A league-wide investigation into variability of rugby league match running from 322 Super League games

Validation of field-based running tests to determine maximal aerobic speed in professional rugby league

Practitioners place importance on high-speed running (HSR) to monitor training practice and match-play demands, whilst attempting to maximise fitness and minimize the risk of injury occurrence. Practitioners apply various methods to quantify HSR, such as absolute thresholds, percentage of maximum sprint speed and maximal aerobic speed (MAS). A recent survey demonstrates the 5-minute run and 1200m shuttle test (ST) to be implemented among rugby league practitioners to quantify HSR by incorporating MAS. However, it is unclear as to how valid these methods are to accurately quantify MAS. Therefore, the aim of this study was to assess the validity of the 5-minute run and 1200m ST when compared to a gold standard measure for MAS. Twenty 1st team professional rugby league players competing in the European Super League participated in this study. Players were required to complete an incremental treadmill test, 5-minute run and 1200m ST over a two-week period in pre-season. MAS, peak heart rate (HRmax), peak lactate (Lapeak) and rating of perceived exertion (RPE) where collected upon completion of each test. Results demonstrated the 1200m ST to have a higher correlation for MAS than the 5-minute run (1200m ST: r = 0.73, 5-minute run: r = 0.64). However, when assessing validity using the level of agreement between data, the 5-minute run underreported MAS by 0.45 m·s-1 whereas the 1200m ST underreported MAS by 0.77 m·s-1. Ultimately, both field-based tests used in this study underreport MAS when compared to an incremental treadmill test, although the 5-minute run provides a closer agreement and therefore a more valid measurement for MAS than the 1200m ST.

The physical, technical and tactical demands of on-field training drills in professional Rugby league: a systematic scoping review

OBJECTIVES

The main objectives of this scoping review were to conduct a systematic search on the physical, technical and tactical demands of rugby league training, consolidate and summarise key findings and identify any existing gaps in knowledge.

METHODS

A systematic online search of Scopus, PubMed, MEDLINE and SPORTDiscus was conducted from earliest record to 6 August 2023 and supplemented by manually searching reference lists. The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist was followed. Studies were eligible for inclusion if they investigated the physical, technical and/or tactical demands of rugby league training within all levels of competition and included either male or female participants.

RESULTS

The initial search yielded 637 papers, 25 of which were included in the review. Of these studies, the majority (n = 19) exclusively examined the physical demands of training, one paper exclusively examined the technical demands of training, five studies included both physical and technical demands, and no studies examined the tactical demands of training. Small-sided games was the most prevalent drill included within investigations examining the physical and technical demands of various rugby league training drills.

CONCLUSIONS

The present review was the first to scope peer-reviewed literature on the multifaceted demands (i.e. physical, technical and tactical) demands of rugby league training. It is apparent that this area is under researched, specifically in literature examining the technical and tactical elements of rugby league training.

Need a break? The locomotor and tackle pacing profile and loads of women's rugby league match-play following various between-match turnaround durations

OBJECTIVES

The study investigated the locomotor and tackle pacing profile and loads of female rugby league players following various between-match turnaround durations. Specifically, the study examined the (1) pacing of locomotor and tackle loads across the time-course of a match and; (2) whole-match and peak locomotor and tackle loads of match-play.

METHODS

Microtechnology data were collected from elite female rugby league players (n = 172) representing all National Rugby League Women's teams (n = 6 teams) across two seasons. Players were categorised into backs, adjustables, forwards or interchange players. Data was calculated for the whole-match (m), per minute (m.min-1) and peak (running: m.min-1; acceleration: m.s-2) locomotor and tackle loads (number and efficiency (%)) of match-play. The pacing as well as the locomotor and tackle loads of match-play were examined following short (≤6 days), normal (7 days) or long (≥8 days) turnarounds.

RESULTS

The pacing profile of playing positions varied across short, normal and long match turnarounds. Trivial to moderate differences existed in the whole-match, per minute and peak locomotor loads across match turnaround durations (effect size ≤ 1.2).

CONCLUSIONS

Following various between-match turnaround durations (i.e., short, normal and long match turnarounds), there were variations in the locomotor and tackle pacing profile and loads whereby, the pacing profile of positional groups was more affected than the load profile. The findings can be used in applied settings to guide the recovery strategies and training plans of female rugby league players to optimise performance and wellbeing across various match turnaround durations.

Changing gears: data-driven velocity zones to support monitoring and research in men's rugby league

OBJECTIVES

The study aimed to (1) apply a data-mining approach to league-wide microtechnology data to identify absolute velocity zone thresholds and (2) apply the respective velocity zones to microtechnology data to examine the locomotor demands of elite match-play.

METHODS

League-wide microtechnology data were collected from elite male rugby league players representing all National Rugby League (NRL) teams (n = 16 teams, one excluded due to a different microtechnology device; n = 4836 files) over one season. To identify four velocity zones, spectral clustering with a beta smoothing cut-off of 0.1 was applied to each players' instantaneous match-play velocity data. Velocity zones for each player were calculated as the median while the overarching velocity zones were determined through an incremental search to minimise root mean square error.

RESULTS

The velocity zones identified through spectral clustering were 0-13.99 km · h-1 (i.e., low velocity), 14.00-20.99 km · h-1 (i.e., moderate velocity), 21.00-24.49 km · h-1 (i.e., high velocity) and >24.50 km · h-1 (i.e., very-high velocity).

CONCLUSIONS

The application of spectral clustering (i.e., a data-mining method) to league-wide rugby league microtechnology data yielded insights into the distribution of velocity data, thereby informing the cut-off values which best place similar data points into the same velocity zones. As the identified zones are representative of the intensities of locomotion achieved by elite male rugby league players, it is suggested that when absolute zones are used, the consistent application of the identified zones would facilitate standardisation, longitudinal athlete monitoring as well as comparisons between teams, leagues and published literature.

Individual and situational factors affecting the movement characteristics and internal responses to Touch match-play during an international tournament

PURPOSE

To examine the influence of individual and situational factors on the movement characteristics and internal responses of players to an international Touch tournament.

METHODS

Using 47 International Touch players (25 men and 22 women), the associations between the movement characteristics and internal responses with individual (sprint, glycolytic test, Yo-Yo intermittent recovery test level 1 [Yo-Yo IR1], jump performance and well-being) and situational (sex, squad, position, competition day, points scored/conceded, result, and opposition rank) factors were examined using linear mixed modelling.

RESULTS

Yo-Yo IR1 distance was associated with all movement characteristics and internal responses (r = -0.29 to 0.37), whilst sprint and glycolytic times only influenced mean heart rate (HRmean) (r = 0.15) and high-speed distance (r = 0.10), respectively. Sex influenced high-speed distance (r = -0.41), whilst squad was associated with playing time and HRmean (r = -0.10-0.33). Other associations included: playing position with all movement characteristics (r = -0.67-0.81); points conceded with relative distance (r = -0.14); winning with high metabolic power and session RPE (r = -0.07-0.09), and opposition rank with HRmean and RPE (r = 0.11-0.35).

CONCLUSIONS

Individual and situational factors can influence the movement characteristics and internal responses to Touch and should be considered when developing the characteristics of players and interpreting responses to match-play.

The speed and acceleration of the ball carrier and tackler into contact during front-on tackles in rugby league

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.

Moving beyond velocity derivatives; using global positioning system data to extract sequential movement patterns at different levels of rugby league match-play

This study aims to (a) quantify the movement patterns during rugby league match-play and (b) identify if differences exist by levels of competition within the movement patterns and units through the sequential movement pattern (SMP) algorithm. Global Positioning System data were analysed from three competition levels; four Super League regular (regular-SL), three Super League (semi-)Finals (final-SL) and four international rugby league (international) matches. The SMP framework extracted movement pattern data for each athlete within the dataset. Between competition levels, differences were analysed using linear discriminant analysis (LDA). Movement patterns were decomposed into their composite movement units; then Kruskal-Wallis rank-sum and Dunn post-hoc were used to show differences. The SMP algorithm found 121 movement patterns comprised mainly of "walk" and "jog" based movement units. The LDA had an accuracy score of 0.81, showing good separation between competition levels. Linear discriminant 1 and 2 explained 86% and 14% of the variance. The Kruskal-Wallis found differences between competition levels for 9 of 17 movement units. Differences were primarily present between regular-SL and international with other combinations showing less differences. Movement units which showed significant differences between competition levels were mainly composed of low velocities with mixed acceleration and turning angles. The SMP algorithm found 121 movement patterns across all levels of rugby league match-play, of which, 9 were found to show significant differences between competition levels. Of these nine, all showed significant differences present between international and domestic, whereas only four found differences present within the domestic levels. This study shows the SMP algorithm can be used to differentiate between levels of rugby league and that higher levels of competition may have greater velocity demands.Highlights This study shows that movement patterns and movement units can be used to investigate team sports through the application of the SMP frameworkOne hundred and twenty-one movement patterns were found to be present within rugby league match-play, with the walk- and jog-based movement units most prevalent. No movement pattern was unique to a single competition level.Further analysis revealed that the majority of movement units analysed had significant differences between international and domestic rugby league, whereas only four movement units (i.e. f,m,n,q) had significant differences within the two domestic rugby league levels.International rugby league had higher occurrences of the movement patterns consisting of higher velocity movement units (ie. T,S,y). This suggests that international rugby league players may need greater high velocity exposure in training.

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.

The Maximal Intensity Period: Rationalising its Use in Team Sports Practice

Quantifying the highest intensity of competition (the maximal intensity period [MIP]) for varying durations in team sports has been used to identify training targets to inform the preparation of players. However, its usefulness has recently been questioned since it may still underestimate the training intensity required to produce specific physiological adaptations. Within this conceptual review, we aimed to: (i) describe the methods used to determine the MIP; (ii) compare the data obtained using MIP or whole-match analysis, considering the influence of different contextual factors; (iii) rationalise the use of the MIP in team sports practice and (iv) provide limitations and future directions in the area. Different methods are used to determine the MIP, with MIP values far greater than those derived from averaging across the whole match, although they could be affected by contextual factors that should be considered in practice. Additionally, while the MIP might be utilised during sport-specific drills, it is inappropriate to inform the intensity of interval-based, repeated sprint and linear speed training modes. Lastly, MIP does not consider any variable of internal load, a major limitation when informing training practice. In conclusion, practitioners should be aware of the potential use or misuse of the MIP.

How fast is fast? Defining Velocity Zones in Women's Rugby League

OBJECTIVES

The study aimed to: 1) apply a data-mining approach to identify velocity zone thresholds for female rugby league players and; 2) apply these velocity zones to examine the locomotor demands of match-play.

METHODS

Microtechnology data were collected from elite female rugby league players representing all National Rugby League Women's teams (n=85 players; n=224 files) over one season. Spectral clustering with a beta smoothing cut-off of 0.1 was applied to each player's instantaneous match-play velocity data for the identification of four zones. To account for outliers within repeated data-points, the velocity zones for each player were calculated as the median. The overarching velocity zones were determined through an incremental search to minimise the root mean square error.

RESULTS

Through a data-mining approach, four velocity zones were determined. Rounded to the nearest 0.5 km.h^-1 the velocity values across each zone were classified as low (0 to 11.49 km.h^-1), moderate (11.50 to 17.49 km.h^-1), high (17.50 to 20.99 km.h^-1) and very-high (>21.00 km.h^-1). Practical application of the zones demonstrated positional group differences in the absolute (effect size (ES):-0.03 to 1.77) and relative (ES: -0.04 to 1.60) locomotor demands of match-play. The back positional group covered greater absolute and relative distances at a very-high velocity than all other positions.

CONCLUSIONS

This work informs the velocity zones that could be applied consistently to women's rugby league data within practical (i.e. in the training and monitoring of players) and academic (i.e. as a model for future research to analyse locomotor demands) settings.

SARS-CoV-2 transmission during rugby league matches: do players become infected after participating with SARS-CoV-2 positive players?

OBJECTIVES

To examine the interactions between SARS-CoV-2 positive players and other players during rugby league matches and determine within-match SARS-CoV-2 transmission risk.

METHODS

Four Super League matches in which SARS-CoV-2 positive players were subsequently found to have participated were analysed. Players were identified as increased-risk contacts, and player interactions and proximities were analysed by video footage and global positioning system (GPS) data. The primary outcome was new positive cases of SARS-CoV-2 within 14 days of the match in increased-risk contacts and other players participating in the matches.

RESULTS

Out of 136 total players, there were 8 SARS-CoV-2 positive players, 28 players identified as increased-risk contacts and 100 other players in the matches. Increased-risk contacts and other players were involved in 11.4±9.0 (maximum 32) and 4.0±5.2 (maximum 23) tackles, respectively. From GPS data, increased-risk contacts and other players were within 2 m of SARS-CoV-2 positive players on 10.4±18.0 (maximum 88) and 12.5±20.7 (maximum 121) occasions, totalling 65.7±137.7 (maximum 689) and 89.5±169.4 (maximum 1003) s, respectively. Within 14 days of the match, one increased-risk contact and five players returned positive SARS-CoV-2 reverse transcriptase PCR (RT-PCR) tests, and 27 increased-risk contacts and 95 other participants returned negative SARS-CoV-2 RT-PCR tests. Positive cases were most likely traced to social interactions, car sharing and wider community transmission and not linked to in-match transmission.

CONCLUSION

Despite tackle involvements and close proximity interactions with SARS-CoV-2 positive players, in-match SARS-CoV-2 transmission was not confirmed. While larger datasets are needed, these findings suggest rugby presents a lower risk of viral transmission than previously predicted.

Women's Rugby League: Positional Groups and Peak Locomotor Demands

The aims of this study were to (a) use a data-based approach to identify positional groups within National Rugby League Women's (NRLW) match-play and (b) quantify the peak locomotor demands of NRLW match-play by positional groups. Microtechnology (Global Navigational Satellite System [GNSS] and integrated inertial sensors; n = 142 files; n = 76 players) and match statistics (n = 238 files; n = 80 players) were collected from all NRLW teams across the 2019 season. Data-based clustering of match statistics was utilized to identify positional clusters through classifying individual playing positions into distinct positional groups. Moving averages (0.5, 1, 2, 3, 5, and 10 min) of peak running and average acceleration/deceleration demands were calculated via microtechnology data for each player per match. All analysis was undertaken in R (R Foundation for Statistical Computing) with positional differences determined via a linear mixed model and effect sizes (ES). Data-based clustering suggested that, when informed by match statistics, individual playing positions can be clustered into one of three positional groups. Based on the clustering of the individual positions, these groups could be broadly defined as backs (fullback, wing, and center), adjustables (halfback, five-eighth, and hooker), and forwards (prop, second-row, and lock). Backs and adjustables demonstrated greater running (backs: ES 0.51-1.00; p < 0.05; adjustables: ES 0.51-0.74, p < 0.05) and average acceleration/deceleration (backs: ES 0.48-0.87; p < 0.05; adjustables: ES 0.60-0.85, p < 0.05) demands than forwards across all durations. Smaller differences (small to trivial) were noted between backs and adjustables across peak running and average acceleration/deceleration demands. Such findings suggest an emerging need to delineate training programs in situations in which individual playing positions train in positional group based settings. Collectively, this work informs the positional groupings that could be applied when examining NRLW data and supports the development of a framework for specifically training female rugby league players for the demands of the NRLW competition.