Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers

Injury management is critical in all sports, directly impacting player performance. Baseball players are particularly susceptible to injuries, as players often compete in 5 to 7 games per week, placing continuous strain on their bodies. Among various injuries, Tommy John Surgery (TJS) poses a notable risk for Major League Baseball (MLB) pitchers. Traditional TJS prediction methods required sensors or video-based motion capture, which are impractical during actual games and limited in making predictions too close to the injuries, such as within 30 pitches. To address these challenges, this study proposes a deep learning (DL) framework that utilizes both classification and regression tasks. Using MLB pitching data (2016–2023), the classification model detects injury risk up to 100 days in advance with a high prediction performance of 0.73 F1-score, while the regression model estimates the time remaining until the player’s last pre-surgery game with R² of 0.79. In addition, to enhance our model’s applicability, we employ an explainable artificial intelligence technique to analyze the impacting mechanical features, such as a lowered four-seam fastball release point, that accelerate UCL deterioration, increasing TJS risk. These findings provide a practical foundation for early intervention strategies, potentially preserving pitcher health and reducing the need for complex surgical procedures.