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The relative contribution of training intensity and duration to daily measures of training load in professional rugby league and union
Authors:Dan Weaving  Nicholas Dalton-Barron  Shaun McLaren  Sean Scantlebury  Cloe Cummins  Gregory Roe
Institution:1. Carnegie Applied Rugby Research Centre, Leeds Beckett University , Leeds, West Yorkshire, UK;2. Leeds Rhinos Rugby League Club , Leeds, UK;3. Department of Sport, Health, and Exercise Science, University of Hull , Hull, UK d.a.weaving@leedsbeckett.ac.uk;5. England Performance Unit, The Rugby Football League , Leeds, UK;6. Catapult Sports , Leeds, UK ORCID Iconhttps://orcid.org/0000-0002-8476-3042;7. England Performance Unit, The Rugby Football League , Leeds, UK;8. School of Science and Technology, University of New England , Armidale, NSW, Australia;9. National Rugby League , Australia ORCID Iconhttps://orcid.org/0000-0003-1960-8916;10. Bath Rugby , Bath, UK ORCID Iconhttps://orcid.org/0000-0003-3901-4568
Abstract:ABSTRACT

This study examined the relative contribution of exercise duration and intensity to team-sport athlete’s training load. Male, professional rugby league (n = 10) and union (n = 22) players were monitored over 6- and 52-week training periods, respectively. Whole-session (load) and per-minute (intensity) metrics were monitored (league: session rating of perceived exertion training load sRPE-TL], individualised training impulse, total distance, BodyLoad?; union: sRPE-TL, total distance, high-speed running distance, PlayerLoad?). Separate principal component analyses were conducted on the load and intensity measures to consolidate raw data into principal components (PC, k = 4). The first load PC captured 70% and 74% of the total variance in the rugby league and rugby union datasets, respectively. Multiple linear regression subsequently revealed that session duration explained 73% and 57% of the variance in first load PC, respectively, while the four intensity PCs explained an additional 24% and 34%, respectively. Across two professional rugby training programmes, the majority of the variability in training load measures was explained by session duration (~60–70%), while a smaller proportion was explained by session intensity (~30%). When modelling the training load, training intensity and duration should be disaggregated to better account for their between-session variability.
Keywords:Training load  principal component analysis  time series  rugby
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