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Billy T. Hulin, Tim J. Gabbett, Simon Kearney and Alex Corvo


To quantify activity profiles in approximately 5-min periods to determine if the intensity of rugby league match play changes after the most intense period of play and to determine if the intensity of activity during predefined periods of match play differ between successful and less-successful teams playing at an elite standard.


Movement was recorded using a MinimaxX global positioning system (GPS) unit sampling at 10 Hz during 25 rugby league matches, equating to 200 GPS files. Data for each half of match play were separated into 8 equal periods. These periods represented the most intense phase of match play (peak period), the period after the most intense phase of match play (subsequent period), and the average demands of all other periods in a match (mean period). Two rugby league teams were split into a high-success and a low-success group based on their success rates throughout their season.


Compared with their less-successful counterparts, adjustables and hit-up forwards from the high-success team covered less total distance (P < .01) and less high-intensity-running distance (P < .01) and were involved in a greater number of collisions (P < .01) during the mean period of match play.


Although a greater number of collisions during match play is linked with a greater rate of success, greater amounts of high-intensity running and total distance are not related to competitive success in elite rugby league. These results suggest that technical and tactical differences, rather than activity profiles, may be the distinguishing factor between successful and less-successful rugby league teams.

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Billy T. Hulin, Tim J. Gabbett, Rich D. Johnston and David G. Jenkins

Purpose: To determine (1) how change-of-direction (COD) workloads influence PlayerLoad (PL) variables when controlling total distance covered and (2) relationships among collision workloads and PL variables during rugby league match play. Methods: Participants completed 3 protocols (crossover design) consisting of 10 repetitions of a 60-m effort in 15 s. The difference between protocols was the COD demands required to complete 1 repetition: no COD (straight line), 1° × 180° COD, or 3° × 180° COD. During rugby league matches, relationships among collision workloads, triaxial vector-magnitude PlayerLoad (PLVM), anteroposterior + mediolateral PL (PL2D), and PLVM accumulated at locomotor velocities below 2 m·s−1 (ie, PLSLOW) were examined using Pearson correlations (r) with coefficients of determination (R 2). Results: Comparing 3° × 180° COD to straight-line drills, PLVM·min−1 (d = 1.50 ± 0.49, large, likelihood = 100%, almost certainly), PL2D·min−1 (d = 1.38 ± 0.53, large, likelihood = 100%, almost certainly), and PLSLOW·min−1 (d = 1.69 ± 0.40, large, likelihood = 100%, almost certainly) were greater. Collisions per minute demonstrated a distinct (ie, R 2 < .50) relationship from PLVM·min−1 (R 2 = .30, r = .55) and PL2D·min−1 (R 2 = .37, r = .61). Total distance per minute demonstrated a very large relationship with PLVM·min−1 (R 2 = .62, r = .79) and PL2D·min−1 (R 2 = .57, r = .76). Conclusions: PL variables demonstrate (1) large increases as COD demands intensify, (2) separate relationships from collision workloads, and (3) moderate to very large relationships with total distance during match play. PL variables should be used with caution to measure collision workloads in team sport.

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Billy T. Hulin, Tim J. Gabbett, Nathan J. Pickworth, Rich D. Johnston and David G. Jenkins

Purpose: To examine relationships among physical performance, workload, and injury risk in professional rugby league players. Methods: Maximal-effort (n = 112) and submaximal (n = 1084) running performances of 45 players were recorded from 1 club over 2 consecutive seasons. Poorer and better submaximal running performance was determined by higher and lower exercise heart rates, respectively. Exponentially weighted moving averages and daily rolling averages were used to assess microtechnology-derived acute and chronic field-based workloads. The associations among within-individual submaximal running performance, workload, and noncontact lower-limb injury were then investigated. Results: The injury risk associated with poorer submaximal performance was “likely” greater than stable (relative risk = 1.8; 90% confidence interval, 0.9–3.7) and better submaximal performance (relative risk = 2.0; 90% confidence interval, 0.9–4.4). Compared with greater submaximal performance, poorer performance was associated with lower chronic workloads (effect size [d] = 0.82 [0.13], large) and higher acute:chronic workload ratios (d = 0.49 [0.14], small). Chronic workload demonstrated a “nearly perfect” positive relationship with maximal-effort running performance (exponentially weighted moving average, R 2 = .91 [.15]; rolling average, R 2 = .91 [.14]). At acute:chronic workload ratios >1.9, no differences in injury risk were found between rolling average and exponentially weighted moving average methods (relative risk = 1.1; 90% confidence interval, 0.3–3.8; unclear). Conclusions: Reductions in submaximal running performance are related with low chronic workloads, high acute:chronic workload ratios, and increased injury risk. These findings demonstrate that a submaximal running assessment can be used to provide information on physical performance and injury risk in professional rugby league players.