Rugby game performances and weekly workload: Using of data mining process to enter in the complexity

Abstract

This study aimed to i) identify key performance indicators of professional rugby matches, ii) define synthetic indicators of performance and iii) analyze how weekly workload (2WL) influences match performance throughout an entire season at different time-points (considering WL of up to 8 weeks prior to competition). This study uses abundant sports data and data mining techniques to assess player performance and to determine the influence of 2WL on performance. WL, locomotor activity and rugby specific actions were collected on 14 professional players (26.9 ± 1.9 years) during training and official matches. In order to highlight key performance indicators, a mixed-linear model was used to compare the players' activity relatively to competition results. This analysis showed that defensive skills represent a fundamental factor of team performance. Furthermore, a principal component analysis demonstrated that 88% of locomotor activity could be highlighted by 2 dimensions including total distance, high-speed/metabolic efforts and the number of sprints and accelerations. The final purpose of this study was to analyze the influence that WL has on match performance. To verify this, 2 different statistical models were used. A threshold-based model, from data mining processes, identified the positive influence (p<0.05) that chronic body impacts has on the ability to win offensive 1 on 1 duels during competition. This study highlights practical implications necessary for developing a better understanding of rugby match performance through the use of data mining processes.

Fig 1. Multivariable statistical approach.

Fig 2. First principal plane from PCA on normalized (Z-score) individual speed descriptors.

Fig 3. First principal plane from PCA on specific activities.

Fig 4. Correlation matrices between the variables “X” of the weekly workload and the parameters “Y” of the game’s activity.

Fig 5. Traditional decision tree illustrating the level of “Running performance”.

Fig 6

Conditional regression trees showing the influence of workload parameters on some activity indicators during the matches: a) number of sprints and accelerations, b) number of offensive duals won.