RT Journal Article
SR Electronic
T1 Improving predictor selection for injury modelling methods in male footballers
JF BMJ Open Sport & Exercise Medicine
JO BMJ OPEN SP EX MED
FD BMJ Publishing Group Ltd
SP e000634
DO 10.1136/bmjsem-2019-000634
VO 6
IS 1
A1 Fraser Philp
A1 Ahmad Al-shallawi
A1 Theocharis Kyriacou
A1 Dimitra Blana
A1 Anand Pandyan
YR 2020
UL http://bmjopensem.bmj.com/content/6/1/e000634.abstract
AB 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.