Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Burch V, Reuben F
Committee Member
Tian, Wenmeng (Meg)
Committee Member
Strawderman, Lesley
Date of Degree
5-1-2020
Document Type
Graduate Thesis - Open Access
Major
Industrial Engineering
Degree Name
Master of Science
College
James Worth Bagley College of Engineering
Department
Department of Industrial and Systems Engineering
Abstract
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. K-means 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 student-athlete, 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.
URI
https://hdl.handle.net/11668/16672
Recommended Citation
Shelly, Zachary, "Using k-means clustering to create training groups for elite football student athletes on the basis of game demands." (2020). Theses and Dissertations. 4895.
https://scholarsjunction.msstate.edu/td/4895