Secondary Logo

Journal Logo

Positional Differences in Professional Rugby League Match Play Through the Use of Global Positioning Systems

Austin, Damien J.1; Kelly, Stephen J.1

Journal of Strength and Conditioning Research: January 2013 - Volume 27 - Issue 1 - p 14–19
doi: 10.1519/JSC.0b013e31824e108c
Original Research
Free

Austin, DJ, and Kelly, SJ. Positional differences in professional rugby league match play through the use of global positioning systems. J Strength Cond Res 27(1): 14–19, 2013—The current use of tracking technology in the form of global positioning systems allows for a greater analysis of locomotor activities occurring in games and a larger volume of games when compared with time-motion analysis. Therefore, the aim of this study is to be the first to analyze the physiological demands of forwards and backs throughout the entirety of an Australian professional rugby league season. The movement patterns of 185 players from a professional rugby league club were recorded during 28 National Rugby League games played in Australia during the 2010 season. The players were clustered into 2 positional groups, backs and forwards. Maximum match-play time recorded was 99 minutes and 50 seconds in a semifinal game recorded for both a forward and back. The mean total distances covered in a game for forwards and backs were 5,964 ± 696 and 7,628 ± 744 m, respectively (p < 0.05). The maximum distance recorded by a forward was 10,511 and 10,359 m for a back. The average number of occurrences in high-intensity running (>18 km·h−1) was 23 ± 4 and for forwards and significantly higher backs with 35 ± 8 (p < 0.05). The maximum work rate in a 10-minute block of match play was 115 and 120 m·min−1 of play for forwards and backs, respectively. Understanding the physiological demands of a sport is important for coaches to deliver athletes optimal training programs that elicit appropriate and specific physiological adaptation. The differences in locomotor activities, which occur between positions, need to be accounted for when developing training programs.

1Sydney Swans Football Club, Sydney, New South Wales, Australia

2Sydney Roosters Rugby League Club, Sydney, New South Wales, Australia

3School of Human Movement Studies, The University of Technology of Sydney, Sydney, New South Wales, Australia

Address correspondence to Damien Austin, ausd55@yahoo.com.au.

Back to Top | Article Outline

Introduction

Rugby league football requires players to intermittently run at high intensity and be involved in collision events, such as tackles (6,9). Current research (12,14,15,18) on the physiological demands of rugby league match play have been based on the subjective measure of time-motion analysis (TMA) via game video of a single or group of players. Most recently, video analysis by King et al. (12) studying physiological demands over 3 professional rugby league matches, estimated distances covered in match play to vary from 6,265 m on average for outside backs and 4,310 m for forwards. It was also demonstrated that players spent 16–17% of total match time in high-intensity activity. The use of TMA has been used to provide an insight into the physiological demands of match play, compare playing levels, and rule changes in rugby league (12,15,18). More recently, rugby league clubs have been allowed to use tracking technology in the form of global positioning systems (GPSs) in games.

The use of GPS technology has been reported in a variety of sports, such as Australian Football League (AFL), soccer, hockey, and rugby union (4,5,9,17). The GPS has exhibited moderate to high levels of validity and reliability for reporting distances covered in low- or high-intensity running, allowing coaches and sport scientists to obtain specific positional information (3,10). Through the use of GPS information, match-play demands can be further analyzed with respect to overall training methods or positional requirements.

The majority of the literature regarding the physiological demands of rugby league (1,7,12,15,18) has used TMA as the preferred method of analysis, with a minimal number of games analyzed during a season. McLellan et al. (13) studying 5 matches and 22 players analyzed the physiological demands of forwards and backs in rugby league. This current research by McLellan et al. (13) had a decrease in total distance covered by forwards and backs (4,982 and 5,573 m, respectively) than those reported by King et al. (12). Distance covered performing high-intensity running and sprinting by forwards (232 m) and backs (440 m) was also significantly decreased at 5 and 8% of the total distance when compared with the values presented by King et al. (12). The variance in methodology in the 2 studies may further contribute to these reported differences.

Although there seems to be similarities and differences among research on the movement demands of rugby league, there is limited quantitative research in this area because of the number of subjects and games studied. Further analysis is required to establish how these movement and physical demands change throughout match play, allowing coaches to understand the effects of fatigue throughout a game. Therefore, the aim of this study is to be the first to give an in-depth analysis of the movement demands of forwards and backs in Elite Australian rugby league over the entirety of a season (28 games) from round 1 to the Grand final. Further analysis of match-play demands will be given in 10-minute blocks of match play to illustrate whether fatigue plays a role throughout match play on distance covered.

Back to Top | Article Outline

Methods

Experimental Approach to the Problem

Examining the match-play demands of elite professional Rugby League forwards and backs over the entirety of a National Rugby League (NRL) season (28 games) is important to provide an in-depth analysis of the physical demands placed on players during a season. Analysis of physical demands over 10-minute time periods throughout match play gives a greater insight into physical demands placed on players and how fatigue affects players' ability to perform throughout a game. Measuring 10-minute periods of play also enables coaches to replicate the most demanding periods of play and train to offset fatigue.

Back to Top | Article Outline

Subjects

The movement patterns of 185 players clustered into 2 positional groups; backs (halfback, five-eight, center, wing, and fullback) (number 82: age: 25 ± 3 years; weight: 106 ± 8 kg; height: 186 ± 5 cm) and forwards (front row, back row, lock) (number 103: age: 24 ± 4 years; weight: 94 ± 4 kg; height: 185 ± 6 cm) from an elite Rugby League club were recorded during 28 NRL games played in Australia during the 2010 season. The players had undertaken 3 months of preseason training before the study, and all were employed as professional rugby league players. Because of the inconsistency of dummy-half and hookers wearing GPS and the variability of the position, hookers were removed from the analysis. This is consistent with previous studies undertaken by Austin et al. (1). All the players were fully familiarized with the GPS units used for data collection through preseason training sessions. Written informed consent was obtained from all the participants before data collection. Ethics approval for all the experimental procedures was granted by the Ethics Committee of the University of Technology Sydney.

Back to Top | Article Outline

Procedures

The players were asked to wear an individual GPS unit (5 Hz SPI Elite; GPSports Systems, Canberra, Australian Capital Territory, Australia). The GPS unit was positioned between the scapulae blades encased in a purpose-designed vest worn by each player. A full descriptive process of the use of GPS unit use in rugby league has previously been defined by McLellan et al. (13). As per McLellan et al. (13), the players had previously worn the units and vest in preseason training, including all outside training sessions involving rugby league match related skill and conditioning sessions. Furthermore, GPS units had also been worn in preliminary practice games. The validity and reliability of 5-Hz GPS units measuring distances and speeds at low to high intensity has been demonstrated in previous studies (10,11,13,16).

The units were turned on 20–30 minutes before the game commencing, with only match play used for analysis. Total time was calculated for ‘playing time’ only, that is, how much time was the player on the playing field only and time off the field, such as injury, periods on the bench, or reserve, is removed from the data analysis. Time off during match play, such as injury time or video referee, was included in the study, because this was part of the game duration; hence ‘playing time’ may exceed the standard 80 minutes of match play. Changes in game movements, distances, and locomotor speeds were analyzed over total playing time. Distances were also calculated per 10 minutes of play to provide information about different periods within each match. Work rate was calculated as distance (meters) covered per minute of play, referred to as meters per minute.

Distance covered was calculated according to 6 movement categories; standing, walking or jogging (0–12 km·h−1), cruising (12–14 km·h−1), striding (14–18 km·h−1), high-intensity running (18–20 km·h−1), sprinting (20–24 km·h−1), and high-intensity sprinting (>24 km·h−1) (13). High-intensity speed zones (>18 km·h−1) were further analyzed into frequency of occurrences, minimum, maximum, and average distance covered.

Table 1

Table 1

All data were downloaded to a computer using SPI Ezy v.2.1 (GP Sports, Canberra, Australia) and analyzed using Team AMS v.2.1 software (GPSports, Canberra, Australia). Once appropriately formatted, data were exported to Microsoft Excel (Microsoft Corporation, USA) for the purpose of data management.

Back to Top | Article Outline

Statistical Analyses

The GPS data were assessed through the GPSports analysis system (5 Hz SPI Elite; GPSports Systems, Australian Capital Territory, Australia). Data for distance, duration, percentage time, distance in speed zones, and number of high-intensity speed zones are presented as means and SD. Analysis of variance (ANOVA) was used to establish differences in the variables among playing positions, and residuals were checked for normality. Repeated measures ANOVA was used to assess variance in work rate over 10-minute time periods. All statistics were run on SPSS v18.0 for Windows with an alpha of 0.05 set a priori.

Back to Top | Article Outline

Results

Total Time

The mean total time of match play was 70 ± 8 minutes for forwards and 89 ± 8 minutes (p < 0.05) for backs. Maximum match-play time was 99 minutes and 50 seconds in a semifinal game recorded for both a forward and back.

Back to Top | Article Outline

Distance

The mean total distances covered in a game for forwards and backs were 5,964 ± 696 and 7,628 ± 744 m, respectively (p < 0.05). The mean total distance ranged from 4,135–7,644 m for forwards and 5,676–9,760 m for backs (Table 1). The maximum distance recorded by a single player in a game was 10,511 and 10,359 m for a forwards and back, respectively. The mean work rate for complete games was significantly different (p < 0.05): 85 ± 4 and 86 ± 5 m·min−1 for forwards and backs, respectively. Throughout the season, mean work rate ranged between 75 and 97 m·min−1 for forwards and 72–100 m·min−1 for backs, respectively (Figure 1).

Back to Top | Article Outline

Movement Demands

The average number of entries of high-intensity running (>18 km·h−1) was 23 ± 4 and for forwards and significantly higher for backs with 35 ± 8 (p < 0.05). Forwards therefore completed, on average 0.329 high-intensity runs per minute of match play, whereas backs completed 0.393 per minute. The maximum frequency of entries in high-intensity speed zones occurring in a game was 32 for forwards and 51 for backs (0.457 and 0.584 high-intensity runs per minute of match play, respectively). The average distance traveled in high-intensity running (>18 km·h−1) for forwards was 17 ± 2 m, ranging from mean distances of 5 ± 0.3 to 43 ± 6 m, while backs averaged 18 ± 2 m with a mean range of 5 ± 0.2 to 56 ± 14 m (p > 0.05). The mean maximum distance recorded was significantly higher (p < 0.05) in backs (45 ± 16 m) compared to forwards (33 ± 11 m). Maximum distance covered in high-intensity running (>18 km·h−1) was 55 and 96 m for forwards and backs, respectively. Forwards covered a maximum of 10.16 m in high-intensity running per minute of match play, whereas backs completed a maximum of 13.07 m.

Back to Top | Article Outline

Meters Per Minute of Each 10-Minute Block of Match Play

The maximum work rate of 10-minute block of match play was 115 and 120 m·min−1 of play for forwards and backs, respectively (Figure 2).

Back to Top | Article Outline

Discussion

The purpose of this study is to be the first to quantify the movement demands of forwards and backs in the National rugby league competition over the entirety of a season (28 games) from round 1 to the Grand final. Further analysis of match-play demands will be given in 10-minute blocks of match play to illustrate whether fatigue plays a role throughout match play on distance covered.

In this study, the mean total distances covered in match play for forwards and backs were 5,964 ± 696 and 7,628 ± 744 m, respectively These findings are substantially higher than those reported by King et al. (12) with outside backs, adjustables and hit-up forwards reported as covering on average 6,265, 5,908, and 4,310 m, respectively. A similar result is reported in this study for maximum distances by a forward (10,511 m) and back (10,359 m), during a single game lasting 99 minutes and 50 seconds. King et al. (12) reported maximum distances of 6,486; 6,082; and 4,579 for outside backs, adjustables and hit-up forwards, respectively. This may be because of methodological differences, with this study using GPS compared with TMA methods used by King et al. (12). Furthermore this study analyzed a higher quantity of games compared with 3 matches during a season by King et al. (12). Meir et al. (15) reported forwards covered approximately 10,000 m and backs approximately 8,500 m during a game. This is closer to the present results for forwards but still relatively low compared with distances covered by backs in this study. Once again methodological differences between studies and changes in rules could explain the discrepancies in values, because Meir et al. (15) also used TMA over 2 games.

A more recent study by McLellan et al. (13) using the same GPS technology as the current research, over 5 matches, reported that forwards covered 4,982 m and backs 5,573 m in a game. During this study, forwards covered 232 m and backs 440 m in high-intensity running (>18 km·h−1). Both results were substantially lower than those recorded in this study over a full season of games. In this study, forwards covered 432 m and backs covered 749 m per match in high-intensity running (>18 km·h−1). The distance in high-intensity running is almost double those suggested by McLellan et al. (13), which may result from a greater number of players and games analyzed in this study. The higher number of games analyzed would allow for a greater understanding of the movement demands of match play, allowing for higher variance in playing styles of opposition teams and referee interpretation. Furthermore, this demonstrates the need for a greater quantity of matches to be analyzed, as distances reported in previous studies may underestimate the higher demands players may encounter in match play in total distance and high-intensity running.

This study reveals that backs covered a 42% greater distance and 34% higher frequency of high-intensity running (>18 km·h−1) when compared with forwards. This may be because of the positional demands, which could leave more space for backs to run before contact with the opposition or because of the greater playing time experienced by backs. This study demonstrates the need to accommodate greater high-intensity running by backs in training, because maximum values are significantly higher in frequency and distance when compared with forwards. Training acceleration should be a priority for forwards because of the shorter average high-intensity running distance reported for this positional group.

Sirotic et al. (18) used TMA to study the physical and game-skill demands of 17 professional and 22 semiprofessional rugby league matches. In this study, it was demonstrated that professional rugby league players experienced decrements in most physical performance measures during the second-half of match play compared with the first half (Figure 1). This study takes this further by separating match play into 10-minute intervals; noting the decrement in distance covered demonstrated by meters per minute. There is 18% decrement in performance for forwards and 15% for backs in first 10 minutes to the last during the first half of match play. These percentages decrements remain relatively consistent when comparing the first 10-minute block to the last 10 minutes of match play (70–80 minutes). The second half of match play has a smaller percentage in performance decrement (13% forwards and 8% backs) from the first to the last 10-minute period of play. Sirotic et al. (18) suggests the higher physical demands placed on elite players during the first half could result in an earlier onset of physical fatigue.

Figure 1

Figure 1

When considering total distance over time spent on the field by a player, a useful measure of meters per minute can be used to give a simple gauge of locomotive activity in match play. The variable of meters per minute has been used in AFL (2). The highest recorded meters per minute in a complete game was 97 and 100 m for forwards and backs, respectively. However, when comparing 10-minute segments of the game, 115 m·min−1 for forwards and 120 m/min for backs were the highest meters per minute recorded by the players. The players would be expected to have the least amount of fatigue within the first 10 minutes of match play compared with later time periods. This gives an indication of what maximum values of locomotor activities the players experience in match play and therefore should be aiming to match or exceed, during training.

Using meters per minute allows for a simple way to analyze and monitor training volume, when undertaking game simulation training. It would also be recommended when training without the use collision events, such as tackling, skill drills, should exceed the maximum meters per minute reported for games. This will compensate for the time taken to complete tackles in games without registering as locomotor activity on GPS. Additional running in training can compensate for the extra physical demand of collision events experienced in games. This is one way to increase the physical demand on players and reduce the likelihood of injury.

Figure 2

Figure 2

Limited by the ability of GPS to measure locomotor activities, there is no research via accelerometers within GPS units measuring collision events, such as tackling. Collisions have been reported to incur a large physiological demand (1,8), significantly increasing the intensity of games. Further study needs to be undertaken into load measures calculated from accelerometer data in GPS analysis systems to establish a greater understanding of the types of impacts and quantifying collision events in rugby league match play. Future research in the specific positional demands of players in match play would allow for even greater transference of GPS analysis to training methodologies used in professional rugby league. Understanding the physiological demands of a sport is important for coaches to deliver athletes optimal training programs that elicit appropriate and specific physiological adaptation. It has also been shown the higher number of games analyzed give a greater illustration of the physiological demands of rugby league and the limitation of previous studies using a small number of games and subjects.

Back to Top | Article Outline

Practical Applications

This study has shown the need for greater quantity in analysis of rugby league match play and training, not previously allowed by video analysis and low sample numbers. The higher match-play variables shown in this study, compared with previous research, have demonstrated a possible undervaluing by coaches of match-play demands when designing training programs. Coaches need to accommodate for the maximum values in time, distance, meters per minute and high-intensity running frequency and distances, which in some cases have been under valued by other studies. These values also need to be developed based on forwards and backs positional data, because the study demonstrates these positions can vary greatly. Coaches should be aware of the incremental decrease in locomotive performance as the game progresses in each 10-minute increment in each half of match play. The use of real time GPS technology would allows coaches to monitor training and compare with match-play demands, signs of fatigue, and decreases in high-intensity running through meters or high-intensity runs per minute shown in this study. This will distinguish whether training has given players the physical capacity to perform for longer duration without decrements in performance.

Back to Top | Article Outline

Acknowledgments

There has been no financial assistance with this research. The results of this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.

Back to Top | Article Outline

References

1. Austin D, Gabbett T, Jenkins D. Repeated high-intensity exercise in professional rugby league. J Strength Cond Res 25: 1898–1904, 2011.
2. Brewer C, Dawson B, Heasman J, Stewart G, Cormack S. Movement pattern comparisons in elite (AFL) and sub-elite (WAFL) Australian football games using GPS. J Sci Med Sport 13: 618–623, 2010.
3. Coutts A, Duffield R. Validity and reliability of GPS units for measuring movement demands of team sports. J Sci Med Sport 13, 133–135, 2010.
4. Coutts A, Quinn J, Hocking J, Castagna C, Rampinini E. Match running performance in elite Australian rules football. J Sci Med Sport 13: 543–548, 2010.
5. Cunniffe B, Proctor W, Baker J, Davies B. An evaluation of the physical demands of elite rugby union using global positioning system tracking software. J Strength Cond Res 23: 1195–1203, 2009.
6. Gabbett T. Physiological characteristics of junior and senior rugby league players. Br J Sports Med 36: 334–339, 2002.
7. Gabbett T. Science of rugby league football: A review. J Sports Sci 23: 961–977, 2005.
8. Gabbett T. Influence of fatigue on tackling technique in rugby league players. J Strength Cond Res 22: 625–632, 2008.
9. Gabbett T. GPS analysis of elite women's field hockey training and competition J Strength Cond Res 24: 1321–1324, 2010.
10. Gray A, Jenkins D, Andrews M, Taaffe D, Glover M. Validity and reliability of GPS for measuring distance traveled in field based team sports. J Sports Sci 28: 1319–1325, 2010.
11. Jennings D, Cormack S, Coutts AJ, Boyd L, Aughey RJ. The validity and reliability of GPS units for measuring distance in team sport specific running patterns. Int J Sports Physiol Perform 5: 328–341, 2010.
12. King T, Jenkins D, Gabbett T. A time-motion analysis of professional rugby league match-play. J Sports Sci 27: 213–219, 2009.
13. McLellan C, Lovell D, Gass G. Performance analysis of elite rugby league match play using global positioning systems. J Strength Cond Res, 25: 1703–1710, 2011.
14. Meir R, Arthur D, Forrest M. Time and motion analysis of professional rugby league: A case study. Strength Cond Coach 1: 24–29, 1993.
15. Meir R, Colla P, Milligan C. Impact of the 10-metre rule change on professional rugby league: Implications for training. Strength Cond J 23: 42–46, 2001.
16. Portas MD, Harley JA, Barnes CA, Rush CJ. The validity and reliability of 1-Hz and 5-Hz global positioning systems of linear, multidirectional, and soccer-specific activities. I J Sports Physiol Perform 5: 448–458, 2010.
17. Roberts S, Trewartha G, Higgitt R, El-Abd J, Stokes K. The physical demands of elite English rugby union. J Sports Sci. 26: 825–833, 2008.
18. Sirotic A, Coutts A, Knowles H, Catterick C. A comparison of match demands between elite and semi-elite rugby league competition. J Sports Sci 27: 203–211, 2009.
Keywords:

high-intensity running; microtechnology; motion analysis

© 2013 National Strength and Conditioning Association