 2026 season to examine how algorithmic review reshapes umpire judgment and player behavior. We study where umpires placed the effective strike zone boundary, how consistently they applied that boundary, how they responded to overturned calls, and which calls players chose to challenge. In 2026, the effective called boundary shifted toward the automated strike zone beyond the trajectory observed in prior seasons, while the consistency of that boundary largely continued its existing trend. Following an overturned call, umpires temporarily adjusted subsequent decisions near the corrected boundary, although these effects did not consistently persist into the next game. Count dependent variation in calling remained, while differences associated with player status narrowed. Players, meanwhile, left many overturnable calls unchallenged and appeared to base challenge decisions more strongly on immediately observable evidence than on the precise geometry of the automated zone. Together, these findings show that selective AI review does more than correct individual errors. It reshapes human judgment, adaptation, and strategic behavior around an algorithmic authority.

Problem

• Automated Ball-Strike
• umpire judgment
• player behavior
• algorithmic review

Innovation

• Automated Ball-Strike challenge system
• human AI interaction
• umpire judgment
• player behavior
• algorithmic authority