An enhanced metric of injury risk utilizing Artificial Intelligence

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Abstract: Injuries are one of the largest determinants in sport; and as such injury risk management should be at the forefront of all major sporting organizations. Many risk management platforms and algorithms attempt to utilize the broad range of available data to assist in identifying players that are likely to encounter issues. Presented in this study are the results of such a platform that analyses performance against many common pitfalls with the problem. Sophisticated data-preprocessing coupled with Artificial-Neural-Networks are utilized on a dataset of players across two Australian Rules Football teams to identify if lower-body soft-tissue injury risk is related to training patterns and player-load. Analysis of player-risk on gameday and in training sessions is discussed with relevance to the inherent-increased risk on gameday. Influence of previous injury and recent injury history are analyzed to identify the influence on the resulting injury risk. With multiple teams’ worth of data available, utilization of both training sets in AI training and validation is discussed to identify if different training patterns can be combined under the one model and if a model can be pre-trained with one team in order to yield better results. Overall, models with ROC-AUC scores of 0.78 can be achieved which can be utilized by organizations to reduce injury rates by providing a means of targeted intervention in training plans and player load.

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