  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. This study analyzes pitching data for 2,515 games across multiple KBO
seasons to compare decisions made by human umpires with those made by ABS,
focusing specifically on differences 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.
