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

Romain Dubois (1) , Noëlle Bru (2) , Anne Le Cunuder (3) , Mark Lyons (4) , Olivier Maurelli (5) , Kilian Philippe (1) , Jacques Prioux (3) , Thierry Paillard (1)

1 MEPS - Mouvement, Équilibre, Performance, Santé
2 LMAP - Laboratoire de Mathématiques et de leurs Applications [Pau]
3 M2S - Laboratoire Mouvement Sport Santé
4 UL - University of Limerick
5 APERE - Adaptations Physiologiques à l'Exercice et Réadaptation à l'effort - UR UPJV 3300

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.

Domains: Life Sciences [q-bio]; Human health and pathology; Tissues and Organs [q-bio.TO]

Romain Dubois, Noëlle Bru, Anne Le Cunuder, Mark Lyons, Olivier Maurelli, et al.. Rugby game performances and weekly workload Using of data mining process to enter in the complexity. PLoS ONE, 2020, 15 (1), pp.e0228107. ⟨10.1371/journal.pone.0228107⟩. ⟨hal-02472308⟩