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Activity Number: 243 - Statistics in Sports and Beyond
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Sports
Abstract #318788
Title: Predicting Sports Readiness and Injury Risk of UCONN Athletes Using Novel Statistical Metrics
Author(s): Morris H Morgan* and Catie Dann and Yannis K Halkiadakis and Kristin D Morgan
Companies: Meck Limited, LLC and UCONN Athletic and University of Connecticut and University of Connecticut
Keywords: Sports ; classification; regression; logistics; metrics; athletes

The University of Connecticut-Storrs (UCONN) athletes as part of a comprehensive strength and conditioning program are run through a battery of physical test to assess their readiness for sport participation. The present study addresses athletes from four varsity level programs (Division 1- Men & Women Basketball and Women’s Lacrosse and Soccer) who range in age from 17-24 years old. Prior to commencing team play these athletes undergo a sport specific pre-season training regimen. After pre-season conditioning programs, the aforementioned test battery is performed with the intent of developing quantitative metrics for assessing athletes readiness for immediate sport participation. Here our overarching goal is to identify a class of metrics that can enhance current in-house evaluative tools for assessing an injured athlete’s readiness for a return to sport activity. Additionally, it is our desire to develop robust statistical regression-based models that characterize sport readiness and isolates useful classification metrics that predicts the potential injury risk an athlete may face if certain quantitative performance levels are not met. This work is significant because it exploits the use of low-cost exercise equipment available to most college and university athletic programs for this purpose.

Authors who are presenting talks have a * after their name.

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