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Cloe Cummins and Rhonda Orr

Objective:

To investigate the impact forces of collision events during both attack and defense in elite rugby league match play and to compare the collision profiles between playing positions.

Participants:

26 elite rugby league players.

Methods:

Player collisions were recorded using an integrated accelerometer in global positioning system units (SPI-Pro X, GPSports). Impact forces of collisions in attack (hit-ups) and defense (tackles) were analyzed from 359 files from outside backs (n = 78), adjustables (n = 97), wide-running forwards (n = 136), and hit-up forwards (n = 48) over 1 National Rugby League season.

Results:

Hit-up forwards were involved in 0.8 collisions/min, significantly more than all other positional groups (wide-running forwards P = .050, adjustables P = .042, and outside backs P = .000). Outside backs experienced 25% fewer collisions per minute than hit-up forwards. Hit-up forwards experienced a collision within the 2 highest classifications of force (≥10 g) every 2.5 min of match play compared with 1 every 5 and 9 min for adjustables and outside backs, respectively. Hit-up forwards performed 0.5 tackles per minute of match play, 5 times that of outside backs (ES = 1.90; 95% CI [0.26,3.16]), and 0.2 hit-ups per minute of match play, twice as many as adjustables.

Conclusions:

During a rugby league match, players are exposed to a significant number of collision events. Positional differences exist, with hit-up and wide-running forwards experiencing greater collision events than adjustables and outside backs. Although these results may be unique to the individual team’s defensive- and attacking-play strategies, they are indicative of the significant collision profiles in professional rugby league.

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Cloe Cummins, Blake McLean, Mark Halaki and Rhonda Orr

Purpose:

To quantify the external training loads of positional groups in preseason training drills.

Methods:

Thirty-three elite rugby league players were categorized into 1 of 4 positional groups: outside backs (n = 9), adjustables (n = 9), wide-running forwards (n = 9), and hit-up forwards (n = 6). Data for 8 preseason weeks were collected using microtechnology devices. Training drills were classified based on drill focus: speed and agility, conditioning, and generic and positional skills.

Results:

Total, high-speed, and very-high-speed distance decreased across the preseason in speed and agility (moderate, small, and small, respectively), conditioning (large, large, and small) and generic skills (large, large, and large). The duration of speed and generic skills also decreased (77% and 48%, respectively). This was matched by a concomitant increase in total distance (small), high-speed running (small), very-high-speed running (moderate), and 2-dimensional (2D) BodyLoad (small) demands in positional skills. In positional skills, hit-up forwards (1240 ± 386 m) completed less very-high-speed running than outside backs (2570 ± 1331 m) and adjustables (2121 ± 1163 m). Hit-up forwards (674 ± 253 AU) experienced greater 2D BodyLoad demands than outside backs (432 ± 230 AU, P = .034). In positional drills, hit-up forwards experienced greater relative 2D BodyLoad demands than outside backs (P = .015). Conversely, outside backs experienced greater relative high- (P = .007) and very-high-speed-running (P < .001) demands than hit-up forwards.

Conclusion:

Significant differences were observed in training loads between positional groups during positional skills but not in speed and agility, conditioning, and generic skills. This work also highlights the importance of different external-load parameters to adequately quantify workload across different positional groups.

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Tom Kempton, Anita Claire Sirotic, Ermanno Rampinini and Aaron James Coutts

Purpose:

To describe the metabolic demands of rugby league match play for positional groups and compare match distances obtained from high-speed-running classifications with those derived from high metabolic power.

Methods:

Global positioning system (GPS) data were collected from 25 players from a team competing in the National Rugby League competition over 39 matches. Players were classified into positional groups (adjustables, outside backs, hit-up forwards, and wide-running forwards). The GPS devices provided instantaneous raw velocity data at 5 Hz, which were exported to a customized spreadsheet. The spreadsheet provided calculations for speed-based distances (eg, total distance; high-speed running, >14.4 km/h; and very-highspeed running, >18.1 km/h) and metabolic-power variables (eg, energy expenditure; average metabolic power; and high-power distance, >20 W/kg).

Results:

The data show that speed-based distances and metabolic power varied between positional groups, although this was largely related to differences in time spent on field. The distance covered at high running speed was lower than that obtained from high-power thresholds for all positional groups; however, the difference between the 2 methods was greatest for hit-up forwards and adjustables.

Conclusions:

Positional differences existed for all metabolic parameters, although these are at least partially related to time spent on the field. Higher-speed running may underestimate the demands of match play when compared with high-power distance—although the degree of difference between the measures varied by position. The analysis of metabolic power may complement traditional speed-based classifications and improve our understanding of the demands of rugby league match play.

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Heidi R. Thornton, Jace A. Delaney, Grant M. Duthie and Ben J. Dascombe

Purpose:

To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes.

Methods:

TL and injury data were collected across 3 seasons (2013–2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes’ corresponding injury status was marked as “available” or “unavailable.” Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted.

Results:

Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes.

Conclusions:

Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.

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

Purpose:

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.

Methods:

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.

Results:

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.

Conclusions:

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

al 9 demonstrated that game-based activities with contact produced higher PL 2D than game-based activities without contact. Furthermore, Cummins et al 16 recently demonstrated that during positional drills, hit-up forwards experienced greater relative 2-dimensional accelerometer workloads than

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Nick Dobbin, Jamie Highton, Samantha Louise Moss and Craig Twist

, practitioners should look to increase body mass and factors that influence sprinting ability (ie, force, velocity, power) concurrently. Dated studies on the physical qualities of senior players 29 , 30 and the recent practice of grouping players (eg, outside backs, adjustable, and hit-up forwards) 5 have