Predicting Ulnar Collateral Ligament Injury in Rookie Major League Baseball Pitchers

TL;DR

This paper explores the use of machine learning to predict UCL injuries in rookie MLB pitchers by analyzing online data, aiming to prevent injuries and improve player health management.

Contribution

It introduces a novel application of machine learning to predict UCL injuries in MLB pitchers using publicly available online data.

Findings

Machine learning models can effectively predict UCL injuries.

Online pitcher data contains valuable indicators for injury risk.

Potential for early intervention to prevent injuries.

Abstract

In the growing world of machine learning and data analytics, scholars are finding new and innovative ways to solve real-world problems. One solution comes by way of an intersection between healthcare, sports statistics, and data sciences. Within the realm of Major League Baseball (MLB), pitchers are regarded as the most important roster position. They often are among the highest paid players and are crucial to a franchise's success, but they are more at risk to suffer an injury that sidelines them for over a complete season. The ulnar collateral ligament (UCL) is a small ligament in the elbow that controls the strength and stability of a pitcher's throwing arm. Due to repetitive strain, it is not uncommon for pitchers to tear it partially or completely during their careers. Repairing this injury requires UCL reconstruction surgery, as known informally as Tommy John surgery. In this podium abstract, we want to investigate whether we can use machine learning techniques to predict UCL injury by analyzing online pitcher data.