Purpose This study addresses the lack of objective player-based metrics in men`s rugby league by introducing a comprehensive set of novel performance metrics designed to quantify a player`s overall contribution to team success. Methods Player match performance data were captured by Stats Perform for every National Rugby League season from 2018 until 2022; a total of five seasons. The dataset was divided into offensive and defensive variables and further split according to player position. Five machine learning algorithms (Principal Component Regression, Lasso Regression, Random Forest, Regression Tree, and Extreme Gradient Boost) were considered in the analysis, which ultimately generated Wins Created and Losses Created for offensive and defensive performance, respectively. These two metrics were combined to create a final metric of Net Wins Added. The validity of these player performance metrics against traditional objective and subjective measures of performance in rugby league were evaluated. Results The metrics correctly predicted the winner of 80.9% of matches, as well as predicting the number of team wins per season with an RMSE of 1.9. The metrics displayed moderate agreement (Gwet AC1 = 0.505) when predicting team of the year award recipients. When predicting State of Origin selection, the metrics displayed moderate agreement for New South Wales (0.450) and substantial agreement for Queensland (0.652). Conclusion The development and validation of these objective player performance metrics represent significant potential to enhance talent evaluation and player recruitment.
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