Philp, Fraser Derek ORCID: https://orcid.org/0000-0002-8552-7869, Al-shallawi, A, Kyriacou, T, Blana, D and Pandyan, A ORCID: https://orcid.org/0000-0002-2180-197X (2020) Improving predictor selection for injury modelling methods in male footballers. BMJ Open Sport and Exercise Medicine, 6 (1). e000634 - e000634.

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Abstract

<jats:sec><jats:title>Objectives</jats:title><jats:p>This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team’s 2015/2016 season.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of ‘no-play’ and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.</jats:p></jats:sec>

Item Type: Article
Additional Information: © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Uncontrolled Keywords: football, footballers, injury, predictor
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
Q Science > Q Science (General)
Q Science > QM Human anatomy
Q Science > QP Physiology
R Medicine > RA Public aspects of medicine
Divisions: Faculty of Medicine and Health Sciences > School of Allied Health Professions
Depositing User: Symplectic
Date Deposited: 16 Jan 2020 16:32
Last Modified: 16 Jan 2020 16:32
URI: https://eprints.keele.ac.uk/id/eprint/7520

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