A Bayesian Approach to Ulnar Collateral Ligament (UCL) Reconstruction

Publication Date

2025

Presentation Length

Poster/Gallery presentation

College

College of Sciences & Mathematics

Department

Math and Computer Science, Department of

Student Level

Undergraduate

Faculty Advisor

Will Best

SPARK Session

Data Science

Presentation Type

Poster

Summary

In the past 10 years there has been an alarming rise of MLB players who have had to receive ulnar collateral ligament (UCL) reconstruction, or Tommy John Surgery, to address and fix issues present with the elbow of pitchers after long, consistent use in competitive games. Research has shown that there are correlations present between player injury rates and consistent statistics of pitches thrown by those who have had to receive the surgery. Type of pitch thrown, the rate of pitching, and the spin of a baseball have all been found to have significant impact on shortening the career of a pitcher and expediting their downturn until surgery is required. Building off these previous findings, this project used a Bayesian approach to statistical modeling and analysis to develop a predictive model. It incorporates pitching data from players with and without UCL reconstruction to accurately forecast whether a player is at risk of requiring UCL reconstruction before showing signs of damage.

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