Analyzing the impact of the automatic ball strike system in professional baseball through a case study on KBO league data
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View abstract on PubMed
Summary
This summary is machine-generated.The Automated Ball-Strike (ABS) system, or robot umpires, were studied in the Korean Baseball Organization (KBO). Findings reveal differences in call tendencies between human umpires and ABS, impacting baseball strategy.
Area Of Science
- Sports Science
- Artificial Intelligence in Sports
- Baseball Analytics
Background
- Professional baseball is integrating new technologies like the Automated Ball-Strike (ABS) system.
- The Korean Baseball Organization (KBO) pioneered ABS implementation in its 2024 season.
- ABS utilizes machine learning, computer vision, and tracking for automated ball-strike calls.
Purpose Of The Study
- To compare human umpire and ABS decision tendencies within the strike zone's "gray zone."
- To investigate player adaptation to the ABS system.
- To assess the fairness, consistency, and strategic implications of ABS in professional baseball.
Main Methods
- Analysis of pitch data from 2515 KBO games across multiple seasons.
- Application of mathematical modeling to compare umpire and ABS call tendencies.
- Examination of four key research questions regarding ABS performance and impact.
Main Results
- Identified aggregate differences in ball-strike call tendencies between human umpires and the ABS system.
- Provided insights into player adaptation strategies in response to ABS implementation.
- Evaluated the fairness and consistency of the ABS system in officiating.
Conclusions
- Technological integration in sports officiating, specifically ABS, offers valuable lessons for baseball.
- The study provides a foundation for understanding the impact of AI in sports officiating.
- Findings are relevant for future implementations of automated officiating systems in professional sports.
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