发表机构
KAIST; Yonsei University（韩国科学技术院; 延世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用MLB 2026年自动挑战系统的海量数据，揭示选择性AI复核不仅纠正判罚错误，还促使裁判调整边界判断、球员基于可见证据决策，重塑了人机互动中的判断与策略行为。
AI 中文摘要
美国职业棒球大联盟于2026年采用的自动好球-坏球挑战系统，为研究人类与人工智能的互动提供了一个独特场景：裁判做出每一个好球和坏球的判罚，而球员可以选择性地要求自动系统公开推翻这些判罚。我们分析了2015年至2026年间的4,114,256个投球被判罚数据，以及2026赛季的8,447次挑战，以考察算法复核如何重塑裁判判罚和球员行为。我们研究了裁判将有效好球带边界置于何处、他们应用该边界的一致性如何、他们对被推翻判罚的反应，以及球员选择挑战哪些判罚。2026年，有效判罚边界相比前几个赛季的轨迹进一步向自动好球带偏移，而该边界的一致性大体延续了其现有趋势。在一次判罚被推翻后，裁判会在被纠正的边界附近临时调整随后的判罚，尽管这些影响并未持续到下一场比赛。与计数相关的判罚差异仍然存在，而与球员地位相关的差异则有所缩小。与此同时，球员对许多可被推翻的判罚未提出挑战，且其挑战决策似乎更多基于即时可见的证据，而非自动好球带的精确几何。这些发现共同表明，选择性AI复核不仅仅纠正个别错误，它还围绕算法权威重塑了人类的判断、适应和策略行为。
英文摘要
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.
Comments31 pages, 10 figures, 1 table
