The purpose of this study is to evaluate the influence of competition level on running patterns for five playing position in the most successful 2014-2015 European rugby union team. Seventeen French rugby union championship and seven European rugby Champions Cup games were analysed. Global positioning system (sampling: 10 Hz) were used to determine high-speed movements, high-intensity accelerations, repeated high-intensity efforts and high-intensity micro-movements characteristics for five positional groups. During European Champions Cup games, front row forwards performed a higher number of repeated high-intensity efforts compared to National championship games (5.8±1.6 vs. 3.6±2.3; +61.1%), and back row forwards travelled greater distance both at high-speed movements (3.4±1.8 vs. 2.4±0.9 m·min; +41.7%) and after high-intensity accelerations (78.2±14.0 vs. 68.1 ±13.4 m; +14.8%). In backs, scrum halves carried out more high-intensity accelerations (24.7±3.1 vs. 14.8±5.0; +66.3%) whereas outside backs completed a higher number of high-speed movements (62.7±25.4 vs. 48.3±17.0; +29.8%) and repeated high-intensity efforts (13.5±4.6 vs. 9.7±4.9; +39.2%). These results highlighted that the competition level affected the high-intensity activity differently among the five playing positions. Consequently, training programs in elite rugby should be tailored taking into account both the level of competition and the high-intensity running pattern of each playing position.
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http://dx.doi.org/10.1055/a-1144-3035 | DOI Listing |
Ann Biomed Eng
January 2025
Carnegie Applied Rugby Research (CARR) Centre, Carnegie School of Sport, Leeds Beckett University, Leeds, UK.
Purpose: Head acceleration events (HAEs) are a growing concern in contact sports, prompting two rugby governing bodies to mandate instrumented mouthguards (iMGs). This has resulted in an influx of data imposing financial and time constraints. This study presents two computational methods that leverage a dataset of video-coded match events: cross-correlation synchronisation aligns iMG data to a video recording, by providing playback timestamps for each HAE, enabling analysts to locate them in video footage; and post-synchronisation event matching identifies the coded match event (e.
View Article and Find Full Text PDFInj Prev
January 2025
Carnegie Applied Rugby Research (CARR) centre, Carnegie School of Sport, Leeds Beckett University, Leeds, UK.
Background: Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby.
Methods: This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts.
J Sports Sci
January 2025
Department of Sport, Hartpury University, Gloucestershire, UK.
This study aimed to examine the sleep parameters and sleep/wake regularity of a cohort of student-athletes who start training between 06:30 and 07:00. Twenty-one male Rugby Union players, aged 21 ± 2 years and competing at a national level, were assessed using actigraphy over two weeks, and the Athlete Sleep Screening Questionnaire (ASSQ). Sleep/wake regularity was calculated using the Sleep Regularity Index (SRI).
View Article and Find Full Text PDFEur Respir J
January 2025
European Respiratory Society, Lausanne, Switzerland.
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