Energetic Demands of Interchange and Full-Match Rugby League Players

The introduction of the six-again rule has increased acceleration intensity across all positions in the National Rugby League competition

The impact of the six-again rule change on the movement of National Rugby League (NRL) athletes was examined. Player Global Navigation Satellite System (GNSS) data (10 Hz) was collected from 42 athletes who competed in 56 matches across the 2019 to 2021 NRL seasons. Maximal mean speed (m·min^-1) and acceleration (m·s^-2) were established across a 10 s to 10-min duration via raw GNSS files, with subsequent intercept (mean estimates) and slope values determined via power law analysis. The distributions of match distance (m) and impulse (kN·s^-1) were established during ball-in-play time. To determine the significance between positions and seasons under different rules, linear mixed models were used. Effects were described using standardised effect sizes (ES) with 90% confidence limits (CL). Acceleration intercepts (power law-derived) across all positions were substantially greater (>0.6 SD) following the introduction of the six-again rule in the 2020 (mean ± SD; 1.02 ± 0.10 m·s^-2) and 2021 seasons (1.05 ± 0.08 m·s^-2) compared to the 2019 season (0.91 ± 0.07 m·s^-2). Mean acceleration during ball-in-play time was greater in 2020 (ES; 90% CL = 0.75; ± 0.32) compared to 2019. The acceleration requirements of rugby league increased across all positional groups following the modification in NRL competition rules. Practitioners should tailor training programs for athletes to reflect the increased acceleration intensity found under the revised competition format.

Techniques to derive and clean acceleration and deceleration data of athlete tracking technologies in team sports: A scoping review

The application of acceleration and deceleration data as a measure of an athlete's physical performance is common practice in team sports. Acceleration and deceleration are monitored with athlete tracking technologies during training and games to quantify training load, prevent injury and enhance performance. However, inconsistencies exist throughout the literature in the reported methodological procedures used to quantify acceleration and deceleration. The object of this review was to systematically map and provide a summary of the methodological procedures being used on acceleration and deceleration data obtained from athlete tracking technologies in team sports and describe the applications of the data. Systematic searches of multiple databases were undertaken. To be included, studies must have investigated full body acceleration and/or deceleration data of athlete tracking technologies. The search identified 276 eligible studies. Most studies (60%) did not provide information on how the data was derived and what sequence of steps were taken to clean the data. Acceleration and deceleration data were commonly applied to quantify and describe movement demands using effort metrics. This scoping review identified research gaps in the methodological procedures and deriving and cleaning techniques that warrant future research focussing on their effect on acceleration and deceleration data.

Relationship between training load and recovery in collegiate American football players during pre-season training

Background: The purpose of this study was to examine the relationship between training load and next-day recovery in collegiate American football (AF) players during pre-season.Methods: Seventeen athletes (Linemen, n = 6; Non-linemen, n = 11) participated in the 14-day study wearing monitoring (accelerometer + heart rate) sensors during on-field practice sessions throughout pre-season to assess the physiological (PL), mechanical load (ML) and recording of session RPE (sRPE load) immediately post-practice. Prior to practice, participants completed a drop-jump reactive strength index (RSI) test and reported perceived recovery status (PRS). Loaded counter movement vertical jump was assessed before and after pre-season.Results: For every one unit increase in sRPE load, RSI declined by 0.03. Non-linemen had a lower RSI value of 73.1 units compared to linemen. For every one unit increase in ML, the PRS decreased by 0.01. Non-linemen recorded higher average ML during week 2 (ES = 1.17) compared to linemen. Non-linemen recorded higher RSI values in weeks 1 (ES = -1.41) and 2 (ES = -1.72) compared to linemen. All training load and recovery parameters were lower week 2 compared to week 1 (p < 0.05) for all players.Conclusions: Next-day RSI values were influenced by sRPE load while next-day PRS appears to be more influenced by ML. No difference in PL or sRPE load was observed been groups despite non-linemen completing a higher ML throughout the preseason. A combination of training load and recovery metrics may be needed to monitor the fatigue and state of readiness of each player.

The Quantification of Acceleration Events in Elite Team Sport: a Systematic Review

BACKGROUND

Wearable tracking devices are commonly utilised to quantify the external acceleration load of team sport athletes during training and competition. The ability to accelerate is an important attribute for athletes in many team sports. However, there are many different acceleration metrics that exist in team sport research. This review aimed to provide researchers and practitioners with a clear reporting framework on acceleration variables by outlining the different metrics and calculation processes that have been adopted to quantify acceleration loads in team sport research.

METHODS

A systematic review of three electronic databases (CINAHL, MEDLINE, SPORTDiscus), was performed to identify peer-reviewed studies that published external acceleration load in elite team sports during training and/or competition. Articles published between January 2010 and April 2020 were identified using Boolean search phrases in relation to team sports (population), acceleration/deceleration (comparators), and competition and/or training (outcome). The included studies were required to present external acceleration and/or deceleration load (of any magnitude) from able-bodied athletes (mean age ≥ 18 years) via wearable technologies.

RESULTS

A total of 124 research articles qualified for inclusion. In total, 113/124 studies utilised GPS/GNSS technology to outline the external acceleration load of athletes. Count-based metrics of acceleration were predominant of all metrics in this review (72%). There was a lack of information surrounding the calculation process of acceleration with 13% of studies specifying the filter used in the processing of athlete data, whilst 32% outlined the minimum effort duration (MED). Markers of GPS/GNSS data quality, including horizontal dilution of precision (HDOP) and the average number of satellites connected, were outlined in 24% and 27% of studies respectively.

CONCLUSIONS

Team sport research has predominantly quantified external acceleration load in training and competition with count-based metrics. Despite the influence of data filtering processes and MEDs upon acceleration, this information is largely omitted from team sport research. Future research that outlines acceleration load should present filtering processes, MEDs, HDOP, and the number of connected satellites. For GPS/GNSS systems, satellite planning tools should document evidence of available satellites for data collection to analyse tracking device performance. The development of a consistent acceleration filtering method should be established to promote consistency in the research of external athlete acceleration loads.

Movement Patterns and Match Statistics in the National Rugby League Women's (NRLW) Premiership

As women's rugby league grows, the need for understanding the movement patterns of the sport is essential for coaches and sports scientists. The aims of the present study were to quantify the position-specific demographics, technical match statistics, and movement patterns of the National Rugby League Women's (NRLW) Premiership and to identify whether there was a change in the intensity of play as a function of game time played. A retrospective observational study was conducted utilizing global positioning system, demographic, and match statistics collected from 117 players from all NRLW clubs across the full 2018 and 2019 seasons and were compared between the ten positions using generalized linear mixed models. The GPS data were separated into absolute (i.e., total distance, high-speed running distance, and acceleration load) and relative movement patterns (i.e., mean speed, mean high speed (> 12 km·h^-1), and mean acceleration). For absolute external outputs, fullbacks covered the greatest distance (5,504 m), greatest high-speed distance (1,081 m), and most ball-carry meters (97 m), while five-eighths recorded the greatest acceleration load (1,697 m·s^-2). For relative external outputs, there were no significant differences in mean speed and mean high speed between positions, while mean acceleration only significantly differed between wingers and interchanges. Only interchange players significantly decreased in mean speed as their number of minutes played increased. By understanding the load of NRLW matches, coaches, high-performance staff, and players can better prepare as the NRLW Premiership expands. These movement patterns and match statistics of NRLW matches can lay the foundation for future research as women's rugby league expands. Similarly, coaches, high-performance staff, and players can also refine conditioning practices with a greater understanding of the external output of NRLW players.

The Demands of Professional Rugby League Match-Play: a Meta-analysis

Background

Rugby league is a collision sport, where players are expected to be physically competent in a range of areas, including aerobic fitness, strength, speed and power. Several studies have attempted to characterise the physical demands of rugby league match-play, but these studies often have relatively small sample sizes based on one or two clubs, which makes generalisation of the findings difficult. Therefore, the aim of this review was to synthesise studies that investigated the physical demands of professional rugby league match-play.

Methods

SPORTDiscus, CINAHL, MEDLINE (EBSCO) and Embase (EBSCO) databases were systematically searched from inception until October 2018. Articles were included if they (1) recruited professional rugby league athletes aged ≥ 18 years and (2) provided at least one match-play relevant variable (including playing time, total and relative distance, repeat high-intensity efforts (RHIE), efforts per RHIE, accelerations and decelerations, total and relative collisions). Meta-analyses were used to provide pooled estimates ± 95% confidence intervals.

Results

A total of 30 studies were included. Pooled estimates indicated that, compared to adjustables and backs, forwards have less playing time (− 17.2 ± 5.6 and − 25.6 ± 5.8 min, respectively), cover less ‘slow-speed’ (− 2230 ± 735 and − 1348 ± 655 m, respectively) and ‘high-speed’ distance (− 139 ± 108 and − 229 ± 101 m, respectively), but complete more relative RHIEs (+ 0.05 ± 0.05 and + 0.08 ± 0.04 per minute, respectively), and total (+ 12.0 ± 8.1 and + 12.8 ± 7.2 collisions, respectively) and relative collisions (+ 0.32 ± 0.22 and + 0.41 ± 0.22 collisions per minute, respectively). Notably, when the distance was expressed relative to playing time, forwards were not different from adjustables and backs in slow-speed (P ≥ 0.295) and high-speed (P ≥ 0.889) relative distance. The adjustables and backs subgroups were similar in most variables, except playing time (shorter for adjustables, − 8.5 ± 6.2 min), slow-speed distance (greater for adjustables, + 882 ± 763 m) and total relative distance (greater for adjustables, + 11.3 ± 5.2 m·min⁻¹). There were no significant differences between positional groups for efforts per RHIE, accelerations and decelerations (P ≥ 0.745).

Conclusions

These results indicate the unique physical demands of each playing position and should be considered by strength and conditioning and tactical coaches when planning for professional rugby league performance.

Protocol Registration

https://osf.io/83tq2/

Electronic supplementary material

The online version of this article (10.1186/s40798-019-0197-9) contains supplementary material, which is available to authorized users.