Dissertation/Thesis Abstract

Using K-Means Clustering to Create Training Groups for Elite Football Student Athletes on the Basis of Game Demands
by Shelly, Zachary, M.S., Mississippi State University, 2020, 56; 27834087
Abstract (Summary)

Wearable tech has become increasingly popular with elite level sports organizations. The limiting factor to the value of the wearables is the use cases for the data they provide. This study introduces a technique to be used in tandem with this data to better inform training decisions. Kmeans clustering was used to group athletes from two seasons worth of data from an NCAA Division 1 American Football team. This data provided average game demands of each studentathlete, which was then used to create training groups. The resultant groupings showed results that were similar to traditional groupings used for training in American football, thus validating the results, while also offering insights on individuals that may need to consider training in a non-traditional group. In conclusion, this technique can be brought to athletic training and be useful in any organization that is dealing with training multitudes of athletes.

Indexing (document details)
Advisor: Burch, Reuben F
Commitee: Tian, Wenmeng, Strawderman, Lesley
School: Mississippi State University
Department: Industrial and Systems Engineering
School Location: United States -- Mississippi
Source: MAI 81/11(E), Masters Abstracts International
Subjects: Systems science
Keywords: Clustering, Competitive athlete, Industrial athlete, Sports science, Strength and conditioning, Training
Publication Number: 27834087
ISBN: 9798643196044
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