Neil Collins

Athlete external load is typically quantified as volumes or discretised threshold values using distance, speed and time. A framework accounting for the movement sequences of athletes has previously been proposed using radio frequency data. This study developed a framework to identify sequential movement sequences using GPS-derived spatiotemporal data in team-sports and establish its stability. Thirteen rugby league players during one match were analysed to demonstrate the application of the framework. The framework (Sequential Movement Pattern-mining [SMP]) applies techniques to analyse i) geospatial data (i.e., decimal degree latitude and longitude), ii) determine players turning angles, iii) improve movement descriptor assignment, thus improving movement unit formation and iv) improve the classification and identification of players’ frequent SMP. The SMP framework allows for sub-sequences of movement units to be condensed, removing repeated elements, which offers a novel technique for the quantification of similarities or dis-similarities between players and playing positions. The SMP framework provides a robust and stable method that allows, for the first time the analysis of GPS-derived data and identifies the frequent SMP of field-based team-sport athletes. The application of the SMP framework in practice could optimise the outcomes of training of field-based team-sport athletes by improving training specificity.

On-field spacing has been linked to successful performance in a number of sportsto date, there is limited research investigating this within rugby league. This study aims to (a) quantify the defensive dispersal during rugby league match-play and (b) identify if contextual factors are associated with the dispersal. Global Positioning System data were analysed from 47 European Super League matches (1598 player files). Defensive dispersal was calculated for 1959 defensive sets of rugby league. Linear mixed models were used to analyse the effects of contextual factors on the average defensive dispersal per set when accounting for team and fixture. On-field position and match half were found to significantly affect defensive dispersal. However, set length, play-the-ball length, and final score difference were found to have minimal impact on defensive dispersal. This study demonstrates that defensive dispersal in rugby league can be measured using GPS data and may be strongly influenced by on-field positioning. As such, it quantifies an important element of tactical preparation for rugby league teams.

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.

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.

Purpose: To compare the physical qualities between academy and international youth rugby league (RL) players using principal component analysis. Methods: Six hundred fifty-four males (age = 16.7 [1.4] y; height = 178.4 [13.3] cm; body mass = 82.2 [14.5] kg) from 11 English RL academies participated in this study. Participants completed anthropometric, power (countermovement jump), strength (isometric midthigh pull; IMTP), speed (10 and 40 m speed), and aerobic endurance (prone Yo-Yo IR1) assessments. Principal component analysis was conducted on all physical quality measures. A 1-way analysis of variance with effect sizes was performed on 2 principal components (PCs) to identify differences between academy and international backs, forwards, and pivots at under 16 and 18 age groups. Results: Physical quality measures were reduced to 2 PCs explaining 69.4% of variance. The first PC (35.3%) was influenced by maximum and 10-m momentum, absolute IMTP, and body mass. Ten and forty-meter speed, body mass and fat, prone Yo-Yo, IMTP relative, maximum speed, and countermovement jump contributed to PC2 (34.1%). Significant differences (P < .05, effect size = −1.83) were identified between U18 academy and international backs within PC1. Conclusion: Running momentum, absolute IMTP, and body mass contributed to PC1, while numerous qualities influenced PC2. The physical qualities of academy and international youth RL players are similar, excluding U18 backs. Principal component analysis can reduce the dimensionality of a data set and help identify overall differences between playing levels. Findings suggest that RL practitioners should measure multiple physical qualities when assessing physical performance.

The measurement, analysis, and reporting of physical qualities within sport is vital for practitioners to support athlete development. However, several challenges exist to support this process (e.g., establishing comparative data, managing large data sets) within sport. This article presents 7 challenges associated with physical testing in sport and offers solutions to overcome them. These solutions are supported by a description of the Profiling Physical Qualities (ProPQ) tool. The ProPQ tool uses advanced data analysis, visualization, and interactive elements, to enhance stakeholders' use of data to optimize player development and coaching practices. The ProPQ is currently used across rugby league in England.

Objectives Report two-years of training injury data in senior and academy professional rugby league. Design Prospective cohort study. Method Match and training time-loss injuries and exposure data were recorded from two-seasons of the European Super League competition. Eleven/12 (2021) and 12/12 (2022) senior and 8/12 (2021) and 12/12 (2022) academy teams participated. Training injuries are described in detail and overall match injuries referred to for comparison only. Results 224,000 training exposure hours were recorded with 293 injuries at the senior (mean [95 % confidence interval]; 3[2–3] per 1000 h) and 268 academy level (2 [2–3] per 1000 h), accounting for 31 % and 40 % of all injuries (i.e., matches and training). The severity of training injuries (senior: 35 [30–39], academy: 36 [30–42] days-lost) was similar to match injuries. Lower-limb injuries had the greatest injury incidence at both levels (senior: 1.85 [1.61–2.12], academy: 1.28 [1.08–1.51] per 1000 h). Head injuries at the academy level had greater severity (35 [25–45] vs. 18 [12–14] days-lost; p < 0.01) and burden (17 [16–18] vs. 4[4–5] days-lost per 1000 h; p = 0.02) than senior level. At the senior level, the incidence of contact injuries was lower than non-contact injuries (risk ratio: 0.29 [0.09–0.88], p = 0.02). Conclusion Training injuries accounted for about a third of injuries, with similar injury severity to match-play. Within training there is a higher rate of non-contact vs. contact injuries. Whilst current injury prevention interventions target matches, these data highlight the importance of collecting high quality training injury data to develop and evaluate injury prevention strategies in training

Objectives To compare match injury incidence, severity and burden in men's and women's elite rugby league. Design A prospective cohort epidemiological study. Methods Time loss match injury data were collected from all men's (11,301 exposure hours) and women's (5,244 exposure hours) Super League clubs. Results Injury incidence and burden were not different between men and women (mean [95 % CI]; 54 [45 to 65] vs. 60 [49 to 74] per 1000 match-hours; p = 0.39, and 2332 [1844 to 2951] vs. 1951 [1560 to 2440] days lost per 1000 match-hours; p = 0.26). However, injury severity was greater for men than women (42 [35–50] vs. 35 [29 to 42]; p = 0.01). Lower limbs accounted for 54 % and 52 % of injuries for men and women, with the head/face the most frequently injured location due to concussion (12 [10 to 15] and 10 [8 to 14] per 1000 match-hours for men and women). Injuries to the knee had the greatest burden for men and women (708 [268–1868] and 863 [320–2328] days lost per 1000 match-hours). Being tackled was the most common injury mechanism for men and women (28 % and 38 %) with greater burden (p < 0.01) than other injury mechanisms. Conclusions Male and female rugby league players have similar injury incidence and burden; however, injury severity was higher in men. Head/face injuries have the highest injury incidence and knee injuries have the highest burden. These injuries should be the focus for prevention initiatives at a league (via laws), player, and coach level, with equal and specific focus for both men's and women's rugby league players.

Participation in women’s rugby league has been growing since the foundation of the English women’s rugby league Super League in 2017. However, the evidence base to inform women’s rugby league remains sparse. This study provides the largest quantification of anthropometric and physical qualities of women’s rugby league players to date, identifying differences between positions (forwards & backs) and playing level (Women’s Super League [WSL] vs. International). The height, weight, body composition, lower body strength, jump height, speed and aerobic capacity of 207 players were quantified during the pre-season period. Linear mixed models and effects sizes were used to determine differences between positions and levels. Forwards were significantly (p < 0.05) heavier (forwards: 82.5 ± 14.8kg; backs: 67.7 ± 9.2kg) and have a greater body fat % (forwards: 37.7 ± 6.9%; backs: 30.4 ± 6.3%) than backs. Backs had significantly greater lower body power measured via jump height (forwards: 23.5 ± 4.4cm; backs: 27.6 ± 4.9cm), speed over 10m (forwards: 2.12 ± 0.14s; backs: 1.98 ± 0.11s), 20m (forwards: 3.71 ± 0.27s; backs: 3.46 ± 0.20s), 30m (forwards: 5.29 ± 0.41s; backs: 4.90 ± 0.33s), 40m (forwards: 6.91 ± 0.61s; backs: 6.33 ± 0.46s) and aerobic capacity (forwards: 453.4 ± 258.8m; backs: 665.0 ± 298.2m) than forwards. Additionally, international players were found to have greater anthropometric and physical qualities in comparison to their WSL counterparts. This study adds to the limited evidence base surrounding the anthropometric and physical qualities of elite women’s rugby league players. Comparative values for anthropometric and physical qualities are provided which practitioners may use to evaluate the strengths and weaknesses of players, informing training programs to prepare players for the demands of women’s rugby league.

This is the first study to assess longitudinal changes in anthropometric, physiological, and physical qualities of international women’s rugby league players. Thirteen forwards and 11 backs were tested three times over a 10-month period. Assessments included: standing height and body mass, body composition measured by dual x-ray absorptiometry (DXA), a blood panel, resting metabolic rate (RMR) assessed by indirect calorimetry, aerobic capacity (i.e.,V˙O2max) evaluated by an incremental treadmill test, and isometric force production measured by a force plate. During the pre-season phase, lean mass increased significantly by ~2% for backs (testing point 1: 47 kg; testing point 2: 48 kg) and forwards (testing point 1: 50 kg; testing point 2: 51 kg) (p = ≤ 0.05). Backs significantly increased their V˙O2max by 22% from testing point 1 (40 ml kg-1 min-1) to testing point 3 (49 ml kg-1 min-1) (p = ≤ 0.04). The V˙O2max of forwards increased by 10% from testing point 1 (41 ml kg-1 min-1) to testing point 3 (45 ml kg-1 min-1), however this change was not significant (p = ≥ 0.05). Body mass (values represent the range of means across the three testing points) (backs: 68 kg; forwards: 77–78 kg), fat mass percentage (backs: 25–26%; forwards: 30–31%), resting metabolic rate (backs: 7 MJ day-1; forwards: 7 MJ day-1), isometric mid-thigh pull (backs: 2106–2180 N; forwards: 2155–2241 N), isometric bench press (backs: 799–822 N; forwards: 999–1024 N), isometric prone row (backs: 625–628 N; forwards: 667–678 N) and bloods (backs: ferritin 21–29 ug/L, haemoglobin 137–140 g/L, iron 17–21 umol/L, transferrin 3 g/L, transferring saturation 23–28%; forwards: ferritin 31–33 ug/L, haemoglobin 141–145 g/L, iron 20–23 umol/L, transferrin 3 g/L, transferrin saturation 26–31%) did not change (p = ≥ 0.05). This study provides novel longitudinal data which can be used to better prepare women rugby league players for the unique demands of their sport, underpinning female athlete health.