Analyzing the Impact of the Automatic Ball Strike System in Professional Baseball through a Case Study on KBO League Data

TL;DR

This study evaluates the effects of the Automated Ball-Strike system in the KBO League, analyzing decision tendencies, fairness, and strategic implications of robot umpires compared to human officials.

Contribution

It provides a comprehensive analysis of ABS implementation in professional baseball, highlighting differences in decision-making and strategic impacts using extensive game data.

Findings

ABS shows different decision patterns from human umpires.

Players adapt their strategies in response to ABS calls.

ABS demonstrates higher consistency and fairness.

Abstract

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.