Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football

•Performance of football players was correlated with heart rate data collected before a game.•Performance measures were derived from GPS devices worn by players during the game.•Feature selection and meta-regression techniques were used.•Best result gave a correlation coefficient of 0.86 (p < 0.05).•Potential benefit to team coaches for player selection.

This work investigates the effectiveness of using computer-based machine learning regression algorithms and meta-regression methods to predict performance data for Australian football players based on parameters collected during daily physiological tests. Three experiments are described. The first uses all available data with a variety of regression techniques. The second uses a subset of features selected from the available data using the Random Forest method. The third used meta-regression with the selected feature subset. Our experiments demonstrate that feature selection and meta-regression methods improve the accuracy of predictions for match performance of Australian football players based on daily data of medical tests, compared to regression methods alone. Meta-regression methods and feature selection were able to obtain performance prediction outcomes with significant correlation coefficients. The best results were obtained by the additive regression based on isotonic regression for a set of most influential features selected by Random Forest. This model was able to predict athlete performance data with a correlation coefficient of 0.86 (p < 0.05).