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

Herbert F. Jelinek, Andrei Kelarev, Dean J Robinson, Andrew Stranieri, David J. Cornforth

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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).
Original languageEnglish
Pages (from-to)81-87
Number of pages7
JournalApplied Soft Computing
Volume14
Issue numberA
DOIs
Publication statusPublished - 2014

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