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Activity Number:
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339
- Novel Applications of Statistics in Sports
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Sports
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Abstract #322716
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Title:
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Assessing Injury Risk of UCONN Athletes Using Multi-Dimensional Statistical Classification Models
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Author(s):
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Morris Morgan* and Catie Dann and Yannis K Halkiadakis and Noah Davidson
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Companies:
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Mecklimited.LLC and University of Connecticut and College of Engineering University of Connecticut and College of Engineering University of Connecticut
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Keywords:
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Classification;
sport injuries;
regression;
muscle groups;
metric;
predictors
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Abstract:
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This statistical study is concerned with developing robust classification schemes that will predict injury risk in athletes. Athletes from four different University of Connecticut Division 1 varsity programs (Men’s & Women’s Basketball and Women’s Soccer and Lacrosse) ranging in age from 18-23 years old performed a battery of preseason muscle function assessment tests on the Cybex machine. We highlight how certain derived variables are excellent predictors for assessing such injury risks that are linked to specific muscle groups imbalances. The result is a visual representation of the muscle group data that will provide new ways to help classify and separate the athletes into high and low risk injury groups.
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Authors who are presenting talks have a * after their name.
