Detecting injury risk factors with algorithmic models in elite women’s pathway cricket

Luke Goggins, Anna Warren, David Osguthorpe, Thamindu Wedatilake, Nicholas Peirce, Carly McKay, Keith Stokes, Sean Williams

Research output: Contribution to journalArticlepeer-review

Abstract

This exploratory retrospective cohort analysis aimed to explore how algorithmic models may be able to identify important risk factors that may otherwise not have been apparent. Their association with injury was then assessed with more conventional data models. Participants were players registered on the England and Wales Cricket Board women’s international development pathway (n=17) from April 2018 to August 2019 aged between 14–23 years (mean 18.2±1.9) at the start of the study period. Two supervised learning techniques (a decision tree and random forest with traditional and conditional algorithms) and generalised linear mixed effect models explored associations between risk factors and injury. The supervised learning models did not predict injury (decision tree and random forest area under the curve [AUC] of 0.66 and 0.72 for conditional algorithms) but did identify important risk factors. The best-fitting generalised linear mixed effect model for predicting injury (Akaike Information Criteria [AIC]=843.94, conditional r-squared=0.58) contained smoothed differential 7-day load (P<0.001), average broad jump scores (P<0.001) and 20 m speed (P<0.001). Algorithmic models identified novel injury risk factors in this population, which can guide practice and future confirmatory studies can now investigate.
Original languageEnglish
Pages (from-to)344-349
JournalInternational Journal of Sports Medicine
Volume43
Issue number4
Early online date24 Sep 2021
DOIs
Publication statusPublished - 30 Apr 2022

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