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

Romain Dubois, Noëlle Bru, Thierry Paillard, Anne Le Cunuder, Mark Lyons, Olivier Maurelli, Kilian Philippe and Jacques Prioux

PLOS ONE, 2020, vol. 15, issue 1, 1-21

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

Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228107 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 28107&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0228107

DOI: 10.1371/journal.pone.0228107

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().