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Jace A. Delaney, Grant M. Duthie, Heidi R. Thornton, Tannath J. Scott, David Gay and Ben J. Dascombe

Rugby league involves frequent periods of high-intensity running including acceleration and deceleration efforts, often occurring at low speeds.

Purpose:

To quantify the energetic cost of running and acceleration efforts during rugby league competition to aid in prescription and monitoring of training.

Methods:

Global positioning system (GPS) data were collected from 37 professional rugby league players across 2 seasons. Peak values for relative distance, average acceleration/deceleration, and metabolic power (Pmet) were calculated for 10 different moving-average durations (1–10 min) for each position. A mixed-effects model was used to assess the effect of position for each duration, and individual comparisons were made using a magnitude-based-inference network.

Results:

There were almost certainly large differences in relative distance and Pmet between the 10-min window and all moving averages <5 min in duration (ES = 1.21–1.88). Fullbacks, halves, and hookers covered greater relative distances than outside backs, edge forwards, and middle forwards for moving averages lasting 2–10 min. Acceleration/deceleration demands were greatest in hookers and halves compared with fullbacks, middle forwards, and outside backs. Pmet was greatest in hookers, halves, and fullbacks compared with middle forwards and outside backs.

Conclusions:

Competition running intensities varied by both position and moving-average duration. Hookers exhibited the greatest Pmet of all positions, due to high involvement in both attack and defense. Fullbacks also reached high Pmet, possibly due to a greater absolute volume of running. This study provides coaches with match data that can be used for the prescription and monitoring of specific training drills.

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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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César Meylan, Joshua Trewin and Kelly McKean

The aims of the current study were to examine the external validity of inertial-based parameters (inertial movement analysis [IMA]) to detect multiplanar explosive actions during maximal sprinting and change of direction (COD) and to further determine its reliability, set appropriate magnitude bands for match analysis, and assess its variability during international women’s soccer matches. Twenty U20 female soccer players, wearing global positioning system (GPS) units with a built-in accelerometer, completed 3 trials of a 40-m sprint and a 20-m sprint with a change of direction to the right or left at 10 m. Furthermore, 13 women’s national-team players (157 files; 4–27 matches/player) were analyzed to ascertain match-to-match variability. Video synchronization indicated that the IMA signal was instantaneous with explosive movement (acceleration, deceleration, COD). Peak GPS velocity during the 40-m sprint showed similar reliability (coefficient of variation [CV] = 2.1%) to timing gates but increased before and after COD (CV = 4.5–13%). IMA variability was greater at the start of sprints (CV = 16–21%) than before and after COD (CV = 13–16%). IMA threshold for match analysis was set at 2.5 m · s–1 · s–1 by subtracting 1 SD from the mean IMA during sprint trials. IMA match variability (CV = 14%) differed from high-speed GPS metrics (35–60%). Practitioners are advised that timing lights should remain the gold standard for monitoring sprint and acceleration capabilities of athletes. However, IMA could be a reliable method to monitor explosive actions between matches and assess changes due to various factors such as congested schedule, tactics, heat, or altitude.

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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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Tim J. Gabbett

Purpose:

A limitation of most rugby league time–motion studies is that researchers have examined the demands of single teams, with no investigations of all teams in an entire competition. This study investigated the activity profiles and technical and tactical performances of successful and less-successful teams throughout an entire rugby league competition.

Methods:

In total, 185 rugby league players representing 11 teams from a semiprofessional competition participated in this study. Global positioning system analysis was completed across the entire season. Video footage from individual matches was also coded via notational analysis for technical and tactical performance of teams.

Results:

Trivial to small differences were found among Top 4, Middle 4, and Bottom 4 teams for absolute and relative total distances covered and distances covered at low speeds. Small, nonsignificant differences (P = .054, ES = 0.31) were found between groups for the distance covered sprinting, with Top 4 teams covering greater sprinting distances than Bottom 4 teams. Top 4 teams made more meters in attack and conceded fewer meters in defense than Bottom 4 teams. Bottom 4 teams had a greater percentage of slow play-the-balls in defense than Top 4 teams (74.8% ± 7.3% vs 67.2% ± 8.3%). Middle 4 teams showed the greatest reduction in high-speed running from the first to the second half (–20.4%), while Bottom 4 teams completed 14.3% more high-speed running in the second half than in the first half.

Conclusion:

These findings demonstrate that a combination of activity profiles and technical and tactical performance are associated with playing success in semiprofessional rugby league players.

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Denise Jennings, Stuart Cormack, Aaron J. Coutts, Luke J. Boyd and Robert J. Aughey

Purpose:

To examine the difference in distance measured by two global positioning system (GPS) units of the same model worn by the same player while performing movements common to team sports.

Methods:

Twenty elite Australian football players completed two trials of the straight line movement (10, 20, 40 m) at four speeds (walk, jog, stride, sprint), two trials of the changes of direction (COD) courses of two different frequencies (gradual and tight), and five trials of a team sport running simulation circuit. To assess inter-unit variability for total and high intensity running (HIR) distance measured in matches, data from eight field players were collected in three Australian Hockey League (AHL) matches during the 2009 season. Each subject wore two GPS devices (MinimaxX v2.5, Catapult, Australia) that collected position data at 5 Hz for each movement and match trial. The percentage difference ±90% confidence interval (CI) was used to determine differences between units.

Results:

Differences (±90% CI) between the units ranged from 9.9 ± 4.7% to 11.9 ± 19.5% for straight line running movements and from 9.5 ± 7.2% to 10.7 ± 7.9% in the COD courses. Similar results were exhibited in the team sport circuit (11.1 ± 4.2%). Total distance (10.3 ± 6.2%) and HIR distance (10.3 ± 15.6) measured during the match play displayed similar variability.

Conclusion:

It is recommended that players wear the same GPS unit for each exercise session to reduce measurement error. The level of between-unit measurement error should be considered when comparing results from players wearing different GPS units.

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Laura A. Garvican, Kristal Hammond, Matthew C. Varley, Christopher J. Gore, Francois Billaut and Robert J. Aughey

Purpose:

This study investigated the decrement in running performance of elite soccer players competing at low altitude and time course for abatement of these decrements.

Methods:

Twenty elite youth soccer players had their activity profile, in a sea-level (SL) and 2 altitude (Alt, 1600 m, d 4, and d 6) matches, measured with a global positioning system. Measures expressed in meters per minute of match time were total distance, low- and high-velocity running (LoVR, 0.01–4.16 m/s; HiVR, 4.17–10.0 m/s), and frequency of maximal accelerations (>2.78 m/s2). The peak and subsequent stanza for each measure were identified and a transient fatigue index calculated. Mean heart rate (HR) during the final minute of a submaximal running task (5 min, 11 km/h) was recorded at SL and for 10 d at Alt. Differences were determined between SL and Alt using percentage change and effect-size (ES) statistic with 90% confidence intervals.

Results:

Mean HR almost certainly increased on d 1 (5.4%, ES 1.01 ± 0.35) and remained probably elevated on both d 2 (ES 0.42 ± 0.31) and d3 (ES 0.30 ± 0.25), returning to baseline at d 5. Total distance was almost certainly lower than SL (ES –0.76 ± 0.37) at d 4 and remained probably reduced on d 6 (ES –0.42 ± 0.36). HiVR probably decreased at d 4 vs SL (–0.47 ± 0.59), with no clear effect of altitude at d 6 (–0.08 ± 0.41). Transient fatigue in matches was evident at SL and Alt, with a possibly greater decrement at Alt.

Conclusion:

Despite some physiological adaptation, match running performance of youth soccer players is compromised for at least 6 d at low altitude.

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Hani Al Haddad, Ben M. Simpson, Martin Buchheit, Valter Di Salvo and Alberto Mendez-Villanueva

This study assessed the relationship between peak match speed (PMS) and maximal sprinting speed (MSS) in regard to age and playing positions. MSS and absolute PMS (PMSAbs) were collected from 180 male youth soccer players (U13–U17, 15.0 ± 1.2 y, 161.5 ± 9.2 cm, and 48.3 ± 8.7 kg). The fastest 10-m split over a 40-m sprint was used to determine MSS. PMSAbs was recorded using a global positioning system and was also expressed as a percentage of MSS (PMSRel). Sprint data were compared between age groups and between playing positions. Results showed that regardless of age and playing positions, faster players were likely to reach higher PMSAbs and possibly lower PMSRel. Despite a lower PMSAbs than in older groups (eg, 23.4 ± 1.8 vs 26.8 ± 1.9 km/h for U13 and U17, respectively, ES = 1.9 90%, confidence limits [1.6;2.1]), younger players reached a greater PMSRel (92.0% ± 6.3% vs. 87.2% ± 5.7% for U13 and U17, respectively, ES = –0.8 90% CL [–1.0;–0.5]). Playing position also affected PMSAbs and PMSRel, as strikers were likely to reach higher PMSAbs (eg, 27.0 ± 2.7 vs 23.6 ± 2.2 km/h for strikers and central midfielders, respectively, ES = 2.0 [1.7;2.2]) and PMSRel (eg, 93.6% ± 5.2% vs 85.3% ± 6.5% for strikers and central midfielders, respectively, ES = 1.0 [0.7;1.3]) than all other positions. The findings confirm that age and playing position affect the absolute and relative intensity of speed-related actions during matches.

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Nick B. Murray, Tim J. Gabbett and Andrew D. Townshend

Objectives:

To investigate the relationship between the proportion of preseason training sessions completed and load and injury during the ensuing Australian Football League season.

Design:

Single-cohort, observational study.

Methods:

Forty-six elite male Australian football players from 1 club participated. Players were divided into 3 equal groups based on the amount of preseason training completed (high [HTL], >85% sessions completed; medium [MTL], 50–85% sessions completed; and low [LTL], <50% sessions completed). Global positioning system (GPS) technology was used to record training and game loads, with all injuries recorded and classified by club medical staff. Differences between groups were analyzed using a 2-way (group × training/competition phase) repeated-measures ANOVA, along with magnitude-based inferences. Injury incidence was expressed as injuries per 1000 h.

Results:

The HTL and MTL groups completed a greater proportion of in-season training sessions (81.1% and 74.2%) and matches (76.7% and 76.1%) than the LTL (56.9% and 52.7%) group. Total distance and player load were significantly greater during the first half of the in-season period for the HTL (P = .03, ES = 0.88) and MTL (P = .02, ES = 0.93) groups than the LTL group. The relative risk of injury for the LTL group (26.8/1000 h) was 1.9 times greater than that for the HTL group (14.2/1000 h) (χ2 = 3.48, df = 2, P = .17).

Conclusions:

Completing a greater proportion of preseason training resulted in higher training loads and greater participation in training and competition during the competitive phase of the season.

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Thomas W.J. Lovell, Anita C. Sirotic, Franco M. Impellizzeri and Aaron J. Coutts

Purpose:

The purpose of this study was to examine the validity of session rating of perceived exertion (sRPE) for monitoring training intensity in rugby league.

Methods:

Thirty-two professional rugby league players participated in this study. Training-load (TL) data were collected during an entire season and assessed via microtechnology (heart-rate [HR] monitors, global positioning systems [GPS], and accelerometers) and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and various other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during rugby league training.

Results:

There were significant within-individual correlations between sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 62.4% of the adjusted variance in sRPE-TL could be explained by TL measures of distance, impacts, body load, and training impulse (y = 37.21 + 0.93 distance − 0.39 impacts + 0.18 body load + 0.03 training impulse). Furthermore, 35.2% of the adjusted variance in sRPE could be explained by exercise-intensity measures of percentage of peak HR (%HRpeak), impacts/min, m/min, and body load/min (y = −0.01 + 0.37%HRpeak + 0.10 impacts/min + 0.17 m/min + 0.09 body load/min).

Conclusion:

A combination of internal and external TL factors predicts sRPE in rugby league training better than any individual measures alone. These findings provide new evidence to support the use of sRPE as a global measure of exercise intensity in rugby league training.