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

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)

Using Supervised Learning to Predict English Premier League Match Results From Starting Line-up Player Data

Soccer is one of the most popular sports around the world. Many people, whether they are a fan of a soccer team, a player of online soccer games or even the professional coach of a soccer team, will attempt to use some relevant data to predict the result of a match. Many of these kinds of prediction models are built based on data from the match itself, such as the overall number of shots, yellow or red cards, fouls committed, etc. of the home and away teams. However, this research attempted to predict soccer game results (win, draw or loss) based on data from players in the starting line-up during the first 12 weeks of the 2018-2019 season of the English Premier League

Identifying Optimal Technical and Tactical Performance Characteristics in Australian Football

This study identified the optimal technical and tactical performance characteristics of Australian football teams. The application of machine learning approaches identified the key indicators of successful AFL teams. The main findings of this research provide an evidence-base for key stakeholders to inform their training and match day decisions