The cause of sport injuries are multifactorial and necessitate sophisticated statistical
approaches for accurate identification of risk factors predisposing athletes to injury. Pattern
recognition analyses have been adopted across sporting disciplines due to their ability to
account for repeated measures and non-linear interactions of datasets, however there are
limited examples of their use in injury risk prediction. This study incorporated two-years of
rigorous monitoring of athletes with 1740 individual weekly data points across domains of
training load, performance testing, musculoskeletal screening, and injury history parameters, to be one of the first to employ a pattern recognition approach to predict the risk factors
of specific non-contact lower limb injuries in Rugby Union. Predictive models (injured vs.
non-injured) were generated for non-contact lower limb, non-contact ankle, and severe noncontact injuries using Bayesian pattern recognition from a pool of 36 Senior Academy
Rugby Union athletes
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