Machine Learning-Based Classification of Team Playoff Advancement Using Pitching Performance Metrics in Korean Professional Baseball

This study develops and evaluates machine learning models for classifying Korean Baseball Organization (KBO) playoff advancement using pitching metrics from 2015 to 2024 (N = 100 team-seasons), focusing specifically on pitching’s contribution to playoff qualification to address the ERA-FIP paradox at the team level. Five algorithms were compared: Random Forest, Support Vector Machines, Logistic Regression, Neural Networks, and Decision Trees. Independent variables included ten pitching statistics: Earned Run Average (ERA), Walks and Hits per Inning Pitched (WHIP), Fielding Independent Pitching (FIP), Strikeouts per 9 Innings (K/9), Walks per 9 Innings (BB/9), Strikeout-to-Walk Ratio (K/BB), Home Runs per 9 Innings (HR/9), batting average against (BAA), and opponent On-Base Percentage (OBP) and On-Base Plus Slugging (OPS). Logistic Regression achieved the highest classification performance with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.804 and classification accuracy of 73.0%, followed by Neural Network (AUC = 0.799, CA = 72.0). Feature importance analysis showed ERA and WHIP, both defense-dependent metrics, as the dominant predictors of postseason qualification, collectively accounting for 33.7% of information gain, while FIP ranks fifth, indicating that defense-dependent metrics are more informative for team success than defense-independent measures. The findings highlight the strategic importance of pitching–defense synergy, demonstrate the applicability of machine learning-based playoff classification beyond Major League Baseball, and provide empirical evidence that defense-dependent metrics (ERA, WHIP) exhibit superior discriminatory power compared to defense-independent metrics (FIP) for team playoff qualification. Findings reflect pitching’s contribution to playoff success; comprehensive models integrating hitting, defense, and managerial factors would provide more complete classification frameworks.