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Activity Number: 184
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Sports
Abstract #315705 View Presentation
Title: Predicting Injury Risk in College Athletes
Author(s): Sameer K. Deshpande* and Nicholas Potter and Shane T. Jensen and Daniel McCarthy and Katherine Heller
Companies: University of Pennsylvania and Duke University and University of Pennsylvania and University of Pennsylvania and Duke University
Keywords: Logistic regression ; Hierarchical modeling ; Injury risk
Abstract:

We propose models to predict the probability of injury among college athletes using several years' worth of data from many Duke Univesity athletic teams. Our dataset contains pre-season measurements from a range of biomechanical, functional assessment, and physical tests that indicate whether an athlete displays irregular or inefficient movements that may lead to injury. Our first goal is to determine if any of these, along with previous injury history, are predictive of incidence of different types of in-season injuries using regularized logistic regression. Rather than modeling each distinct injury separately, we use a Bayesian hierarchical model to "borrow strength"' across the different types of injuries and sports in our dataset.


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