Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers
Abstract 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...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01138-1 |
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| author | Bosuk Kang Minsu Park Angel P. del Pobil Eunil Park |
| author_facet | Bosuk Kang Minsu Park Angel P. del Pobil Eunil Park |
| author_sort | Bosuk Kang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-b23e331cbd5440b5ab098a2025d81328 |
| institution | DOAJ |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-b23e331cbd5440b5ab098a2025d813282025-08-20T03:06:50ZengSpringerOpenJournal of Big Data2196-11152025-04-0112113010.1186/s40537-025-01138-1Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchersBosuk Kang0Minsu Park1Angel P. del Pobil2Eunil Park3Department of Semiconductor and Display Engineering, Sungkyunkwan UniversityDepartment of Applied Artificial Intelligence, Sungkyunkwan UniversityRobotic Intelligence Laboratory, Jaume I UniversityDepartment of Applied Artificial Intelligence, Sungkyunkwan UniversityAbstract 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.https://doi.org/10.1186/s40537-025-01138-1Injury predictionTommy John Surgery (TJS)BigdataBaseballDeep learning (DL)Classification |
| spellingShingle | Bosuk Kang Minsu Park Angel P. del Pobil Eunil Park Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers Journal of Big Data Injury prediction Tommy John Surgery (TJS) Bigdata Baseball Deep learning (DL) Classification |
| title | Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers |
| title_full | Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers |
| title_fullStr | Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers |
| title_full_unstemmed | Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers |
| title_short | Data-driven approaches for predicting Tommy John Surgery risk in major league baseball pitchers |
| title_sort | data driven approaches for predicting tommy john surgery risk in major league baseball pitchers |
| topic | Injury prediction Tommy John Surgery (TJS) Bigdata Baseball Deep learning (DL) Classification |
| url | https://doi.org/10.1186/s40537-025-01138-1 |
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