  To investigate whether training load monitoring data could be used to predict
injuries in elite Australian football players, data were collected from elite
athletes over 3 seasons at an Australian football club. Loads were quantified
using GPS devices, accelerometers and player perceived exertion ratings.
Absolute and relative training load metrics were calculated for each player
each day (rolling average, exponentially weighted moving average, acute:chronic
workload ratio, monotony and strain). Injury prediction models (regularised
logistic regression, generalised estimating equations, random forests and
support vector machines) were built for non-contact, non-contact time-loss and
hamstring specific injuries using the first two seasons of data. Injury
predictions were generated for the third season and evaluated using the area
under the receiver operator characteristic (AUC). Predictive performance was
only marginally better than chance for models of non-contact and non-contact
time-loss injuries (AUC$<$0.65). The best performing model was a multivariate
logistic regression for hamstring injuries (best AUC=0.76). Learning curves
suggested logistic regression was underfitting the load-injury relationship and
that using a more complex model or increasing the amount of model building data
may lead to future improvements. Injury prediction models built using training
load data from a single club showed poor ability to predict injuries when
tested on previously unseen data, suggesting they are limited as a daily
decision tool for practitioners. Focusing the modelling approach on specific
injury types and increasing the amount of training data may lead to the
development of improved predictive models for injury prevention.
