Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs

Abstract

The assessment of competitive movement demands in team sports has traditionally relied upon global positioning system (GPS) analyses presented as fixed-time epochs (e.g., 5–40 min). More recently, presenting game data as a rolling average has become prevalent due to concerns over a loss of sampling resolution associated with the windowing of data over fixed periods. Accordingly, this study compared rolling average (ROLL) and fixed-time (FIXED) epochs for quantifying the peak movement demands of international rugby union match-play as a function of playing position. Elite players from three different squads (n = 119) were monitored using 10 Hz GPS during 36 matches played in the 2014–2017 seasons. Players categorised broadly as forwards and backs, and then by positional sub-group (FR: front row, SR: second row, BR: back row, HB: half back, MF: midfield, B3: back three) were monitored during match-play for peak values of high-speed running (>5 m·s-1; HSR) and relative distance covered (m·min-1) over 60–300 s using two types of sample-epoch (ROLL, FIXED). Irrespective of the method used, as the epoch length increased, values for the intensity of running actions decreased (e.g., For the backs using the ROLL method, distance covered decreased from 177.4 ± 20.6 m·min-1 in the 60 s epoch to 107.5 ± 13.3 m·min-1 for the 300 s epoch). For the team as a whole, and irrespective of position, estimates of fixed effects indicated significant between-method differences across all time-points for both relative distance covered and HSR. Movement demands were underestimated consistently by FIXED versus ROLL with differences being most pronounced using 60 s epochs (95% CI HSR: -6.05 to -4.70 m·min-1, 95% CI distance: -18.45 to -16.43 m·min-1). For all HSR time epochs except one, all backs groups increased more (p < 0.01) from FIXED to ROLL than the forward groups. Linear mixed modelling of ROLL data highlighted that for HSR (except 60 s epoch), SR was the only group not significantly different to FR. For relative distance covered all other position groups were greater than the FR (p < 0.05). The FIXED method underestimated both relative distance (~11%) and HSR values (up to ~20%) compared to the ROLL method. These differences were exaggerated for the HSR variable in the backs position who covered the greatest HSR distance; highlighting important consideration for those implementing the FIXED method of analysis. The data provides coaches with a worst-case scenario reference on the running demands required for periods of 60–300 s in length. This information offers novel insight into game demands and can be used to inform the design of training games to increase specificity of preparation for the most demanding phases of matches.

The Most Demanding Scenarios of Play in Basketball Competition From Elite Under-18 Teams

“…In particular, Vázquez-Guerrero et al (2019) showed that basketball players averaged 1.8 and 1.5 high-intensity accelerations and decelerations and covered a relative distance of 72.6 m min −1 . A similar trend has been observed in previous studies conducted in other sports, such as associated football (Abbott et al, 2018;Delaney et al, 2018;Martín-García et al, 2018;Casamichana et al, 2019), rugby (Delaney et al, 2016;Cunningham et al, 2018), Gaelic football (Malone et al, 2017), and Australian football (Delaney et al, 2017).…”

“…While some studies have determined the most demanding scenarios in physical demands during competition in intermittent team sports such as association football (Abbott et al, 2018;Delaney et al, 2018;Martín-García et al, 2018;Casamichana et al, 2019), rugby (Delaney et al, 2016;Cunningham et al, 2018), Gaelic football (Malone et al, 2017), and Australian football (Delaney et al, 2017) through different time average rolling durations, no studies are available that quantify physical demands during match play in basketball using this approach. A plethora of studies have examined the average (mainly per minute) and absolute physical demands of match play in basketball reporting that players usually cover 5-6 km at an average speed of 70-90 m min −1 and perform a total of 40-50 jumps (Stojanović et al, 2018).…”

Applied Sport Science for Male Age-Grade Rugby Union in England

“…Furthermore, the maximum average speed using a 0.1-s rolling mean for a 1min period during U18 academy RU ranged between 154 ± 17 (front row) and 185 ± 20 (scrum-half) m min −1 demonstrating substantially greater values than those presented in whole match analysis [31]. These values can be used when planning, 'live' monitoring and retrospectively analysing training so players are prepared for the 'worstcase scenario' during matches as in recently completed studies in senior international players [7,38].…”

Heat alleviation strategies for athletic performance: A review and practitioner guidelines

“…Whilst it might be logical for athletes to prepare in conditions which represent the mean environmental conditions in which they will be competing, for example, mean air conditions for Tokyo in August are 30-31°C,~70% RH, the authors (and others) recommend that as a minimum, athletes train in the maximum/ upper limit of environmental conditions which are forecast for the location at the time of year, for instance maximum air conditions for Tokyo in August are >34°C, >80% RH [146]. Indeed, it has been proposed that HA temperatures should be 5-10°C higher than the mean of the event location [148], in keeping with conventional training programs that mimic "worst-case scenario" game demands [149][150][151]. Athletes and practitioners should also be cautious of being reliant on mean temperatures, particularly given within day differences of~5°C are likely and events/heats may be scheduled at different times of the day [146].…”