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Background: Quantifying the peak match demands within the football codes is useful for the appropriate prescription of external training load. Wearable microtechnology devices can be used to identify the peak match demands, although various methodologies exist at present., Objectives: This systematic review aimed to identify the methodologies and microtechnology-derived variables used to determine the peak match demands, and to summarise current data on the peak match demands in the football codes., Methods: A systematic search of electronic databases was performed from earliest record to May 2018; keywords relating to microtechnology, peak match demands and football codes were used., Results: Twenty-seven studies met the eligibility criteria. Six football codes were reported: rugby league (n = 7), rugby union (n = 5), rugby sevens (n = 4), soccer (n = 6), Australian Football (n = 2) and Gaelic Football (n = 3). Three methodologies were identified: moving averages, segmental and 'ball in play'. The moving averages is the most commonly used (63%) and superior method, identifying higher peak demands than other methods. The most commonly used variables were relative distance covered (63%) and external load in specified speed zones (57%)., Conclusion: This systematic review has identified moving averages to be the most appropriate method for identifying the peak match demands in the football codes. Practitioners and researchers should choose the most relevant duration-specific period and microtechnology-derived variable for their specific needs. The code specific peak match demands revealed can be used for the prescription of conditioning drills and training intensity.

The aim was to use a combination of video analysis and microtechnology (10 Hz global positioning system [GPS]) to quantify and compare the speed and acceleration of ball-carriers and tacklers during the pre-contact phase (contact − 0.5s) of the tackle event during rugby league match-play. Data were collected from 44 professional male rugby league players from two Super League clubs across two competitive matches. Tackle events were coded and subject to three stages of inclusion criteria to identify front-on tackles. 10 Hz GPS data was synchronised with video to extract the speed and acceleration of the ball-carrier and tackler into each front-on tackle (n = 214). Linear mixed effects models (effect size [ES], confidence intervals, p-values) compared differences. Overall, ball-carriers (4.73 ± 1.12 m∙s−1) had greater speed into front-on tackles than tacklers (2.82 ± 1.07 m∙s−1; ES = 1.69). Ball-carriers accelerated (0.67 ± 1.01 m∙s−2) into contact whilst tacklers decelerated (−1.26 ± 1.36 m∙s−2; ES = 1.74). Positional comparisons showed speed was greater during back vs. back (ES = 0.66) and back vs. forward (ES = 0.40) than forward vs. forward tackle events. Findings can be used to inform strategies to improve performance and player welfare. [ABSTRACT FROM AUTHOR]

To (i) quantify the differences in locomotor and technical characteristics between different drill categories in female soccer and (ii) explore the training drill distributions between different standards of competition. Technical (ball touches, ball releases, high-speed ball releases) and locomotor data (total distance, high-speed running distance [>5.29 m∙s−1]) were collected using foot-mounted inertial measurement units from 458 female soccer players from three Women's Super League (WSL; n = 76 players), eight Women's Championship (WC; n = 217) and eight WSL Academy (WSLA; n = 165) teams over a 28-week period. Data were analysed using general linear mixed effects. Across all standards, the largest proportion of time was spent in technical (TEC) (WSL = 38%, WC = 28%, WSLA = 29%) and small-sided extensive games (SSGe) (WSL = 20%, WC = 31%, WSLA = 30%) drills. WSL completed more TEC and tactical (TAC) training whilst WC and WSLA players completed more SSGe and possession (POS) drills. Technical drills elicited the highest number of touches, releases and the highest total distance and high-speed activity. Position-specific drills elicited the lowest number of touches and releases and the lowest total distance. When the technical and locomotor demand of each drill were made relative to time, there were limited differences between drills, suggesting drill duration was the main moderating factor. Findings provide novel understanding of the technical and locomotor demands of different drill categories in female soccer. These results can be used by coaches and practitioners to inform training session design. [ABSTRACT FROM AUTHOR]

Understanding the maximal intensity periods (MIP) of soccer matches can optimise training prescription. The aim was to establish differences between positions and other contextual factors (match location, match outcome, playing formation and score line) for both external and internal MIP variables and to investigate the differences in the match start time between MIP variables. Maximal moving averages (1 to 10 min) for average speed, high-speed running (5.5–7 m·s−1), sprinting (>7 m·s−1; all m·min−1), average acceleration/deceleration (m·s−2) and heart rate (bpm, % maximal) were calculated from 24 professional youth players across 31 matches. Linear mixed models determined differences in MIP variables between positions, contextual factors and in the match start time of MIPs. Trivial to large positional differences existed in maximal external intensities while central defenders presented the lowest heart rate. It was unclear whether maximal intensities were influenced by contextual factors. MIPs for average speed, acceleration/deceleration and heart rate tend to occur concurrently (ES = trivial) within the first 30 min, while high-speed running and sprinting are likely to occur concurrently (ES = trivial) throughout a whole match. Practitioners could target maximising average speed and average acceleration/deceleration in technical-tactical based training to maximise heart rate responses. [ABSTRACT FROM AUTHOR]

The 2019 and 2020 Super League (SL) seasons included several competition rule changes. This study aimed to quantify the difference between the 2018, 2019 and 2020 SL seasons for duration, locomotor and event characteristics of matches. Microtechnology and match event data were analysed from 11 SL teams, comprising 124 players, from 416 competitive matches across a three-year data collection period. Due to an enforced suspension of league competition as a consequence of COVID-19 restrictions, and subsequent rule changes upon return to play, season 2020 was divided into season 2020a (i.e. Pre-COVID suspension) and season 2020b (i.e. Post-COVID suspension). Duration, locomotor variables, and match events were analysed per whole-match and ball-in-play (BIP) periods with differences between seasons determined using mixed-effects models. There were significant (ρ ≤ 0.05) reductions in whole-match and BIP durations for adjustables and backs in 2019 when compared to 2018; albeit the magnitude of reduction was less during BIP analyses. Despite reduced duration, adjustables reported an increased average speed suggesting reduced recovery time between bouts. Both forwards and adjustables also experienced an increase in missed tackles between 2018 and 2019 seasons. When comparing 2019 to 2020a, adjustables and backs increased their average speed and distance whilst all positional groups increased average acceleration both for whole-match and BIP analyses. When comparing 2020a to 2020b, all positional groups experienced reduced average speed and average acceleration for both whole-match and BIP analyses. Forwards experienced an increased number of tackles and carries, adjustables experienced an increased number of carries, and backs experienced an increased number of missed tackles when comparing these variables between season 2020a and 2020b. Rule changes have a greater effect on whole-match duration and locomotor characteristics than those reported during BIP periods which suggests the implemented rule changes have removed stagnant time from matches. Amendments to tackle related rules within matches (e.g., introduction of the 'six-again' rule) increases the number of collision related events such as carries and tackles. [ABSTRACT FROM AUTHOR]

This study aimed to identify which physical and technical-tactical performance indicators (PI) can classify between levels of rugby league match-play. Data were collected from 46 European Super League (ESL) and 36 under-19 Academy (Academy) level matches over two seasons. Thirty-one ESL players and 41 Academy players participated. Microtechnology units were used to analyse the physical PI and matches were videoed and coded for individual technical-tactical PI, resulting in 157 predictor variables. Data were split into training and testing datasets. Random forests (RF) were built to reduce the dimensionality of the data, identify variables of importance and build classification models. To aid practical interpretation, conditional inference (CI) trees were built. Nine variables were identified as most important for backs, classifying between levels with 83% (RF) and 78% (CI tree) accuracy. The combination of variables with the highest classification rate was PlayerLoad2D, PlayerLoadSLOW per Kg body mass and high-speed running distance. Four variables were identified as most important for forwards, classifying with 68% (RF) and 64% (CI tree) accuracy. Defensive play-the-ball losses alone had the highest classification rate for forwards. The identified PI and their unique combinations can be developed during training to aid in progression through the rugby league playing pathway. [ABSTRACT FROM AUTHOR]

Understanding the locomotor characteristics of competition can help rugby league (RL) coaches optimise training prescription. To date, no research exists on the locomotor characteristics of women's RL. The aim was to compare whole match and peak locomotor characteristics of women's RL competition at international (RL World Cup [WRLWC]) and domestic level (Super League [WSL]). Microtechnology data were collected from 58 players from 3-WSL clubs and 1-WRLWC team. Participants were classified into forwards (n = 30) and backs (n = 28). Partial least squares correlation analysis established which variables were important to discriminate between the level of competition (international vs. domestic) and positional group (forwards vs. backs). Linear mixed-effects models estimated the differences between standards of competition and positional group for those variables. International forwards were most likely exposed to greater peak 1-min average acceleration (standardised mean difference = 1.23 [0.42 to 2.04]) and peak 3-min average acceleration (1.13 [0.41 to 1.85]) than domestic forwards. International backs likely completed greater peak 1-min average acceleration (0.83 [0.08 to 1.58]) than domestic backs and possibly greater high-speed-distances (0.45 [−0.17 to 1.07]). Findings highlight the need for positional specific training across levels to prepare representative players for the increased match characteristics of international competition. [ABSTRACT FROM AUTHOR]

Identifying the external training load variables which influence subjective internal response will help reduce the mismatch between coach-intended and athlete-perceived training intensity. Therefore, this study aimed to reduce external training load measures into distinct principal components (PCs), plot internal training response (quantified via session Rating of Perceived Exertion [sRPE]) against the identified PCs and investigate how the prescription of PCs influences subjective internal training response. Twenty-nine school to international level youth athletes wore microtechnology units for field-based training sessions. SRPE was collected post-session and assigned to the microtechnology unit data for the corresponding training session. 198 rugby union, 145 field hockey and 142 soccer observations were analysed. The external training variables were reduced to two PCs for each sport cumulatively explaining 91%, 96% and 91% of sRPE variance in rugby union, field hockey and soccer, respectively. However, when internal response was plotted against the PCs, the lack of separation between low-, moderate- and high-intensity training sessions precluded further analysis as the prescription of the PCs do not appear to distinguish subjective session intensity. A coach may therefore wish to consider the multitude of physiological, psychological and environmental factors which influence sRPE alongside external training load prescription. [ABSTRACT FROM AUTHOR]

To quantify the peak movement and contact demands of National Rugby League (NRL) and European Super League (ESL) competition players were tracked during 10 NRL (166 files) and 10 ESL (143 files) matches using microtechnology devices. The peak 1- to 5-min periods were then calculated for average match speed (m·min−1), and acceleration (m·s−2) when 0, 1, 2, and ≥3 collisions per min occurred. Linear mixed effect models and Cohen's effect size statistic (± 90%CI) were used to determine the differences in movement profiles when collisions occurred. Compared to no collision periods, as frequency of collisions per minute increased, there were progressive reductions in running speed for most positional groups. The addition of 1 or more collisions per min resulted in average effect size reductions in match speed of −0.14 for NRL forwards, −0.89 for NRL backs, −0.48 for ESL forwards, and −2.41 for ESL backs. ESL forwards had the highest frequency of peak periods involving 3 or more collisions per min, 22% of all periods, followed by NRL forwards (14%), NRL backs (10%) and ESL backs (8%). This study highlights the peak movement and collision demands of professional rugby league competition and allows practitioners to develop training drills that reflect worst case scenarios. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to: (a) identify the association between external-workloads and injury-risk in the subsequent week; and (b) understand the effectiveness of workload variables in establishing injury-risk.Design: Retrospective cohort study.Methods: Workload and injury data (soft-tissue) were collected from forty-eight professional male rugby league players. Load variables included duration (min), total distance (m), relative distance (mmin-1), high speed distance ([m]>20kmh-1), very-high speed distance ([m]>25kmh-1), acceleration and deceleration efforts (count) and PlayerLoad (Arbitrary Unit: AU). Cumulative two-, three- and four-weekly loads; Acute:Chronic Workload Ratio (ACWR); Mean-Standard Deviation Workload Ratio (MSWR) and strain values were calculated and divided into three equally-sized bins (low, moderate and high). Generalised Estimating Equations analysed relationships between workload variables and injury probability in the subsequent week.Results: Injury-risk increased alongside increases in the ACWR for duration, total distance and PlayerLoad. Conversely, injury-risk decreased (Area Under Curve: 0.569-0.585) with increases in the four-weekly duration, total distance, accelerations, decelerations and PlayerLoad. For relative distance, high four-weekly workloads (high: >60mmin-1) demonstrated a positive association with injury-risk, whilst high two-weekly loads (high: >82 mmin-1) were negatively associated.Conclusions: A range of external workload metrics and summary statistics demonstrate either positive or negative associations with injury-risk status. Such findings provide the framework for the development of decision-support systems in which external workload metrics (e.g. total or high speed distance) can be uniquely and routinely monitored across a range of summary statistics (i.e. cumulative weekly loads and ACWR) in order to optimise player performance and welfare. [ABSTRACT FROM AUTHOR]

Understanding the most demanding passages of European Super League competition can optimise training prescription. We established positional and match half differences in peak relative distances (m·min−1) across durations, and the number of collisions, high-speed- and very-high-speed-distance completed in the peak 10 min period. Moving-averages (10 s, 30 s, 1 min, 5 min, 10 min) of instantaneous speed (m·s−1) were calculated from 25 professional rugby league players during 25 matches via microtechnology. Maximal m·min−1 was taken for each duration for each half. Concurrently, collisions (n), high-speed- (5 to 7 m·s−1; m) and very-high-speed-distance (> 7 m·s−1; m) were coded during each peak 10 min. Mixed-effects models determined differences between positions and halves. Aside from peak 10 s, trivial differences were observed in peak m·min−1 between positions or halves across durations. During peak 10 min periods, adjustables, full- and outside-backs ran more at high-speed and very-high-speed whilst middle- and edge-forwards completed more collisions. Peak m·min−1 is similar between positional groups across a range of durations and are maintained between halves of the match. Practitioners should consider that whilst the overall peak locomotor "intensity" is similar, how they achieve this differs between positions with forwards also exposed to additional collision bouts. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the validity of global positioning system (GPS) and micro-electrical- mechanical-system (MEMS) data generated in real time through a dedicated receiver. Postsession data acted as the criterion as it is used to plan the volume and intensity of future training and is downloaded directly from the device. Twenty-five professional rugby league players completed 2 training sessions wearing an MEMS device (Catapult S5, firmware version: 5.27). During sessions, real-time data were collected through the manufacturer receiver and dedicated software (Openfield v1.14), which was positioned outdoors at the same location for every session. The GPS variables included total-, low- (0-3 m⋅ s-1), moderate- (3.1-5 m⋅ s-1), high- (5.1 -7 m⋅ s-1), and very high-speed (>7.1 m⋅ s-1) distances. Micro-electrical-mechanical-system data included total session PlayerLoad. When compared to postsession data, mean bias for total-, low-, moderate-, high-, and very high-speed distances were all trivial, with the typical error of the estimate (TEE) small, small, trivial, trivial and small, respectively. Pearson correlation coefficients for total-, low-, moderate-, high- and very-high-speed distances were nearly perfect, nearly perfect, perfect, perfect, and nearly perfect, respectively. For PlayerLoad, mean bias was trivial, whereas TEE was moderate and correlation nearly perfect. Practitioners should be confident that when interpreting real-time speed-derived metrics, the data generated in real-time are comparable with those downloaded directly from the device postsession. However, practitioners should refrain from interpreting accelerometer-derived data (i.e., PlayerLoad) or acknowledge the moderate error associated with this real-time measure. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Understanding the required training load to elicit improvements in intermittent running performance is a crucial process for strength and conditioning coaches. The aim was to investigate whether internal and external training load (TL) variables are related in a 'dose-response' manner with changes in 30-15 intermittent fitness test (30-15IFT) performance following a period of training. METHODS: Microtechnology data and session-rating-of-perceived-exertion (sRPE) were collected from 16 professional youth male rugby league players during a six-week pre-season period. The terminal running speed achieved during the 30-15IFT (v30-15IFT) prior to, and following the training period, represented a player's 'starting fitness' and 'end fitness' respectively. Derived from microtechnology, total-distance and PlayerLoad? were used to represent the 'global' external training load while sRPE represented the 'global' internal load. Total-distance was differentiated into the distance (m) covered at low- (SZ1; 0 to 3 m·s-1), moderate- (SZ2; 3.1 to 5 m·s-1), high- (SZ3; 5.1 to 7 m·s-1) and veryhigh- speeds (SZ4; > 7.1 m·s-1). The distance (m) covered above each player's individualised high-speed-threshold (> v30-15IFT [threshold range: 4.58 to 5.41 m·s-1]; IndSZ) was also determined. PlayerLoad was differentiated into low- (0 to 1 AU; PLZ1), moderate- (1.1 to 2 AU; PLZ2), high- (2.1 to 3 AU; PLZ3) and very-high (>3 AU; PLZ4) PlayerLoad?. A novel 'leave one out' partial least squares correlation analysis (PLSCA) was conducted to investigate which TL variables had the most influence on the shared information (inertia) with 'end fitness'. This involved creating a baseline PLSCA model with all TL variables and 'starting fitness' included and then systematically omitting each variable to understand the change in shared information and their subsequent variable importance. Variables deemed important were then included in a refined PLSCA and multiple linear regression model. RESULTS: The baseline PLSCA model was not significantly related with 'end fitness' (inertia: 18.76; P = 0.12). The findings of the PLSCA showed that a player's 'starting fitness' (change in inertia [CiI]: 4.14) and SZ4 (> 7 m·s-1; CiI: 3.61) distance (m) produced the greatest decrease in the measured inertia compared to baseline. This refined PLSCA model ('starting fitness', SZ3 and SZ4 distance) was significantly related (inertia: 17.54; P < 0.0001) with 30-15IFT 'end fitness'. Using multiple linear regression, this refined model explained a significant (P < 0.001) proportion of the variance (adjusted r2 = 0.73) in 30-15IFT 'end fitness'. DISCUSSION: The findings suggest that arbitrary 'high-intensity' speed-derived TL methods are related in a 'dose-response' manner that explain a large proportion of variability in the 'fitness' response while 'global', 'low-intensity' and 'individualised' TL methods (distance > v30-15IFT) do not. PRACTICAL APPLICATIONS: The accumulation of distance above 7 m·s-1, relative to a players starting fitness, appears important to understand changes in running performance following 6-weeks of training. Such analysis can be used to guide the prescription of load across a training period. For example, the refined regression model suggests that for a 1 km·hr-1 improvement in v30-15IFT following 6weeks of training, a player with a starting v30-15IFT of 18 km·hr-1 should target an accumulation of 450m above 7 m·s-1 compared to 800m for a player with a starting v3015IFT of 19.5 km·hr-1. [ABSTRACT FROM AUTHOR]