Table 1.
Potential predictive risk factor variable list.
Fig 1.
An adapted Figure [10] demonstrating the process of injury prediction validation using a pattern recognition approach.
Athlete monitoring and training load refer to input variables for the pattern recognition algorithms. Injury classification is typically the binary response (injury yes/no) in labelling the training vectors. The models are those tested by the injury prediction algorithms in this study. Cross-validation and feature selection are processes of training, validating, and testing the models developed, further supported by the output determining the usefulness of the model. Furthermore, blue leaves describe how to train, validate, and test the model developed by the injury prediction algorithm.
Table 2.
Algorithm agreements among feature selection models and their associated classification for non-contact lower limb injuries during the season.
Table 3.
Classification accuracy for severe non-contact lower limb injury during phases of the season.
Table 4.
Classification accuracy for non-contact lower limb injury during phases of the season.
Table 5.
Classification accuracy for non-contact ankle injury during phases of the season.