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.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-5894 |
| Date | 01 May 2020 |
| Creators | Shelly, Zachary |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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