Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players
- Submitting institution
-
University of Gloucestershire
- Unit of assessment
- 24 - Sport and Exercise Sciences, Leisure and Tourism
- Output identifier
- 494
- Type
- D - Journal article
- DOI
-
10.1016/j.jsams.2020.04.021
- Title of journal
- Journal of Science and Medicine in Sport
- Article number
- -
- First page
- 1044
- Volume
- 23
- Issue
- 11
- ISSN
- 1440-2440
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2020
- URL
-
http://eprints.glos.ac.uk/8341/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- -
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
