One of the main reasons certainly resides in the difficulty to identify and evaluate the key performance indicators (individual and collective) in team-sports. In spite of the correlations between WL, performance, and more particularly physical performance, which are commonly accepted in team sports, very few studies have successfully established these relationships in a competitive context. Indeed, many studies demonstrate the influence of weekly workloads (2WL) on acute and chronic physical performance, physiological adaptations and injury risks in elite rugby players. Workload (WL) monitoring, its’ management and developing optimal adaptational capabilities are important parameters to consider in elite team-sport environments. Optimizing physical preparedness has, therefore, become the main concern for team staffs. promotion-relegation championships), among other factors, has greatly contributed to enhanced risks of injury and non-functional adaptations. The incessant increase in game intensity and competitive demands (i.e.
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Rugby union (RU) became a professional sport in 1995 and has since come across multiple ethical and financial issues. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors. There are no patents, products in development or marketed products to declare. The SASP Club Athletique Brive Correze provided us access to the GPS and activity data, but it had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: We have the following interests: Romain Dubois was employed by SASP Club Athletique Brive Correze. Nevertheless, anticipated data access from qualifying researchers with an approved protocol will be possible with the agreement of the club (SASP Club Athletique Brive Correze) by contacting the corresponding author ( the club ( or the head S&C coach during the study period ( No funding was received for this study and there was no conflict of interest for this study. These data can be made public from June 2021. The workload parameters were the propriety of the players and for this study, they were accepted that these workload parameters were used to better understand the links between the workload and the performance. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Thank you for the players who participated to this study.
Received: AugAccepted: JanuPublished: January 29, 2020Ĭopyright: © 2020 Dubois et al. PLoS ONE 15(1):Įditor: Cristina Cortis, University of Cassino e Lazio Meridionale, ITALY (2020) Rugby game performances and weekly workload: Using of data mining process to enter in the complexity. This study highlights practical implications necessary for developing a better understanding of rugby match performance through the use of data mining processes.Ĭitation: Dubois R, Bru N, Paillard T, Le Cunuder A, Lyons M, Maurelli O, et al. 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. To verify this, 2 different statistical models were used. The final purpose of this study was to analyze the influence that WL has on match 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. This analysis showed that defensive skills represent a fundamental factor of team performance. In order to highlight key performance indicators, a mixed-linear model was used to compare the players’ activity relatively to competition results.
WL, locomotor activity and rugby specific actions were collected on 14 professional players (26.9 ± 1.9 years) during training and official matches. This study uses abundant sports data and data mining techniques to assess player performance and to determine the influence of 2WL on performance.
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).
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