A recent study from the University of Michigan analyzed data from the Korean Baseball Organization (KBO) before and after the introduction of the Automated Ball-Strike (ABS) system. The findings revealed that star hitters performed significantly worse under the ABS, with fewer walks and more strikeouts compared to average hitters. Specifically, a star hitter struck out nearly three more times and walked nearly two fewer times per 100 at-bats than an average player.
This shift is attributed to the elimination of human bias in umpiring, as noted by the study's lead author, Song Ji-min. Prior to the ABS, umpires may have favored well-known players during close calls due to unconscious bias. The KBO plans to implement the ABS fully in 2024, utilizing camera and pitch-tracking technology to determine ball placement, thereby neutralizing the star effect.
Interestingly, the study found that high-level pitchers did not exhibit the same decline in performance. Researchers speculate this may be due to fewer measurable opportunities for pitchers to show a similar drop. The implications of this study extend beyond baseball, suggesting that automated evaluation systems could reduce bias in various fields, such as hiring and performance assessments, where high-status individuals often receive unintentional advantages.
Editor's Note
The introduction of automated systems in sports like baseball highlights a significant shift in how performance is evaluated. By removing human biases, organizations can ensure a fairer assessment of talent, which may influence hiring and evaluation practices across various industries. The KBO's approach could serve as a model for other leagues, including Major League Baseball, which is still grappling with the integration of automation in officiating.
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