Publication Date
Spring 2026
Presentation Length
30 minutes
College
College of Sciences & Mathematics
Department
Math and Computer Science, Department of
Student Level
Undergraduate
Faculty Mentor
Will Best
Presentation Type
Talk/Oral
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. and Best, Will Ph.D., "BayesBall : A comprehensive framework for predicting UCL injury" (2026). SPARK Symposium Presentations. 1057.
https://repository.belmont.edu/spark_presentations/1057
Included in
Applied Statistics Commons, Categorical Data Analysis Commons, Other Statistics and Probability Commons, Probability Commons, Statistical Methodology Commons, Statistical Models Commons
