Predicting Field-Sport Distances Without Global Positioning Systems in Indoor Play: A Comparative Study of Machine-Learning Techniques
The results demonstrate varying levels of accuracy and precision across different sports, genders, and contexts (game vs practice), highlighting the importance of selecting the appropriate model for specific applications in optimizing team performance, injury prevention, and player conditioning.
Authors
- Alan Huebner (ORCID: https://orcid.org/0000-0003-2141-5793)
- John P. Wagle (ORCID: https://orcid.org/0000-0003-4508-4271)
- Jonathan D. Hauenstein (ORCID: https://orcid.org/0000-0002-9252-8210)
- Bruce A. Kay (ORCID: https://orcid.org/0000-0002-0902-0935)
- David M. Smiley (ORCID: https://orcid.org/0000-0002-8211-3783)
- Martine Labbé (ORCID: https://orcid.org/0000-0001-7471-2308)
- Jonathon R. Lever (ORCID: https://orcid.org/0009-0000-1167-7795)
- Casey J. Metoyer
- H. Bill
- Gabriel Tauro
- William Sovine
Institutions
- University of Notre Dame (US)
Publication Details
- Journal
- International Journal of Sports Physiology and Performance
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1123/ijspp.2024-0361
- Primary Topic
- Sports Performance and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00