Analysis of Physical Demands During Youth Soccer Match-Play: Considerations of Sampling Method and Epoch Length

Abstract

The purpose of this study was to examine the physical match profiles of professional soccer players using 3 and 5 min fixed and rolling averages as well as fixed 1 min averages, with considerations to training prescription. Twenty-nine, professional U23 soccer outfield players competed across 17 competitive matches during the 2017/18 season, equating to a total of 130 separate physical match profiles. Match activities were recorded using global positioning system (GPS) devices with integrated micro-electrical mechanical systems (MEMS), recording total distance (TD), high-speed running (HSR) and metabolic power (MP). For each individual match profile and variable, 1, 3 and 5 min peak, post-peak, and average values were calculated using fixed-time epochs (FIXED) and rolling averages (ROLL). Linear mixed models were employed to examine the differences in the dependent variables as a function of the method of measurement. Results revealed significantly higher peak values, for relative TD, relative HSR and relative MP when employing the ROLL sampling method, in comparison to the FIXED method, for both 3 min and 5 min epoch lengths. Analysis of epoch length revealed significantly higher peak values, across all positions, for relative TD, relative HSR and MP for 1 min epochs, in comparison to 3 min and 5 min epochs. The data offers a novel insight into the appropriate identification of physical demands during youth soccer match-play.Researchers and practitioners should consider the sampling method and epoch length when assessing the physical demands of competitive match-play, as well as when designing and prescribing sport-specific conditioning drills.

Implementing High-speed Running and Sprinting Training in Professional Soccer

“…Time-motion analyses of soccer matches report professional players to regularly cover total distances ranging between 10-13 km of which around 900 m and 250-300 m travelled at high-speed running (HSR; speed ranging from 19.8 km • h -1 to 25.2 km • h -1 ) and sprinting (speed ≥ 25.2 km • h -1 ), respectively [1,2]. Although HSR and sprinting account togeth-er for only 10 % of the total distance covered during a match, the high intensity physical efforts they inherently involve are generally considered by researchers and practitioners of paramount importance for both competition outcomes and soccer specific fitness training [3][4][5]. Observational analyses of the locomotive demands during official soccer matches across the last ten years, have highlighted a consistent increase of HSR and sprinting efforts and relative distances by 24-35 % and 36-63 %, respectively [6,7].…”

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

“…The Bayesian methodology [based on the quantification of the relative degree of evidence for supporting two rival hypotheses, null hypothesis (H 0 ) vs. alternative hypothesis (H 1 ), by means of the Bayesian factor (BF 10 ) (Linke et al, 2018;Doncaster et al, 2019)] has been recently suggested as an alternative to the traditional frequentist statistics (based on confidence intervals and p values) for hypothesis testing due to (among others) the following benefits: the BF 10 quantifies evidence that the data provide for H 0 vs. H 1 , the BF 10 can quantify evidence in favor of H 0 , and the BF 10 is not "violently biased" against H 0 (Ly et al, 2016;Wagenmakers et al, 2018).…”

“…These observed differences in players' physical demands between the sequential and rolling average time epoch approaches seem to increase as the time epoch length decreases (i.e., below 5 min), which may be due to the physiological, contextual, and technical-tactical demands of the sport (Ly et al, 2016;Wagenmakers et al, 2018). Furthermore, it has been also suggested that the higher the sample frequency is, the larger the inter-approach differences may be (Doncaster et al, 2019).…”

Effect of Scaling Task Constraints on the Learning Processes of Under-11 Badminton Players during Match-Play

“…In the third phase, the Bayesian methodology was carried out on all the variables. The Bayesian methodology (based on the quantification of the relative degree of evidence for supporting two rival hypotheses, the null hypothesis (H0) vs. alternative hypothesis (H1), by means of the Bayesian factor (BF10) [ 54 , 55 ]) has been recently suggested as an alternative to the traditional frequentist statistics (based on confidence intervals and p values) for hypothesis testing due to (among others) the following benefits: the BF10 quantifies evidence that the data provide for H0 vs. H1; the BF10 can quantify evidence in favor of H0; and the BF10 is not “violently biased” against H0 [ 56 , 57 ]. The BF10 was interpreted using the evidence categories suggested by Lee and Wagenmakers [ 58 ]: <1/100 = extreme evidence for H0; from 1/100 to <1/30 = very strong evidence for H0; from 1/30 to <1/10 = strong evidence for H0; from 1/10 to <1/3 = moderate evidence for H0; from 1/3 to <1 anecdotal evidence for H0; from 1 to 3 = anecdotal evidence for H1; from >3 to 10 = moderate evidence for H1; from >10 to 30 = strong evidence for H1; from >30 to 100 = very strong evidence for H1; and >100 extreme evidence for H1.…”