When athletes train while fatigued, they are at greater risk of injury, underperformance, and burnout. Therefore, identifying athlete fatigue prior to injury will significantly improve the performance of MIT sports teams. To evaluate training and fatigue patterns over time, the MIT Sports Performance Center (SPC) collects weekly performance data points on each athlete. However, despite consistent data collection, there is inconsistent communication of these metrics to coaches. To address this gap between data collection and decision making, I, along with SPC coaches, designed an API/dashboard that both consolidates data and calculates readiness scores for each player. This study aims to evaluate whether a readiness monitoring system influences coaching decisions and whether those decisions are associated with improved short term athlete recovery and participation outcomes.

I am excited to participate in SuperUROP to pursue a problem I wouldn’t normally be able to solve in coursework. As a student athlete, I have witnessed the vast uncertainty around injury prevention. As a mechanical engineer, I have gotten hands on experience creating and testing hypothesis. Through this SuperUROP, I am able to merge those two fields into something I am very passionate about.