Publication Date
Spring 2026
Presentation Length
Poster/Gallery presentation
College
College of Sciences & Mathematics
Department
Math and Computer Science, Department of
Student Level
Undergraduate
Faculty Mentor
Will Best
Presentation Type
Poster
Summary
Ulnar Collateral Ligament (UCL) reconstruction, commonly referred to as Tommy John Surgery, has seen a significant rise among Major League Baseball (MLB) pitchers, prompting growing interest in identifying the mechanical and performance-based factors that contribute to injury risk. While previous studies have examined these relationships using traditional frequentist approaches separately, this study combines multiple different model techniques to present a broad framework for finding significant predictors of UCL Surgery. These models include Lasso and Ridge Regression, Principal Component Regression (PCR) , Partial Least Squares Regression (PLS) , Random Forest, Multiple Linear Regression, and a Bayesian Statistical Model. Using these models, our findings aim to refine injury prediction models and provide a more comprehensive statistical framework for understanding the mechanics underlying UCL injuries in professional baseball.
Recommended Citation
Pinter, Brady M., "BayesBall : A comprehensive framework for predicting UCL injury" (2026). SPARK Symposium Presentations. 1058.
https://repository.belmont.edu/spark_presentations/1058
