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

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dc.contributor.author Dubois, Romain
dc.contributor.author Bru, Noelle
dc.contributor.author Paillard, Thierry
dc.contributor.author Le Cunuder, Anne
dc.contributor.author Lyons, Mark
dc.contributor.author Maurelli, Olivier
dc.contributor.author Philippe, Kilian
dc.contributor.author Prioux, Jacques
dc.date.accessioned 2020-02-19T09:36:58Z
dc.date.available 2020-02-19T09:36:58Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/10344/8534
dc.description peer-reviewed en_US
dc.description.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. en_US
dc.language.iso eng en_US
dc.publisher Public Library of Science en_US
dc.relation.ispartofseries PLoS/ONE;
dc.subject Team performance en_US
dc.subject attempted tackles en_US
dc.subject ruck participation en_US
dc.title Rugby game performances and weekly workload: Using of data mining process to enter in the complexity en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1371/journal.pone.0228107
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 2943135


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