Recent advancements in professional baseball have led to the introduction of the Automated Ball-Strike (ABS) system, or ``robot umpires,'' which utilize machine learning, computer vision, and precise tracking technologies to automate ball-strike calls. The Korean Baseball Organization (KBO) league became the first professional baseball league to implement ABS during the 2024 season. Leveraging pitch data from 2,515 KBO games across multiple seasons and employing mathematical modeling, we examine the aggregate decision tendencies of human umpires versus those of the ABS within the ``gray zone'' of the strike zone. We propose and answer four research questions to examine the differences between human and robot umpires, player adaptation to ABS, assess the ABS system's fairness and consistency, and analyze its strategic implications for the game. Our findings offer valuable insights into the impact of technological integration in sports officiating, providing lessons relevant to future implementations in professional baseball and beyond.

翻译：职业棒球领域的最新进展催生了自动好球判定系统的引入，即利用机器学习、计算机视觉与精密追踪技术实现好球判定自动化的"机器人裁判"。韩国职业棒球联盟在2024赛季率先成为实施该系统的职业棒球联赛。本研究基于多个赛季共2,515场KBO比赛的投球数据，运用数学模型分析了在好球区"灰色地带"内人类裁判与自动好球判定系统的整体判罚倾向差异。通过提出并解答四个研究问题，我们系统考察了人机裁判的判罚差异、运动员对系统的适应过程，评估了该系统的公平性与一致性，并剖析了其对比赛策略的影响。研究结果为技术融入体育裁判领域的影响提供了重要见解，对职业棒球乃至其他运动未来的技术应用具有参考价值。