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    Predictive Modeling of Hamstring Strain Injuries in Elite Australian Footballers

    Ruddy, Joshua D and Shield, Anthony J. and Maniar, Nirav and Williams, Morgan D. and Duhig, Steven J. and Timmins, Ryan G. and Hickey, Jack and Bourne, Matthew N. and Opar, David A. (2018) Predictive Modeling of Hamstring Strain Injuries in Elite Australian Footballers. Medicine & Science in Sports & Exercise, 50 (5). pp. 906-914. ISSN 0195-9131

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    Purpose Three of the most commonly identified hamstring strain injury (HSI) risk factors are age, previous HSI, and low levels of eccentric hamstring strength. However, no study has investigated the ability of these risk factors to predict the incidence of HSI in elite Australian footballers. Accordingly, the purpose of this prospective cohort study was to investigate the predictive ability of HSI risk factors using machine learning techniques. Methods Eccentric hamstring strength, demographic and injury history data were collected at the start of preseason for 186 and 176 elite Australian footballers in 2013 and 2015, respectively. Any prospectively occurring HSI were reported to the research team. Using various machine learning techniques, predictive models were built for 2013 and 2015 within-year HSI prediction and between-year HSI prediction (2013 to 2015). The calculated probabilities of HSI were compared with the injury outcomes and area under the curve (AUC) was determined and used to assess the predictive performance of each model. Results The minimum, maximum, and median AUC values for the 2013 models were 0.26, 0.91, and 0.58, respectively. For the 2015 models, the minimum, maximum and median AUC values were, correspondingly, 0.24, 0.92, and 0.57. For the between-year predictive models the minimum, maximum, and median AUC values were 0.37, 0.73, and 0.52, respectively. Conclusions Although some iterations of the models achieved near perfect prediction, the large ranges in AUC highlight the fragility of the data. The 2013 models performed slightly better than the 2015 models. The predictive performance of between-year HSI models was poor however. In conclusion, risk factor data cannot be used to identify athletes at an increased risk of HSI with any consistency.

    Item Type: Article
    Keywords: Predictive Modeling; Hamstring Strain Injuries; Elite Australian Footballers;
    Academic Unit: Faculty of Science and Engineering > Sports Science and Nutrition
    Item ID: 17923
    Identification Number:
    Depositing User: Jack Hickey
    Date Deposited: 08 Dec 2023 15:34
    Journal or Publication Title: Medicine & Science in Sports & Exercise
    Publisher: Lippincott, Williams & Wilkins
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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