Article Text

Download PDFPDF

Improving predictor selection for injury modelling methods in male footballers
  1. Fraser Philp1,
  2. Ahmad Al-shallawi2,3,
  3. Theocharis Kyriacou4,
  4. Dimitra Blana2,
  5. Anand Pandyan1
  1. 1School of Health and Rehabilitation, Keele University, Keele, Staffordhire, UK
  2. 2Institute of Science and Technology in Medicine, Keele University, Keele, Staffordshire, UK
  3. 3The Engineering Technical College of Mosul, Northern Technical University, Mosul, Nineveh, Iraq
  4. 4School of Computing and Mathematics, Keele University, Keele, Staffordshire, UK
  1. Correspondence to Dr Fraser Philp; f.d.philp{at}


Objectives 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.

Methods 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.

Results 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.

Conclusion 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.

  • soccer
  • injuries
  • validation
  • statistics
  • football

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:

View Full Text

Statistics from


  • Twitter @fdphilp

  • Contributors All authors in this study have been involved in the planning, conduct and reporting of the work described in the article. All authors have seen an approved the final draft of this article.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient and public involvement statement This project did not receive patient or public involvement.

  • Patient consent for publication Obtained.

  • Ethics approval Keele University Ethical Review Panel (ERP1237).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available on reasonable request. Data are available in a public, open access repository—Code (R) (

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.