Predicting Ulnar Collateral Ligament Injury in Rookie Major League Baseball Pitchers
This addresses injury prediction for MLB teams and pitchers, but appears incremental as it applies existing methods to a specific sports domain.
The study tackled predicting ulnar collateral ligament injury in rookie Major League Baseball pitchers using machine learning on online pitcher data, but no concrete results or numbers were provided.
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.