International Journal of Sports Physiology and Performance, 2015, 10, 703 -710 http://dx.doi.org/10.1123/ijspp.2014-0080 © 2015 Human Kinetics, Inc.
Original Investigation
Physical Demands of Match Play in Successful and Less-Successful Elite Rugby League Teams 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. Keywords: winning, losing, activity profiles, team sport, time–motion analysis Rugby league is an international team sport in which games last 80 minutes (2 × 40-min halves).1,2 Like Australian football,3,4 soccer,5–7 and hockey,8 rugby league involves brief periods of highintensity running interspersed with longer periods of low-intensity activity.9–11 Rugby league players are often required to perform collisions, accelerations, and high-speed-running efforts with minimal recovery between efforts.11 Three or more sprints, high-intensity accelerations, or tackles with less than 21 seconds recovery between high-intensity efforts has been defined as a repeated high-intensityeffort bout.9,11 Notably, 70% of all repeated high-intensity-effort bouts occur in the 5-minute period preceding a try.9 Clearly, even though high-intensity exercise represents the minority of rugby league match play,9–11 success (or lack of success) may be influenced by a team’s ability (or inability) to perform repeated bouts of high-intensity activity. In soccer, temporary reductions in high-intensity activity follow the most intense passages of play.5,6,12–14 Mohr et al14 established that in the 5-minute period after the greatest amount of high-intensity running, there was a 12% reduction in high-intensity running compared with the average demands of a match. A more recent study also showed that high-intensity running decreases below the match average in the 5-minute period subsequent to the most intense phase of the game and then recovers in the following 5-minute period.6 However, it has also been demonstrated that ball-in-play time can differ between 5-minute periods of match play, therefore limiting the opportunity to perform physical work.15 Given that rugby league also has periods of intermittent high-intensity running coupled with Hulin and Gabbett are with the School of Exercise Science, Australian Catholic University, Brisbane, QLD, Australia. Kearney and Corvo are with Melbourne Storm Rugby League Club, Melbourne, Australia. Address author correspondence to Tim Gabbett at [email protected].
collisions,11 these methods of investigating temporary reductions in performance within a match could offer valuable insight into the performance fluctuations of rugby league match play. Furthermore, given that recent evidence has demonstrated that the phase of play (ie, attacking or defending) can affect the physical demands of match play,16 this factor must be considered, along with other confounding variables such as ball-in-play time.17 Kempton et al18 examined the effect of the most intense period of match play on physical and technical performance in elite senior and junior ball-playing positions (ie, hooker, halfback, five-eighth, and fullback) in rugby league teams having low success rates (elite, 36% of matches won; junior elite, 13% of matches won). These ball-playing positions have an important role of providing team structure and coordinating attacking movements,19 and the number and quality of their skill performances have been shown to reduce in the 5-minute period after the peak intensity period of match play.18 However, given that the ability to execute a range of skills while fatigued is able to discriminate between players competing at different levels of competition,20 these findings18 may not be representative of the fatigue response of players from teams with higher success rates. There is currently a lack of evidence relating to differences in the intensity of match play between successful and less-successful teams in rugby league. Although 1 study demonstrated that increases in total distance were related to greater competitive success of 1 team,21 there is a paucity of evidence comparing separate teams with high and low success rates. Studies from soccer have demonstrated that teams finishing in the bottom 5 ladder positions cover greater high-intensity-running distance than teams finishing in the top 5 positions.22 In contrast with earlier studies14 it has been highlighted that teams competing in higher divisions of competition cover less total distance and less high-speed distance than teams competing in lower divisions of competition.23 One of the few studies that 703
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have investigated match intensities between successful and lesssuccessful rugby league teams was conducted by dividing first- and second-half data into quartiles.24 The results demonstrated that playing intensities during the selected quartiles of match play differed between winning and losing semielite rugby league teams, with winning teams producing higher intensities than losing teams.24 Although that study only investigated these relationships among hookers, second-rowers, and locks (ie, backs and props were excluded), the findings clearly show that match intensities differ between successful and less-successful semielite rugby league teams. However, whether these relationships remain consistent among elite teams and all positional groups remains unclear. Further additions to the understanding of rugby league activity profiles could be offered by investigating differences in segmental activity profiles between all positional groups during a rugby league match and between successful and less-successful rugby league teams. Given that differences have been shown between wholematch activity profiles of winning and losing teams,21,25 differences in the within-match activity profiles may also exist between successful and less-successful teams. Therefore, the purpose of this study was to quantify activity profiles in approximately 5-minute 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 differs between successful and less-successful teams playing at an elite standard. In addition, we aimed to account for contextual factors such as ball-in-play time and time in attack and defense.
Methods Participants Thirty-one players (mean ± SD age 24.8 ± 3.1 y) from 2 elite rugby league teams participated in this study. Participants were competing in the Australian National Rugby League competition. All participants received a clear explanation of the study, including information on the risks and benefits, and written consent was obtained. All experimental procedures were approved by the institutional review board for human investigation.
Comparison of High- and Low-Success Teams Teams were split into high-success and low-success groups based on the percentage of matches won throughout the team’s season. The high-success team won 71% of matches played, whereas the low-success team won 58% of matches played. Although the difference in the percentage of matches won between the high- and low-success teams (71% vs 58%) may be considered small in a numerical sense, a difference of 13% in the most recent (2013) rugby league season differentiated between teams finishing with a final ladder position of 13th and 6th, with the former being excluded from the finals series and the latter being rewarded with a finals series match played on their home ground. Therefore, although these differences may be considered small, the consequences of such a difference could be quite large.
Global Positioning System Analysis Global positioning system (GPS) analysis was completed during 25 elite matches (15 for the high-success team, 10 for the lowsuccess team), equating to 200 GPS files. The mean ± SD satellite
availabilities for GPS data samples were 12 ± 1 (high-success team) and 11 ± 2 (low-success team). Positional groups were categorized into adjustables (hooker, halfback, five-eighth, and fullback), hit-up forwards (props, second-rowers, and locks), and outside backs (center and wing).9 As this study intended to quantify differences between successful and less-successful teams, interchange players were included in the analysis to investigate the activity profiles of each team as a whole. Movement was recorded using a MinimaxX GPS unit (Team S4, Catapult Innovations, Melbourne, Australia) sampling at 10 Hz. The GPS signal provided information on velocity, distance, position, and acceleration. The GPS unit also included a triaxial accelerometer and gyroscope sampling at 100 Hz, which provided information on physical collisions and repeated high-intensity efforts. The unit was worn in a small vest, on the upper back of the players. While recommendations for reporting GPS data have been presented,26 there is generally a wide range of reporting methods employed in the scientific literature.11,27,28 To date, there are no standardized methods for reporting velocity zones and several definitions of what constitutes an effort.3,11,26 Previous studies of teamsport demands have employed 5-Hz technology11,28 and as such have employed broad movement-velocity bands due to the larger measurement error with lower sampling frequencies.29,30 To allow comparisons with other researchers,11 data were categorized into movement-velocity bands corresponding to low (0–5 m/s) and high (>5 m/s) intensities, with total distance covered being represented by the sum of distances covered in these 2 intensity bands, number of collisions, and repeated high-intensity-effort bouts. A repeated high-intensity-effort bout was defined as 3 or more high-acceleration (≥2.79 m/s2),3 high-velocity, or contact efforts with less than 21 seconds recovery between efforts.11 All data were analyzed using Catapult Sprint software (version 5.1.0.1). The 10-Hz MinimaxX units have been shown to have acceptable validity and reliability for measuring sprinting velocities and accelerations.30,31 The MinimaxX units have also been shown to offer a valid measurement of collisions32 and repeated high-intensity efforts33 commonly observed in rugby league.
Segmental Data Analysis Commercially available match footage was coded for activity and recovery cycles by a member of the research team. Time when the ball was continuously in play was considered activity, while any stoppages during the match (eg, for scrums, penalties, line dropouts, tries, and video referee decisions) were considered recovery. The intraobserver reliability was assessed by coding 10 randomly selected matches. The intraclass correlation coefficients and typical error of measurement for the coding of activity and recovery cycles were .95 and 1.2% and .83 and 4.5%, respectively. First- and second-half GPS and ball-in-play data for each match were separated into 8 equal periods (mean ± SD 340 ± 28 s). Thus, each match was separated into 16 periods of approximately 5 minutes, based on the duration of each half. All GPS variables were analyzed in relation to the duration of each period. Periods that contained the greatest ball-in-play time, total distance, high-intensity-running distance, number of collisions, and number of repeated high-intensity-effort bouts were identified and compared with the subsequent period and the mean period (minus the peak and subsequent period). 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
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average demands of all other periods in a match (mean period). For the analysis of the peak, subsequent, and mean periods, files were eliminated from the analysis when the peak period occurred before halftime, full-time, or the player being interchanged. These files were maintained in the data set for the comparison of predefined periods between the 2 teams with high and low success rates.
No significant difference (51.1% ± 4.9% vs 49.9% ± 5.2%, P = .569) was found between the amounts of time high- and low-success teams spent in possession of the ball (ie, in attack).
Peak, Subsequent, and Mean Periods
Statistical Analysis
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Percentage of Match Play Spent in Possession of the Ball
Independent t tests were used to compare means of high- and low-success teams for the percentage of time spent in possession. Factorial analysis of variance (ANOVA) tests were used to identify differences between the peak, subsequent, and mean periods for the same team and to identify differences between peak, subsequent, and mean periods of high- and low-success teams. Any significant differences involving the 2 teams were followed up using a Tukey honestly significant difference post hoc test. As multiple comparisons were being made, a practical approach determining the scale of difference between high- and low-success teams was used for the analysis of the physical demands across predefined periods. Specifically, Cohen effect-size (ES) statistic and 90% confidence intervals (CI) were used to determine the magnitude of any differences.34 The magnitude of the ES was classified as trivial (2.1).35 Statistical significance was set at P < .05. Data are reported as mean ± SD.
Results Ball in Play The high- and low-success teams’ peak period of ball in play was significantly different from their subsequent (P = .002, ES = 1.4 ± 0.2; P = .001, ES = 1.4 ± 0.3, respectively) and mean period (P = .001, ES = 1.9 ± 0.7; P = .001, ES = 1.7 ± 0.2, respectively). No significant differences were found between the peak (P = .558), subsequent (P = .654), and mean ball-in-play periods (P = .867) of high- and low-success teams (Figure 1[A]). Trivial to small differences (ES = 0.1–0.3 ± 0.2) were found between ball-in-play periods of high- and low-success teams across predefined periods of match play (Figure 1[B]).
Figure 2 shows the total distance covered, high-intensity-running distance covered, number of collisions per minute, and number of repeated high-intensity-effort bouts per minute in the high- and low-success teams during the peak, subsequent, and mean periods. The peak period contained higher total distance for lesssuccessful adjustables (126 ± 14 vs 92 ± 12 m/min, P = .001, ES = 1.5 ± 0.4), hit-up forwards (126 ± 23 vs 97 ± 10 m/min, P = .001, ES = 1.8 ± 0.6), and outside backs (121 ± 15 vs 106 ± 17 m/min, P = .041, ES = 0.9 ± 0.7) than for the successful adjustables, hit-up forwards, and outside backs. Compared with their less-successful counterparts, adjustables, hit-up forwards, and outside backs from the high-success team covered lower total distance during the mean periods of match play (69 ± 10 vs 91 ± 14 m/min, P = .001, ES = 1.4 ± 0.5; 67 ± 9 vs 92 ± 9 m/min, P = .001, ES = 2.0 ± 0.3; and 68 ± 12 vs 88 ± 11 m/min, P = .001, ES = 1.3 ± 0.6, respectively). High-intensity-running distance was greater during the peak period for less-successful adjustables (17 ± 7 vs 8 ± 5 m/min, P = .001, ES = 1.1 ± 0.7) and hit-up forwards (17 ± 7 vs 9 ± 5 m/min, P = .001, ES = 1.4 ± 0.4) than for successful adjustables and hit-up forwards. The mean period for high-intensity-running distance for adjustables, hit-up forwards, and outside backs from the highsuccess team contained less high-intensity-running distance than for the same positional groups with lower success rates (adjustables, 2 ± 2 vs 6 ± 2 m/min, P = .001, ES = 1.4 ± 0.6; hit-up forwards, 3 ± 2 vs 6 ± 2 m/min, P = .001, ES = 1.7 ± 0.4; outside backs, 5 ± 2 vs 7 ± 2 m/min, P = .001, ES = 1.2 ± 0.7). Adjustables from the high-success team were involved in more collisions per minute than adjustables from the low-success team during the peak period (1.6 ± 0.5 vs 1.1 ± 0.4, P = .03, ES = 1.0 ± 0.5) and the mean period (0.8 ± 0.3 vs 0.4 ± 0.3, P = .025, ES = 1.1 ± 0.6). Successful hit-up forwards were involved in more collisions on average than less-successful hit-up forwards (0.8 ± 0.2 vs 0.7 ± 0.2, P = .005, ES = 0.5 ± 0.3). In the peak period, hit-up forwards and outside backs with higher success rates were involved in a smaller number of repeated high-intensity-effort bouts per minute than hit-up forwards and outside backs with low success rates (hit-up forwards, 0.3 ± 0.1 vs 0.5 ± 0.1, P = .008, ES = 0.7 ± 0.4; outside backs, 0.2 ± 0.1 vs 0.4 ± 0.1, P = .009, ES = 1.2 ± 0.7).
Physical Demands of Successful and LessSuccessful Teams During Predefined Periods
Figure 1 — (A) Peak, subsequent, and mean ball-in-play time and (B) ball-in-play time across predefined periods of match play, mean ± SD. #Significant difference (P < .05) from the subsequent and mean period for the same team.
Figure 3 shows the physical demands of selected periods of match play for the high- and low-success teams, for total distance covered, high-intensity-running distance, number of collisions per minute, and number of repeated high-intensity-effort bouts per minute. Adjustables from the low-success team covered greater total distance in periods 1, 3 to 9, and 11 to 16 (ES = 0.6 to 1.4 ± 0.5 to 1.0) and greater high-intensity-running distance in periods 1 to 5 and 9 to 16 (ES = 0.8 to 1.4 ± 0.6 to 1.4) and were involved in fewer collisions during periods 3, 5, 7, 9 to 11, and 13 (ES = 0.8 to 1.0 ± 0.6 to 0.7) than adjustables from the high-success team.
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Figure 2 — Peak, subsequent, and mean (A–C) total distance covered, (D–F) high-intensity-running (HIR) distance covered, (G–I) number of collisions per minute, and (J–L) number of repeated high-intensity-effort (RHIE) bouts per minute for adjustables, hit-up forwards, and outside backs from successful and less successful teams, mean ± SD. *Significant difference (P < 0.05) between teams for the same period. ΨSignificant difference (P < .05) from the subsequent period for the same team. #Significant difference (P < .05) from the mean period for the same team.
Compared with hit-up forwards from the high-success team, low-success hit-up forwards covered greater total distance in periods 2 to 16 (ES = 0.7 to 1.7 ± 0.4 to 0.5) and greater high-intensityrunning distance in periods 1, 3, 5, 6, and 8 to 16 (ES = 0.9 to 1.5 ± 0.8 to 1.1) and were involved in fewer collisions during periods 1, 2, and 10 (ES = 0.6 to 0.7 ± 0.3 to 0.5). Outside backs from the low-success team covered greater total distance in periods 2, 3, 5, 7, 9 to 11, 13, and 16 (ES = 1.0 to 1.3 ± 0.6 to 0.7) and greater high-intensity-running distance in periods 5, 8, and 16 (ES = 1.0 to 1.1 ± 0.6 to 0.7) than outside backs from the high-success team.
Discussion This study is the first to investigate the activity profiles of rugby league match play in 2 elite teams with differing success rates. The study provides conflicting results with previous research.21 In a study of 1 rugby league team, it was demonstrated that greater total distance was covered when winning than when losing, with the greater total distance achieved through greater low-intensity (0–5 m/s) distances (ie, no differences existed in high-intensity-running distance between winning or losing).21 However, it should be noted that the contrasting methods of the
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Figure 3 — Physical demands across predefined periods of match play for elite high- and low-success teams: (A–C) total distance covered, (D–F) highintensity-running (HIR) distance covered, (G–I) number of collisions per minute, and (J–L) number of repeated high-intensity-effort (RHIE) bouts per minute, mean ± SD. H denotes halftime. a Moderate effect size (0.61–1.20) between teams for the same period. b Large effect size (1.21–2.00) between teams for the same period.
previous and current studies may explain these conflicting results. Specifically, the current study investigated the activity profiles of 2 separate teams with differing success rates (ie, high-success team vs low-success team), whereas others21 investigated the results of 1 team (ie, 1 team when winning vs the same team when losing). Therefore, our conflicting results may reflect technical and tactical differences representing a greater distinguishing factor between successful and less-successful teams or success being the result of a multifactorial relationship between technical and tactical effectiveness and activity profiles more than merely activity profiles in isolation.
In contrast to some14,21 but not all22,23,25 studies, our results highlight that an ability to cover greater total distance or greater high-intensity distance does not result in greater competitive success. Moreover, our findings demonstrate that less-successful teams are subjected to a greater running workload in terms of total distance covered and distance covered at high intensities. Given that no significant differences were found for the percentage of time the 2 teams spent in possession of the ball, the differences in the average running demands is unlikely to be the result of a greater amount of time spent defending, which has been shown to elicit greater running demands than attacking.16 Furthermore, it has previously been
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hypothesized that the poorer match performance of losing teams results in a greater amount of scrambling in defense, which in turn increases the amount of high-intensity running performed.21 Indeed, poor defense and missed tackles in rugby league may result in the need for players to cover-defend and pursue members of the opposition, therefore resulting in greater high-intensity-running distance. Another novel finding of this study is that lower running workloads achieved by the high-success team were coupled with a greater number of collisions than for the low-success team. Recent evidence may offer possible explanations for our results. First, it has recently been demonstrated that the inclusion of collisions to small-sided games challenges a player’s ability to maintain running performance36,37 and results in elevated markers of muscle damage.38 Therefore, the high-success team’s involvement in a greater number of collisions may have resulted in a fatiguing effect that reduced running performance. Second, we would suggest a more plausible explanation is that the high-success cohort was able to effectively slow the “play-the-ball” while defending. This could be achieved by tactical strategies that employ more players to complete each tackle on more occasions (ie, 2 and 3 players per tackle on a greater number of occasions), effectively resulting in a greater number of collisions, as more players are involved in each tackle, and a slower play that may not require the need for players to cover greater total distance or high-intensity distance. In addition to the more successful cohort being involved in a greater number of collisions, the more successful hit-up forwards were involved in a moderately greater number of collisions during the initial periods of play (periods 1 and 2). These findings are not surprising given that repeated high-intensity-effort bouts that contain a greater number of contact efforts are more likely to occur in higherlevel competitors.39 Collectively, these data highlight that to ensure competitive success, not only is a player’s ability to be involved in repeated collisions vital, but also these collisions must occur in greater numbers during the initial stages of a match. Furthermore, the fact that our results demonstrated the greatest differences among hit-up forwards and adjustables may suggest that the workload of these positional groups has a greater effect on team success than that of the outside backs. The analysis of the physical demands throughout predefined periods demonstrates a trend for the total distance and high-intensity-running distance of the low-success team to increase during the latter periods of the second half (Figure 2[A], [D], [E], and [F]). These findings provide results that conflict with those of previous studies that have highlighted that physical output decreases in the second half of match play.6,40 However, recent evidence has also demonstrated that teams completing the season in the bottom 4 positions of the ladder increased their high-intensity-running distance in the second halves of matches, whereas teams finishing in the middle 4 and top 4 ladder positions decreased and maintained high-intensity-running distance, respectively.41 These observations have been supported by others24 who have demonstrated that less-successful teams display a greater “end spurt” than teams with higher success rates. The findings of the current and previous studies24,41 suggest that increases in physical output during the latter stages of match play may be linked with a poorer rate of success. Furthermore, these increases in running workloads could be the result of players’ preserving physical output during the earlier stages of match play, therefore facilitating an increase in intensity when anticipating the end of the match.42 Alternatively, this result may indeed be due to the inclusion of interchange players, a contrast in methodology that should be noted when comparing our findings with others.40
Recent evidence has demonstrated that predefined periods underestimate the peak demands of match play compared with a rolling time scale (ie, match intensities after every time point for the next 5-min period).43 As such, we highlight that these results should be interpreted with caution, due to our segmental periods’ being predefined. Finally, as only trivial and small differences were found between the ball-in-play time of the high- and low-success team across any of the predefined periods, we concluded that the differences in physical demands of the 2 teams is not the result of differences in ball-in-play time. However, given that significant differences were found in ball-in-play time across peak, subsequent, and mean periods, we are circumspect in suggesting that reductions in physical output after the peak period were the result of fatigue, as opportunities to perform physical work may have been limited.
Practical Applications The results of this study demonstrate that compared with a more successful team, a less-successful team covers greater total distance, performs more high-intensity running, and depending on position is involved in a greater number of repeated high-intensity-effort bouts. Therefore, these findings suggest that greater amounts of high-intensity activity and total distance are not related to success in elite rugby league. Our data suggest that a greater number of collisions is linked with a greater rate of success. These findings are of importance to coaches and sport scientists when interpreting time–motion analyses, as they may indicate that overall technical and tactical effectiveness of the team rather than greater running workloads may indeed be a greater determinant of success, or success may depend on a myriad of factors unrelated to activity profiles.
Conclusion Coaches and sport scientists should note that greater running workloads are not indicative of success in elite rugby league match play. These findings are of importance to practitioners when interpreting time–motion analyses, as this may indicate that technical and tactical differences may be the distinguishing factor between successful and less-successful teams or that success is the result of a multifactorial relationship between technical and tactical effectiveness and activity profiles, more than merely activity profiles in isolation.
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6. Bradley PS, Noakes TD. Match running performance fluctuations in elite soccer: indicative of fatigue, pacing or situational influences? J Sports Sci. 2013;31(15):1627–1638. PubMed doi:10.1080/0264041 4.2013.796062 7. Di Salvo V, Baron R, Tschan H, Calderon Montero FJ, Bachl N, Pigozzi F. Performance characteristics according to playing position in elite soccer. Int J Sports Med. 2007;28:222–227. PubMed doi:10.1055/s-2006-924294 8. Spencer M, Lawrence S, Rechichi C, Bishop D, Dawson B, Goodman C. Time–motion analysis of elite field hockey, with special reference to repeated sprint activity. J Sports Sci. 2004;22:843–850. PubMed doi:10.1080/02640410410001716715 9. Austin DJ, Gabbett TJ, Jenkins DG. Repeated high-intensity exercise in a professional rugby league. J Strength Cond Res. 2011;25(7):1898– 1904. PubMed doi:10.1519/JSC.0b013e3181e83a5b 10. Gabbett TJ. Sprinting patterns of national rugby league competition. J Strength Cond Res. 2012;26(1):121–130. PubMed doi:10.1519/ JSC.0b013e31821e4c60 11. Gabbett TJ, Jenkins DG, Abernethy B. Physical demands of professional rugby league training and competition using microtechnology. J Sci Med Sport. 2012;15:80–86. PubMed doi:10.1016/j. jsams.2011.07.004 12. Bradley PS, Di Mascio M, Peart D, Olsen P, Sheldon B. High-intensity activity profiles of elite soccer players at different performance levels. J Strength Cond Res. 2010;24:2343–2351. PubMed doi:10.1519/ JSC.0b013e3181aeb1b3 13. Di Mascio M, Bradley PS. Evaluation of the most intense period of high-intensity running in English FA Premier League soccer matches. J Strength Cond Res. 2013;27:909–915. PubMed doi:10.1519/ JSC.0b013e31825ff099 14. Mohr M, Krustrup P, Bangsbo J. Match performance of highstandard soccer players with special reference to development of fatigue. J Sports Sci. 2003;21:519–528. PubMed doi:10.1080/0264041031000071182 15. Carling C, Dupont G. Are declines in physical performance associated with a reduction in skill-related performance during professional soccer match-play? J Sports Sci. 2011;29(1):63–71. PubMed doi:10 .1080/02640414.2010.521945 16. Gabbett TJ, Polley C, Dwyer DB, Kearney S, Corvo A. Influence of field position and phase of play on the physical demands of match-play in professional rugby league forwards. J Sci Med Sport. 2014;17(5):556–561. PubMed doi:10.1016/j.jsams.2013.08.002 17. Carling C. Interpreting physical performance in professional soccer match-play: should we be more pragmatic in our approach? Sports Med. 2013;43:655–663. PubMed doi:10.1007/s40279-013-0055-8 18. Kempton T, Sirotic AC, Cameron M, Coutts AJ. Match-related fatigue reduces physical and technical performance during elite rugby league match-play: a case study. J Sports Sci. 2013;31(16):1770–1780. PubMed doi:10.1080/02640414.2013.803583 19. Sirotic AC, Knowles H, Catterick C, Coutts AJ. Positional match demands of elite rugby league competition. J Strength Cond Res. 2011;25:3076–3087. PubMed doi:10.1519/JSC.0b013e318212dad6 20. Gabbett T, Kelly J, Pezet T. Relationship between physical fitness and playing ability in rugby league players. J Strength Cond Res. 2007;21:1126–1133. PubMed 21. Gabbett TJ. Influence of the opposing team on the physical demands of elite rugby league match play. J Strength Cond Res. 2013;27:1629– 1635. PubMed doi:10.1519/JSC.0b013e318274f30e 22. Di Salvo V, Gregson W, Atkinson G, Tordoff P, Drust B. Analysis of high intensity activity in premier league soccer. Int J Sports Med. 2009;30(3):205–212. PubMed doi:10.1055/s-0028-1105950
23. Di Salvo V, Pigozzi F, González-Haro C, Laughlin MS, De Witt JK. Match performance comparison in top English soccer leagues. Int J Sports Med. 2013;34(6):526–532. PubMed 24. Black GM, Gabbett TJ. Match intensity and pacing strategies in rugby league: an examination of whole-game and interchange players, and winning and losing teams. J Strength Cond Res. 2014;28(6):1507– 1516. PubMed 25. Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisloff U. Technical performance during soccer matches of the Italian Serie A league: effect of fatigue and competitive level. J Sci Med Sport. 2009;12:227–233. PubMed doi:10.1016/j.jsams.2007.10.002 26. Dwyer DB, Gabbett TJ. Global positioning system data analysis: velocity ranges and a new definition of sprinting for field sport athletes. J Strength Cond Res. 2012;26:818–824. PubMed 27. McLellan CP, Lovell DI, Gass G. Performance analysis of elite rugby league match play using global positioning systems. J Strength Cond Res. 2011;25:1703–1710. PubMed doi:10.1519/ JSC.0b013e3181ddf678 28. Waldron M, Twist C, Highton J, Worsfold P, Daniels M. Movement and physiological match demands of elite rugby league using portable global positioning systems. J Sports Sci. 2011;29:1223–1230. PubMed doi:10.1080/02640414.2011.587445 29. Jennings D, Cormack SJ, Coutts AJ, Boyd LJ, Aughey RJ. The validity and reliability of GPS units for measuring distance in team sport specific running patterns. Int J Sports Physiol Perform. 2010;5(3):328– 341. PubMed 30. Varley MC, Fairweather IH, Aughey RJ. Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion. J Sports Sci. 2012;30(2):121–127. PubMed doi :10.1080/02640414.2011.627941 31. Johnston RJ, Watsford ML, Kelly SJ, Pine MJ, Spurrs RW. Validity and interunit reliability of 10 Hz and 15 Hz GPS units for assessing athlete movement demands. J Strength Cond Res. 2014;28(6):1649–1655. PubMed doi:10.1519/JSC.0000000000000323. 32. Gabbett T, Jenkins D, Abernethy B. Physical collisions and injury during professional rugby league skills training. J Sci Med Sport. 2010;13(6):578–583. PubMed doi:10.1016/j.jsams.2010.03.007 33. Gabbett TJ. Influence of playing standard on the physical demands of professional rugby league. J Sports Sci. 2013;31(10):1125–1138. PubMed doi:10.1080/02640414.2013.773401 34. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum; 1988. 35. Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform. 2006;1(1):50–57. PubMed 36. Johnston RD, Gabbett TJ, Seibold AJ, Jenkins DG. Influence of physical contact on pacing strategies during game-based activities. Int J Sports Physiol Perform. 2014;9(5):811–816. PubMed http://dx.doi. org/10.1123/ijspp.2013-0424 37. Gabbett TJ, Jenkins DG, Abernethy B. Influence of wrestling on the physiological and skill demands of small-sided games. J Strength Cond Res. 2012;26(1):113–120. PubMed doi:10.1519/ JSC.0b013e31821d97f4 38. Johnston RD, Gabbett TJ, Seibold AJ, Jenkins DG. Influence of physical contact on neuromuscular fatigue and markers of muscle damage following small-sided games. J Sci Med Sport. 2014;17(5):535–540. PubMed 39. Black GM, Gabbett TJ. Repeated high-intensity-effort activity in elite and semielite rugby league match play. Int J Sports Physiol Perform. 2015;10(6):711–717. PubMed http://dx.doi.org/10.1123/ijspp.2014-0081 40. Kempton T, Sirotic AC, Coutts AJ. An integrated analysis of match-related fatigue in professional rugby league. J Sports Sci. 2015;33(1):39-47. PubMed
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43. Varley MC, Elias GP, Aughey RJ. Current match-analysis techniques’ underestimation of intense periods of high-velocity running. Int J Sports Physiol Perform. 2012;7:183–185. PubMed
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41. Gabbett TJ. Effects of physical, technical, and tactical factors on final ladder position in semiprofessional rugby league. Int J Sports Physiol Perform. 2014;9(4):680–688. http://dx.doi.org/10.1123/IJSPP.2013 -0253 42. Waldron M, Highton J, Daniels M, Twist C. Preliminary evidence of transient fatigue and pacing during interchanges in rugby league. Int J Sports Physiol Perform. 2013;8(2):157–164. PubMed
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Original Investigation
Physical Demands of Match Play in Successful and Less-Successful Elite Rugby League Teams 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. Keywords: winning, losing, activity profiles, team sport, time–motion analysis Rugby league is an international team sport in which games last 80 minutes (2 × 40-min halves).1,2 Like Australian football,3,4 soccer,5–7 and hockey,8 rugby league involves brief periods of highintensity running interspersed with longer periods of low-intensity activity.9–11 Rugby league players are often required to perform collisions, accelerations, and high-speed-running efforts with minimal recovery between efforts.11 Three or more sprints, high-intensity accelerations, or tackles with less than 21 seconds recovery between high-intensity efforts has been defined as a repeated high-intensityeffort bout.9,11 Notably, 70% of all repeated high-intensity-effort bouts occur in the 5-minute period preceding a try.9 Clearly, even though high-intensity exercise represents the minority of rugby league match play,9–11 success (or lack of success) may be influenced by a team’s ability (or inability) to perform repeated bouts of high-intensity activity. In soccer, temporary reductions in high-intensity activity follow the most intense passages of play.5,6,12–14 Mohr et al14 established that in the 5-minute period after the greatest amount of high-intensity running, there was a 12% reduction in high-intensity running compared with the average demands of a match. A more recent study also showed that high-intensity running decreases below the match average in the 5-minute period subsequent to the most intense phase of the game and then recovers in the following 5-minute period.6 However, it has also been demonstrated that ball-in-play time can differ between 5-minute periods of match play, therefore limiting the opportunity to perform physical work.15 Given that rugby league also has periods of intermittent high-intensity running coupled with Hulin and Gabbett are with the School of Exercise Science, Australian Catholic University, Brisbane, QLD, Australia. Kearney and Corvo are with Melbourne Storm Rugby League Club, Melbourne, Australia. Address author correspondence to Tim Gabbett at [email protected].
collisions,11 these methods of investigating temporary reductions in performance within a match could offer valuable insight into the performance fluctuations of rugby league match play. Furthermore, given that recent evidence has demonstrated that the phase of play (ie, attacking or defending) can affect the physical demands of match play,16 this factor must be considered, along with other confounding variables such as ball-in-play time.17 Kempton et al18 examined the effect of the most intense period of match play on physical and technical performance in elite senior and junior ball-playing positions (ie, hooker, halfback, five-eighth, and fullback) in rugby league teams having low success rates (elite, 36% of matches won; junior elite, 13% of matches won). These ball-playing positions have an important role of providing team structure and coordinating attacking movements,19 and the number and quality of their skill performances have been shown to reduce in the 5-minute period after the peak intensity period of match play.18 However, given that the ability to execute a range of skills while fatigued is able to discriminate between players competing at different levels of competition,20 these findings18 may not be representative of the fatigue response of players from teams with higher success rates. There is currently a lack of evidence relating to differences in the intensity of match play between successful and less-successful teams in rugby league. Although 1 study demonstrated that increases in total distance were related to greater competitive success of 1 team,21 there is a paucity of evidence comparing separate teams with high and low success rates. Studies from soccer have demonstrated that teams finishing in the bottom 5 ladder positions cover greater high-intensity-running distance than teams finishing in the top 5 positions.22 In contrast with earlier studies14 it has been highlighted that teams competing in higher divisions of competition cover less total distance and less high-speed distance than teams competing in lower divisions of competition.23 One of the few studies that 703
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have investigated match intensities between successful and lesssuccessful rugby league teams was conducted by dividing first- and second-half data into quartiles.24 The results demonstrated that playing intensities during the selected quartiles of match play differed between winning and losing semielite rugby league teams, with winning teams producing higher intensities than losing teams.24 Although that study only investigated these relationships among hookers, second-rowers, and locks (ie, backs and props were excluded), the findings clearly show that match intensities differ between successful and less-successful semielite rugby league teams. However, whether these relationships remain consistent among elite teams and all positional groups remains unclear. Further additions to the understanding of rugby league activity profiles could be offered by investigating differences in segmental activity profiles between all positional groups during a rugby league match and between successful and less-successful rugby league teams. Given that differences have been shown between wholematch activity profiles of winning and losing teams,21,25 differences in the within-match activity profiles may also exist between successful and less-successful teams. Therefore, the purpose of this study was to quantify activity profiles in approximately 5-minute 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 differs between successful and less-successful teams playing at an elite standard. In addition, we aimed to account for contextual factors such as ball-in-play time and time in attack and defense.
Methods Participants Thirty-one players (mean ± SD age 24.8 ± 3.1 y) from 2 elite rugby league teams participated in this study. Participants were competing in the Australian National Rugby League competition. All participants received a clear explanation of the study, including information on the risks and benefits, and written consent was obtained. All experimental procedures were approved by the institutional review board for human investigation.
Comparison of High- and Low-Success Teams Teams were split into high-success and low-success groups based on the percentage of matches won throughout the team’s season. The high-success team won 71% of matches played, whereas the low-success team won 58% of matches played. Although the difference in the percentage of matches won between the high- and low-success teams (71% vs 58%) may be considered small in a numerical sense, a difference of 13% in the most recent (2013) rugby league season differentiated between teams finishing with a final ladder position of 13th and 6th, with the former being excluded from the finals series and the latter being rewarded with a finals series match played on their home ground. Therefore, although these differences may be considered small, the consequences of such a difference could be quite large.
Global Positioning System Analysis Global positioning system (GPS) analysis was completed during 25 elite matches (15 for the high-success team, 10 for the lowsuccess team), equating to 200 GPS files. The mean ± SD satellite
availabilities for GPS data samples were 12 ± 1 (high-success team) and 11 ± 2 (low-success team). Positional groups were categorized into adjustables (hooker, halfback, five-eighth, and fullback), hit-up forwards (props, second-rowers, and locks), and outside backs (center and wing).9 As this study intended to quantify differences between successful and less-successful teams, interchange players were included in the analysis to investigate the activity profiles of each team as a whole. Movement was recorded using a MinimaxX GPS unit (Team S4, Catapult Innovations, Melbourne, Australia) sampling at 10 Hz. The GPS signal provided information on velocity, distance, position, and acceleration. The GPS unit also included a triaxial accelerometer and gyroscope sampling at 100 Hz, which provided information on physical collisions and repeated high-intensity efforts. The unit was worn in a small vest, on the upper back of the players. While recommendations for reporting GPS data have been presented,26 there is generally a wide range of reporting methods employed in the scientific literature.11,27,28 To date, there are no standardized methods for reporting velocity zones and several definitions of what constitutes an effort.3,11,26 Previous studies of teamsport demands have employed 5-Hz technology11,28 and as such have employed broad movement-velocity bands due to the larger measurement error with lower sampling frequencies.29,30 To allow comparisons with other researchers,11 data were categorized into movement-velocity bands corresponding to low (0–5 m/s) and high (>5 m/s) intensities, with total distance covered being represented by the sum of distances covered in these 2 intensity bands, number of collisions, and repeated high-intensity-effort bouts. A repeated high-intensity-effort bout was defined as 3 or more high-acceleration (≥2.79 m/s2),3 high-velocity, or contact efforts with less than 21 seconds recovery between efforts.11 All data were analyzed using Catapult Sprint software (version 5.1.0.1). The 10-Hz MinimaxX units have been shown to have acceptable validity and reliability for measuring sprinting velocities and accelerations.30,31 The MinimaxX units have also been shown to offer a valid measurement of collisions32 and repeated high-intensity efforts33 commonly observed in rugby league.
Segmental Data Analysis Commercially available match footage was coded for activity and recovery cycles by a member of the research team. Time when the ball was continuously in play was considered activity, while any stoppages during the match (eg, for scrums, penalties, line dropouts, tries, and video referee decisions) were considered recovery. The intraobserver reliability was assessed by coding 10 randomly selected matches. The intraclass correlation coefficients and typical error of measurement for the coding of activity and recovery cycles were .95 and 1.2% and .83 and 4.5%, respectively. First- and second-half GPS and ball-in-play data for each match were separated into 8 equal periods (mean ± SD 340 ± 28 s). Thus, each match was separated into 16 periods of approximately 5 minutes, based on the duration of each half. All GPS variables were analyzed in relation to the duration of each period. Periods that contained the greatest ball-in-play time, total distance, high-intensity-running distance, number of collisions, and number of repeated high-intensity-effort bouts were identified and compared with the subsequent period and the mean period (minus the peak and subsequent period). 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
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average demands of all other periods in a match (mean period). For the analysis of the peak, subsequent, and mean periods, files were eliminated from the analysis when the peak period occurred before halftime, full-time, or the player being interchanged. These files were maintained in the data set for the comparison of predefined periods between the 2 teams with high and low success rates.
No significant difference (51.1% ± 4.9% vs 49.9% ± 5.2%, P = .569) was found between the amounts of time high- and low-success teams spent in possession of the ball (ie, in attack).
Peak, Subsequent, and Mean Periods
Statistical Analysis
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Percentage of Match Play Spent in Possession of the Ball
Independent t tests were used to compare means of high- and low-success teams for the percentage of time spent in possession. Factorial analysis of variance (ANOVA) tests were used to identify differences between the peak, subsequent, and mean periods for the same team and to identify differences between peak, subsequent, and mean periods of high- and low-success teams. Any significant differences involving the 2 teams were followed up using a Tukey honestly significant difference post hoc test. As multiple comparisons were being made, a practical approach determining the scale of difference between high- and low-success teams was used for the analysis of the physical demands across predefined periods. Specifically, Cohen effect-size (ES) statistic and 90% confidence intervals (CI) were used to determine the magnitude of any differences.34 The magnitude of the ES was classified as trivial (2.1).35 Statistical significance was set at P < .05. Data are reported as mean ± SD.
Results Ball in Play The high- and low-success teams’ peak period of ball in play was significantly different from their subsequent (P = .002, ES = 1.4 ± 0.2; P = .001, ES = 1.4 ± 0.3, respectively) and mean period (P = .001, ES = 1.9 ± 0.7; P = .001, ES = 1.7 ± 0.2, respectively). No significant differences were found between the peak (P = .558), subsequent (P = .654), and mean ball-in-play periods (P = .867) of high- and low-success teams (Figure 1[A]). Trivial to small differences (ES = 0.1–0.3 ± 0.2) were found between ball-in-play periods of high- and low-success teams across predefined periods of match play (Figure 1[B]).
Figure 2 shows the total distance covered, high-intensity-running distance covered, number of collisions per minute, and number of repeated high-intensity-effort bouts per minute in the high- and low-success teams during the peak, subsequent, and mean periods. The peak period contained higher total distance for lesssuccessful adjustables (126 ± 14 vs 92 ± 12 m/min, P = .001, ES = 1.5 ± 0.4), hit-up forwards (126 ± 23 vs 97 ± 10 m/min, P = .001, ES = 1.8 ± 0.6), and outside backs (121 ± 15 vs 106 ± 17 m/min, P = .041, ES = 0.9 ± 0.7) than for the successful adjustables, hit-up forwards, and outside backs. Compared with their less-successful counterparts, adjustables, hit-up forwards, and outside backs from the high-success team covered lower total distance during the mean periods of match play (69 ± 10 vs 91 ± 14 m/min, P = .001, ES = 1.4 ± 0.5; 67 ± 9 vs 92 ± 9 m/min, P = .001, ES = 2.0 ± 0.3; and 68 ± 12 vs 88 ± 11 m/min, P = .001, ES = 1.3 ± 0.6, respectively). High-intensity-running distance was greater during the peak period for less-successful adjustables (17 ± 7 vs 8 ± 5 m/min, P = .001, ES = 1.1 ± 0.7) and hit-up forwards (17 ± 7 vs 9 ± 5 m/min, P = .001, ES = 1.4 ± 0.4) than for successful adjustables and hit-up forwards. The mean period for high-intensity-running distance for adjustables, hit-up forwards, and outside backs from the highsuccess team contained less high-intensity-running distance than for the same positional groups with lower success rates (adjustables, 2 ± 2 vs 6 ± 2 m/min, P = .001, ES = 1.4 ± 0.6; hit-up forwards, 3 ± 2 vs 6 ± 2 m/min, P = .001, ES = 1.7 ± 0.4; outside backs, 5 ± 2 vs 7 ± 2 m/min, P = .001, ES = 1.2 ± 0.7). Adjustables from the high-success team were involved in more collisions per minute than adjustables from the low-success team during the peak period (1.6 ± 0.5 vs 1.1 ± 0.4, P = .03, ES = 1.0 ± 0.5) and the mean period (0.8 ± 0.3 vs 0.4 ± 0.3, P = .025, ES = 1.1 ± 0.6). Successful hit-up forwards were involved in more collisions on average than less-successful hit-up forwards (0.8 ± 0.2 vs 0.7 ± 0.2, P = .005, ES = 0.5 ± 0.3). In the peak period, hit-up forwards and outside backs with higher success rates were involved in a smaller number of repeated high-intensity-effort bouts per minute than hit-up forwards and outside backs with low success rates (hit-up forwards, 0.3 ± 0.1 vs 0.5 ± 0.1, P = .008, ES = 0.7 ± 0.4; outside backs, 0.2 ± 0.1 vs 0.4 ± 0.1, P = .009, ES = 1.2 ± 0.7).
Physical Demands of Successful and LessSuccessful Teams During Predefined Periods
Figure 1 — (A) Peak, subsequent, and mean ball-in-play time and (B) ball-in-play time across predefined periods of match play, mean ± SD. #Significant difference (P < .05) from the subsequent and mean period for the same team.
Figure 3 shows the physical demands of selected periods of match play for the high- and low-success teams, for total distance covered, high-intensity-running distance, number of collisions per minute, and number of repeated high-intensity-effort bouts per minute. Adjustables from the low-success team covered greater total distance in periods 1, 3 to 9, and 11 to 16 (ES = 0.6 to 1.4 ± 0.5 to 1.0) and greater high-intensity-running distance in periods 1 to 5 and 9 to 16 (ES = 0.8 to 1.4 ± 0.6 to 1.4) and were involved in fewer collisions during periods 3, 5, 7, 9 to 11, and 13 (ES = 0.8 to 1.0 ± 0.6 to 0.7) than adjustables from the high-success team.
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Figure 2 — Peak, subsequent, and mean (A–C) total distance covered, (D–F) high-intensity-running (HIR) distance covered, (G–I) number of collisions per minute, and (J–L) number of repeated high-intensity-effort (RHIE) bouts per minute for adjustables, hit-up forwards, and outside backs from successful and less successful teams, mean ± SD. *Significant difference (P < 0.05) between teams for the same period. ΨSignificant difference (P < .05) from the subsequent period for the same team. #Significant difference (P < .05) from the mean period for the same team.
Compared with hit-up forwards from the high-success team, low-success hit-up forwards covered greater total distance in periods 2 to 16 (ES = 0.7 to 1.7 ± 0.4 to 0.5) and greater high-intensityrunning distance in periods 1, 3, 5, 6, and 8 to 16 (ES = 0.9 to 1.5 ± 0.8 to 1.1) and were involved in fewer collisions during periods 1, 2, and 10 (ES = 0.6 to 0.7 ± 0.3 to 0.5). Outside backs from the low-success team covered greater total distance in periods 2, 3, 5, 7, 9 to 11, 13, and 16 (ES = 1.0 to 1.3 ± 0.6 to 0.7) and greater high-intensity-running distance in periods 5, 8, and 16 (ES = 1.0 to 1.1 ± 0.6 to 0.7) than outside backs from the high-success team.
Discussion This study is the first to investigate the activity profiles of rugby league match play in 2 elite teams with differing success rates. The study provides conflicting results with previous research.21 In a study of 1 rugby league team, it was demonstrated that greater total distance was covered when winning than when losing, with the greater total distance achieved through greater low-intensity (0–5 m/s) distances (ie, no differences existed in high-intensity-running distance between winning or losing).21 However, it should be noted that the contrasting methods of the
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Figure 3 — Physical demands across predefined periods of match play for elite high- and low-success teams: (A–C) total distance covered, (D–F) highintensity-running (HIR) distance covered, (G–I) number of collisions per minute, and (J–L) number of repeated high-intensity-effort (RHIE) bouts per minute, mean ± SD. H denotes halftime. a Moderate effect size (0.61–1.20) between teams for the same period. b Large effect size (1.21–2.00) between teams for the same period.
previous and current studies may explain these conflicting results. Specifically, the current study investigated the activity profiles of 2 separate teams with differing success rates (ie, high-success team vs low-success team), whereas others21 investigated the results of 1 team (ie, 1 team when winning vs the same team when losing). Therefore, our conflicting results may reflect technical and tactical differences representing a greater distinguishing factor between successful and less-successful teams or success being the result of a multifactorial relationship between technical and tactical effectiveness and activity profiles more than merely activity profiles in isolation.
In contrast to some14,21 but not all22,23,25 studies, our results highlight that an ability to cover greater total distance or greater high-intensity distance does not result in greater competitive success. Moreover, our findings demonstrate that less-successful teams are subjected to a greater running workload in terms of total distance covered and distance covered at high intensities. Given that no significant differences were found for the percentage of time the 2 teams spent in possession of the ball, the differences in the average running demands is unlikely to be the result of a greater amount of time spent defending, which has been shown to elicit greater running demands than attacking.16 Furthermore, it has previously been
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hypothesized that the poorer match performance of losing teams results in a greater amount of scrambling in defense, which in turn increases the amount of high-intensity running performed.21 Indeed, poor defense and missed tackles in rugby league may result in the need for players to cover-defend and pursue members of the opposition, therefore resulting in greater high-intensity-running distance. Another novel finding of this study is that lower running workloads achieved by the high-success team were coupled with a greater number of collisions than for the low-success team. Recent evidence may offer possible explanations for our results. First, it has recently been demonstrated that the inclusion of collisions to small-sided games challenges a player’s ability to maintain running performance36,37 and results in elevated markers of muscle damage.38 Therefore, the high-success team’s involvement in a greater number of collisions may have resulted in a fatiguing effect that reduced running performance. Second, we would suggest a more plausible explanation is that the high-success cohort was able to effectively slow the “play-the-ball” while defending. This could be achieved by tactical strategies that employ more players to complete each tackle on more occasions (ie, 2 and 3 players per tackle on a greater number of occasions), effectively resulting in a greater number of collisions, as more players are involved in each tackle, and a slower play that may not require the need for players to cover greater total distance or high-intensity distance. In addition to the more successful cohort being involved in a greater number of collisions, the more successful hit-up forwards were involved in a moderately greater number of collisions during the initial periods of play (periods 1 and 2). These findings are not surprising given that repeated high-intensity-effort bouts that contain a greater number of contact efforts are more likely to occur in higherlevel competitors.39 Collectively, these data highlight that to ensure competitive success, not only is a player’s ability to be involved in repeated collisions vital, but also these collisions must occur in greater numbers during the initial stages of a match. Furthermore, the fact that our results demonstrated the greatest differences among hit-up forwards and adjustables may suggest that the workload of these positional groups has a greater effect on team success than that of the outside backs. The analysis of the physical demands throughout predefined periods demonstrates a trend for the total distance and high-intensity-running distance of the low-success team to increase during the latter periods of the second half (Figure 2[A], [D], [E], and [F]). These findings provide results that conflict with those of previous studies that have highlighted that physical output decreases in the second half of match play.6,40 However, recent evidence has also demonstrated that teams completing the season in the bottom 4 positions of the ladder increased their high-intensity-running distance in the second halves of matches, whereas teams finishing in the middle 4 and top 4 ladder positions decreased and maintained high-intensity-running distance, respectively.41 These observations have been supported by others24 who have demonstrated that less-successful teams display a greater “end spurt” than teams with higher success rates. The findings of the current and previous studies24,41 suggest that increases in physical output during the latter stages of match play may be linked with a poorer rate of success. Furthermore, these increases in running workloads could be the result of players’ preserving physical output during the earlier stages of match play, therefore facilitating an increase in intensity when anticipating the end of the match.42 Alternatively, this result may indeed be due to the inclusion of interchange players, a contrast in methodology that should be noted when comparing our findings with others.40
Recent evidence has demonstrated that predefined periods underestimate the peak demands of match play compared with a rolling time scale (ie, match intensities after every time point for the next 5-min period).43 As such, we highlight that these results should be interpreted with caution, due to our segmental periods’ being predefined. Finally, as only trivial and small differences were found between the ball-in-play time of the high- and low-success team across any of the predefined periods, we concluded that the differences in physical demands of the 2 teams is not the result of differences in ball-in-play time. However, given that significant differences were found in ball-in-play time across peak, subsequent, and mean periods, we are circumspect in suggesting that reductions in physical output after the peak period were the result of fatigue, as opportunities to perform physical work may have been limited.
Practical Applications The results of this study demonstrate that compared with a more successful team, a less-successful team covers greater total distance, performs more high-intensity running, and depending on position is involved in a greater number of repeated high-intensity-effort bouts. Therefore, these findings suggest that greater amounts of high-intensity activity and total distance are not related to success in elite rugby league. Our data suggest that a greater number of collisions is linked with a greater rate of success. These findings are of importance to coaches and sport scientists when interpreting time–motion analyses, as they may indicate that overall technical and tactical effectiveness of the team rather than greater running workloads may indeed be a greater determinant of success, or success may depend on a myriad of factors unrelated to activity profiles.
Conclusion Coaches and sport scientists should note that greater running workloads are not indicative of success in elite rugby league match play. These findings are of importance to practitioners when interpreting time–motion analyses, as this may indicate that technical and tactical differences may be the distinguishing factor between successful and less-successful teams or that success is the result of a multifactorial relationship between technical and tactical effectiveness and activity profiles, more than merely activity profiles in isolation.
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