International Journal of Sports Physiology and Performance, 2015, 10, 791 -801 http://dx.doi.org/10.1123/ijspp.2014-0004 © 2015 Human Kinetics, Inc.
Original Investigation
Activity Profile of International Rugby Sevens: Effect of Score Line, Opponent, and Substitutes Andrew M. Murray and Matthew C. Varley Purpose: To investigate the influence of score line, level of opposition, and timing of substitutes on the activity profile of rugby sevens players and describe peak periods of activity. Methods: Velocity and distance data were measured via 10-Hz GPS from 17 international-level male rugby sevens players on 2–20 occasions over 4 tournaments (24 matches). Movement data were reported as total distance (TD), high-speed-running distance (HSR, 4.17–10.0 m/s), and the occurrence of maximal accelerations (Accel, ≥2.78 m/s2). A rolling 1-min sample period was used. Results: Regardless of score line or opponent ranking there was a moderate to large reduction in average and peak TD and HSR between match halves. A close halftime score line was associated with a greater HSR distance in the 1st minute of the 1st and 2nd halves compared with when winning. When playing against higher- compared with lower-ranked opposition, players covered moderately greater TD in the 1st minute of the 1st half (difference = 26%; 90% confidence limits = 6, 49). Compared with players who played a full match, substitutes who came on late in the 2nd half had a higher average HSR and Accel by a small magnitude (31%; 5, 65 vs 34%; 6, 69) and a higher average TD by a moderate magnitude (16%; 5, 28). Conclusions: Match score line, opposition, and substitute timing can influence the activity profile of rugby sevens players. Players are likely to perform more running against higher opponents and when the score line is close. This information may influence team selection. Keywords: GPS, match analysis, physical performance, peak, fatigue Rugby sevens games are played by 14 players (7 per team) over 14 minutes (2 × 7-min halves, plus stoppage time). Squads consist of a maximum of 12 players. All 12 are eligible to play in each match, but each team may only make 3 substitutions from the 5 nonstarting players. Male international teams compete across 9 tournaments between October and May in the Sevens World Series. Typically the competition is across 2 days; pool matches are held on day 1 to determine a seeding for day 2, where teams compete for World Series points. The World Series is generally scheduled as sets of 2 tournaments with sets separated by 6 to 8 weeks. These consecutive tournaments are held in different countries, potentially on opposite sides of the world. Data exist on the physical demands of rugby union,1,2 league,3,4 and sevens.5–7 Intensified periods of activity have been investigated in other football codes, using predefined time intervals to identify peak periods of activity.8–10 However, predefined periods can underestimate the peak distance covered by up to 25% compared with using a rolling time scale.11 Thus, the use of rolling time periods provides a more sensitive method for identifying peak periods of activity. Currently the peak periods of running performed in rugby sevens are unknown. The activity profile of players in various football codes has shown evidence of pacing within3,12 and between matches.13 There are a number of variables that may influence a player’s activity and pacing strategy during competition. For example, player activity in Australian football and rugby league can be influenced by match Murray is with Aspire Academy, Doha, Qatar. Varley is with the Inst of Sport, Exercise and Active Living, Victoria University, Melbourne, Australia. Address author correspondence to Andrew Murray at andrew. [email protected].
score line14 and the ranking of the opposition.15 In rugby sevens, substitute players cover a greater total distance (TD) and highspeed-running (HSR) distance per minute than those who play a full match.6 Knowledge of how the different factors can influence match activity in rugby sevens can assist coaches in their tactical decisions before and during competition (eg, squad rotation and player substitutions). The aim of this study was to determine how match score line, the ranking of the opposition, and the use of substitutes may influence match activity. A secondary aim was to use rolling periods to establish peak periods of activity in rugby sevens players and to determine whether these periods are influenced by the aforementioned variables.
Methods Participants and Experimental Design Data were collected as part of an applied athlete support package in elite sport by support staff for the purpose of informing training and coaching decisions. Data were analyzed retrospectively for this study; therefore, ethical approval was not obtained but informed consent was.16 Player velocity was measured via global positioning system (GPS) units (10-Hz MinimaxX S4, Catapult Innovations, Australia) from 17 international-level male rugby sevens players on 2 to 20 occasions over 4 international tournaments (24 matches, 143 individual files). The GPS units were applied as previously reported.12 The mean ± SD number of available satellite signals during matches was 11.3 ± 1.4. Although we did not analyze positional differences of the 17 players primarily, 10 were backs and 7 were forwards. It is common in sevens for players to play a number of positions, and, as such, 3 players played across both lines. 791
792 Murray and Varley
Activity-Profile Measurements
Substitutes
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HSR17
Player-movement data were reported as TD and (4.17– 10.0 m/s) distance. In addition, occurrences of maximal accelerations (Accel, ≥2.78 m/s2) were recorded over a minimum duration of 0.4 seconds.12 Velocity data were calculated using the Doppler shift method, as opposed to the differentiation of positional data, as it is associated with a higher level of precision.18 The 10-Hz GPS is able to detect instantaneous changes of velocity during constant movement and accelerations, with a percentage bias of –3.6% to –0.6%, compared against a laser as the criterion measure, and a typical error expressed as a coefficient of variation of 3.1% to 8.3%.19 When assessed for measuring total distance during a team-sport simulation circuit these 10-Hz GPS units have a reported error of 25% to 75%, possibly; >75% to 95%, likely; >95% to 99%, very likely; >99%, almost certainly. To deal with the inflation of error in declaring a large number of effects as substantial, we adopted a strategy adapted from previous literature.23 First, no adjustment was made for effects that were only possibly substantial; these are regarded as potentially substantial but requiring more data from a larger sample before they can be implemented practically. Second, for each effect with a higher likelihood of being substantial, the probability that the effect was substantial but of opposite magnitude was calculated. These probabilities were then rank ordered, lowest to highest, and were summed sequentially until the probability exceeded 5%. The last effect contributing to this sum and all remaining effects were relabeled as possibly substantial. The overall type I error for the effects
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7s Activity Profile With Rolling Periods 793
labeled as likely, very likely, and almost certainly was therefore less than 5% (very unlikely), which is consistent with the acceptable error rate for a study with only a single effect. The magnitude of a given clear effect was determined from its observed standardized value (the difference in means divided by the between-subjects standard deviation) using the following scale: 7
26.7 ± 8.9
18.7 ± 8.2
–1.08 (–1.39 to –0.78)****
lose >7
27.4 ± 12.1
18.7 ± 8.5
–1.17 (–1.86 to –0.48)***
close
94 ± 36
75 ± 21
–0.77 (–1.17 to –0.37)***
win >7
83 ± 26
66 ± 24
–0.77 (–1.12 to –0.42)****
lose >7
79 ± 24
57 ± 16
–1.13 (–1.96 to –0.30)***
close
42 ± 20
46 ± 26
0.23 (–0.24–0.70)
win >7
22 ± 18
33 ± 24
0.81 (0.32–1.31)***
lose >7
41 ± 27
31 ± 25
–0.56 (–1.53–0.41)
close
103 ± 17
84 ± 17
–1.35 (–1.72 to –0.99)****
win >7
102 ± 13
87 ± 20
–1.10 (–1.40 to –0.80)****
lose >7
103 ± 17
83 ± 21
–1.45 (–2.14 to –0.77)****
close
188 ± 35
168 ± 26
–0.78 (–1.20 to –0.36)***
win >7
182 ± 28
165 ± 29
–0.66 (–1.01 to –0.32)***
lose >7
176 ± 20
142 ± 16
–1.48 (–2.30 to –0.66)***
close
119 ± 33
123 ± 41
0.13 (–0.32–0.58)
win >7
89 ± 31
101 ± 38
0.53 (0.11–0.95)**
lose >7
119 ± 21
105 ± 17
–0.55 (–1.42–0.32)
close
1.3 ± 0.6
1.1 ± 0.5
–0.34 (–0.66 to –0.01)**
win >7
1.3 ± 0.6
1.1 ± 0.6
–0.35 (–0.62 to –0.08)**
lose >7
1.1 ± 0.5
1.1 ± 0.6
–0.13 (–0.76–0.49)
close
3.8 ± 1.6
4.0 ± 1.9
0.11 (–0.28–0.49)
win >7
3.9 ± 1.6
3.5 ± 1.6
–0.22 (–0.55 to –0.10)*
lose >7
3.7 ± 1.4
3.6 ± 1.3
–0.08 (–0.82–0.67)
close
1.8 ± 1.3
2.1 ± 1.7
0.19 (–0.33–0.72)
win >7
1.5 ± 1.4
1.8 ± 1.4
0.30 (–0.16–0.77)*
lose >7
1.9 ± 1.7
1.3 ± 1.2
–0.58 (–1.66–0.50)
Average HSR (m/min) close
Peak HSR (m)
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First HSR (m)
Average TD (m/min)
Peak TD (m)
First TD (m)
Average Accel (count/min)
Peak Accel (count)
First Accel (count)
Abbreviations: HSR, high-speed running; TD, total distance’ Accel, acceleration; ES, effect size; CL, confidence limits. Close data = 8 matches, 10 players, 31 individual match files; win >7 data = 13 matches, 15 players, 46 individual match files; lose >7 data = 3 matches, 6 players, 9 individual match files. Clear substantial effects: *possibly, **likely, ***very likely, ****almost certainly.
794
IJSPP Vol. 10, No. 6, 2015
Table 3 Activity Profiles in the First and Second Halves of Male Full-Match Players During International Rugby Sevens Matches at Different Full-Time Score Lines 1st half, mean ± SD
2nd half, mean ± SD
2nd vs 1st half, ES (90% CL)
29.7 ± 10.9
17.5 ± 7.3
–1.62 (–1.97 to –1.28)****
win >7
25.3 ± 8.7
22.2 ± 7.9
–0.40 (–0.82–0.01)*
lose >7
28.8 ± 11.4
19 ± 7.2
–1.27 (–1.61 to –0.93)****
close
90 ± 34
67 ± 27
–1.04 (–1.44 to –0.63)****
win >7
81 ± 23
70 ± 22
–0.47 (–0.98–0.04)*
lose >7
86 ± 31
69 ± 19
–0.78 (–1.19––0.37)***
close
30 ± 17
41 ± 28
0.64 (0.14–1.14)**
win >7
26 ± 21
26 ± 15
0.00 (–0.73–0.73)
lose >7
36 ± 26
41 ± 25
0.31 (–0.18–0.80)*
close
103 ± 16
82 ± 17
–1.57 (–1.92 to –1.21)****
win >7
105 ± 12
97 ± 17
–0.54 (–0.96 to –0.12)**
lose >7
102 ± 16
82 ± 19
–1.44 (–1.80 to –1.08)****
close
184 ± 35
165 ± 30
–0.73 (–1.14 to –0.33)***
win >7
181 ± 21
174 ± 29
–0.28 (–0.78–0.23)
lose >7
185 ± 30
156 ± 23
–1.21 (–1.63 to –0.79)****
close
100 ± 33
112 ± 50
0.48 (0.02–0.95)*
win >7
100 ± 38
106 ± 22
0.24 (–0.35–0.82)
lose >7
109 ± 33
109 ± 34
–0.46 (–0.45–0.47)
close
1.2 ± 0.5
0.9 ± 0.5
–0.60 (–0.93 to –0.27)***
win >7
1.2 ± 0.6
1.2 ± 0.5
–0.10 (–0.49–0.28)
lose >7
1.3 ± 0.6
1.2 ± 0.6
–0.22 (–0.53–0.08)*
close
3.8 ± 1.7
3.4 ± 1.6
–0.30 (–0.69–0.09)*
win >7
3.8 ± 1.4
3.8 ± 1.6
0.00 (–0.48–0.48)
lose >7
3.9 ± 1.5
4.0 ± 1.9
0.06 (–0.32–0.44)
close
1.6 ± 1.5
2.2 ± 1.6
0.51 (–0.01–1.04)*
win >7
1.4 ± 1.2
1.7 ± 1.3
0.30 (–0.40–1.01)
lose >7
1.9 ± 1.4
1.7 ± 1.4
–0.26 (–0.79–0.27)
Average HSR (m/min) close
Peak HSR (m)
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First HSR (m)
Average TD (m/min)
Peak TD (m)
First TD (m)
Average Accel (count/min)
Peak Accel (count)
First Accel (count)
Abbreviations: HSR, high-speed running; TD, total distance’ Accel, acceleration; ES, effect size; CL, confidence limits. Close data = 8 matches, 11 players, 33 individual match files; win >7 data = 8 matches, 11 players, 21 individual match files; lose >7 data = 8 matches, 12 players, 32 individual match files. Clear substantial effects: *possibly, **likely, ***very likely, ****almost certainly.
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Figure 1 — Standardized differences between average high-speed running (HSR), total distance covered (TD), and count of accelerations (Accel) of male full-match players during international rugby sevens matches at various halftime (HT) and full-time (FT) score lines in the first and second halves. (A) Average HSR (m/min), (B) average TD (m/min), and (C) average Accel (counts/min). Close = 7 points or less difference between team scores, lose >7 = reference team was losing by more than 7 points, win >7 = reference team was winning by more than 7 points. Shaded column represents the smallest important difference. Quantitative chances of higher or lower differences are evaluated according to thresholds identified in statistical analysis: *possibly, **likely, ***very likely, ****almost certainly. 796
Figure 2 — Standardized differences between peak high-speed running (HSR), total distance covered (TD), and number of accelerations (Accel) performed in a 1-minute period (peak) of male full-match players during international rugby sevens matches at various halftime (HT) and full-time (FT) score lines in the first and second halves. (A) Peak HSR (m), (B) peak TD (m), (C) peak Accel (count). Close = 7 points or less difference between team scores, lose >7 = reference team was losing by more than 7 points, win >7 = reference team was winning by more than 7 points. Shaded column represents the smallest important difference. Quantitative chances of higher or lower differences are evaluated according to thresholds identified in statistical analysis: *possibly, **likely, ***very likely, ****almost certainly.
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Substitutes The activity-profile data for players who played a full match or were early or late subs are presented in Table 5. Early subs were on the field for 430 ± 86 seconds (ie, came on at or around halftime) while late subs were on for 196 ± 79 seconds (ie, final 3–4 min). Players who came on as late subs performed greater average HSR and Accel of a small magnitude and a greater average TD of a moderate magnitude than those who played a full match (31%; 5, 65, 34%; 6, 69 and 16%; 5, 28). Late subs performed a lower peak HSR by a moderate magnitude compared with those who played a full match (–24%; –38, –7) and compared with early subs (–28%; –42, –12). Late subs performed a lower peak TD by a moderate magnitude compared with early subs and those who played a full match (–10%; –18, –2 and –15%; –22, –7).
Discussion
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In this study we characterized the activity profiles of internationallevel rugby sevens players using rolling periods and for the first time described peak periods of activity. In addition, the results demonstrate that score line, the level of opposition, and the time at which a player is introduced into the match can influence match activity profile.
Full-Match Activity Profile
Figure 3 — Standardized differences between high-speed running (HSR), total distance covered (TD), and count of accelerations (Accel) performed in the first 1-minute period at the start of each half (first) of male full-match players during international rugby sevens matches at various halftime (HT) and full-time (FT) score lines in the first and second halves. (A) First HSR (m), (B) first TD (m), (C) first Accel (count). Close = 7 points or less difference between team scores, lose >7 = reference team was losing by more than 7 points, win >7 = reference team was winning by more than 7 points. Shaded column represents the smallest important difference. Quantitative chances of higher or lower differences are evaluated according to thresholds identified in statistical analysis: *possibly, **likely, ***very likely, ****almost certainly.
Using rolling periods we have shown, for the first time, the peak TD and HSR distances covered in a 1-minute period (183 ± 30 and 86 ± 30 m/min, respectively). These peak distances are influenced by variables such as the quality of the opponent (higher against better opponents, Table 4). Thus, while the peak periods represent only the average of the true peak distances a player may cover and are likely to be subject to variation across individuals and positions, they offer insight into the high running demand for which rugby sevens players must be physically prepared. The Scottish international players in this study covered an average TD similar to those of Australian international25 and Spanish domestic26 rugby sevens players (~100 m/min). Furthermore, when the distances covered above ~4 m/s in the aforementioned studies were combined to represent a threshold similar to that used in this study, all players covered a similar average HSR distance. The average number of Accel per minute reported in this study (1.3/ min) was similar to that in Australian international players (~1.6/ min6; using a threshold of >2 m/s2 compared with 2.78 m/s2 in this study). Notably, the players in this study performed a threefold higher number of Accel per minute in a peak period than the average (Table 1). As it is more energetically demanding to accelerate than it is to move at a constant velocity,27 this information further highlights the high-intensity requirements of international rugby sevens. Further work may investigate the impact of collisions or high-intensity bouts28 during peak periods of activity.
Relationship Between Score Line and Activity Profile Research is equivocal in other football codes as to whether a close score line leads to an increased amount of running. In rugby league, moderate and large winning margins were associated with greater relative distances compared with losing.15 In Australian football, HSR distance per minute, sprints per minute, and peak speed were higher for players in losing quarters.14 Furthermore, smaller score
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798 Murray and Varley
Table 4 Activity Profiles in the First and Second Halves of Male Full-Match Players During International Rugby Sevens Matches When Playing Against a Higher- or Lower-Ranked Opponent 1st half, mean ± SD
2nd half, mean ± SD
2nd vs 1st half, ES (90% CL)
higher-ranked opponent
31.5 ± 9.8
20.5 ± 6.4
–1.31 (–1.63 to –0.99)****
lower-ranked opponent
25.8 ± 10.7
18.2 ± 8.3
–1.07 (–1.38 to –0.77)****
higher-ranked opponent
95 ± 27
72 ± 20
–0.98 (–1.35 to –0.61)****
lower-ranked opponent
80 ± 31
66 ± 25
–0.67 (–1.02 to –0.32)***
higher-ranked opponent
39 ± 24
40 ± 26
0.09 (–0.36–0.54)
lower-ranked opponent
26 ± 18
35 ± 24
0.68 (0.23–1.14)***
higher-ranked opponent
107 ± 14
89 ± 15
–1.25 (–1.58 to –0.92)****
lower-ranked opponent
99 ± 15
83 ± 21
–1.23 (–1.53 to –0.93)****
higher-ranked opponent
192 ± 26
166 ± 28
–1.00 (–1.38 to –0.61)****
lower-ranked opponent
177 ± 31
162 ± 28
–0.62 (–0.96 to –0.28)***
higher-ranked opponent
118 ± 33
116 ± 37
–0.08 (–0.49–0.34)
lower-ranked opponent
92 ± 31
105 ± 40
0.54 (0.14–0.94)**
higher-ranked opponent
1.2 ± 0.5
1.2 ± 0.6
0.04 (–0.25–0.32)
lower-ranked opponent
1.3 ± 0.6
1.0 ± 0.5
–0.63 (–0.90 to –0.37)
higher-ranked opponent
3.7 ± 1.4
4.1 ± 1.8
0.25 (–0.10–0.60)*
lower-ranked opponent
3.9 ± 1.7
3.4 ± 1.5
–0.36 (–0.68 to –0.05)**
higher-ranked opponent
2.0 ± 1.5
1.9 ± 1.5
–0.09 (–0.57–0.38)
lower-ranked opponent
1.4 ± 1.3
1.8 ± 1.54
0.42 (–0.04–0.88)*
Average HSR (m/min)
Peak HSR (m)
First HSR (m)
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Average TD (m/min)
Peak TD (m)
First TD (m)
Average Accel (count/min)
Peak Accel (count)
First Accel (count)
Abbreviations: HSR, high-speed running; TD, total distance’ Accel, acceleration; ES, effect size; CL, confidence limits. Higher-ranked opponent data = 10 matches, 11 players, 37 individual match files; lower-ranked opponent data = 14 matches, 15 players, 49 individual match files. Clear substantial effects: *possibly, **likely, ***very likely, ****almost certainly.
differentials were associated with increased TD and HSR distance. This pattern is similar to our finding that players covered the greatest peak HSR in the second half when the halftime score differential was close (Figure 2[A]). Notably, when the halftime score was close, players performed more HSR in the first minute of the second half than when winning (Figure 3[A]). Similarly, players performed greater peak HSR in both the first and second halves when the halftime score was close. This finding supports the idea that a close score line will encourage players to work harder, as they may feel that the outcome of the match is still in contention. This higher work rate may not be to the team’s benefit as it may result in players fatiguing earlier in the second half. This may explain why a higher HSR pattern was not associated with a close full-time score. Thus, the effect of score line on match running may be influenced by a combination of the time at which a certain score line occurs and the match running performed to
that point (eg, the longer a score line stays close the longer a player may run at a greater intensity, which may influence the player’s ability to respond to further changes in score line). There may be implications for the use of tactical substitutions late in the match to maintain a high level of match running that pressures opponents in both attack and defense.6 There were not enough data available to explore the relationship between the activity profile across the whole match based on the real-time relative score line.
Effect of Opposition When playing against higher- compared with lower-ranked opponents the players in this study covered a greater TD in the first minute of the first half (Figure 4). Regardless of opponent ranking, peak and average TD and HSR were reduced in the second half compared with the first (Table 4). However, when playing lower-ranked opponents,
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with the first minute of the match, variables other than fatigue (ie, opponent and halftime score line) may have a greater influence on movement at the start of the second half. Future research should look at the interaction between score line and opposition ranking on match running.
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Impact of Substitutes Players who were late subs covered a greater average TD than players who played a full match. Late subs had higher average HSR and Accel than players who played a full match. This pattern is in agreement with previous findings that substitutes (on the field
Original Investigation
Activity Profile of International Rugby Sevens: Effect of Score Line, Opponent, and Substitutes Andrew M. Murray and Matthew C. Varley Purpose: To investigate the influence of score line, level of opposition, and timing of substitutes on the activity profile of rugby sevens players and describe peak periods of activity. Methods: Velocity and distance data were measured via 10-Hz GPS from 17 international-level male rugby sevens players on 2–20 occasions over 4 tournaments (24 matches). Movement data were reported as total distance (TD), high-speed-running distance (HSR, 4.17–10.0 m/s), and the occurrence of maximal accelerations (Accel, ≥2.78 m/s2). A rolling 1-min sample period was used. Results: Regardless of score line or opponent ranking there was a moderate to large reduction in average and peak TD and HSR between match halves. A close halftime score line was associated with a greater HSR distance in the 1st minute of the 1st and 2nd halves compared with when winning. When playing against higher- compared with lower-ranked opposition, players covered moderately greater TD in the 1st minute of the 1st half (difference = 26%; 90% confidence limits = 6, 49). Compared with players who played a full match, substitutes who came on late in the 2nd half had a higher average HSR and Accel by a small magnitude (31%; 5, 65 vs 34%; 6, 69) and a higher average TD by a moderate magnitude (16%; 5, 28). Conclusions: Match score line, opposition, and substitute timing can influence the activity profile of rugby sevens players. Players are likely to perform more running against higher opponents and when the score line is close. This information may influence team selection. Keywords: GPS, match analysis, physical performance, peak, fatigue Rugby sevens games are played by 14 players (7 per team) over 14 minutes (2 × 7-min halves, plus stoppage time). Squads consist of a maximum of 12 players. All 12 are eligible to play in each match, but each team may only make 3 substitutions from the 5 nonstarting players. Male international teams compete across 9 tournaments between October and May in the Sevens World Series. Typically the competition is across 2 days; pool matches are held on day 1 to determine a seeding for day 2, where teams compete for World Series points. The World Series is generally scheduled as sets of 2 tournaments with sets separated by 6 to 8 weeks. These consecutive tournaments are held in different countries, potentially on opposite sides of the world. Data exist on the physical demands of rugby union,1,2 league,3,4 and sevens.5–7 Intensified periods of activity have been investigated in other football codes, using predefined time intervals to identify peak periods of activity.8–10 However, predefined periods can underestimate the peak distance covered by up to 25% compared with using a rolling time scale.11 Thus, the use of rolling time periods provides a more sensitive method for identifying peak periods of activity. Currently the peak periods of running performed in rugby sevens are unknown. The activity profile of players in various football codes has shown evidence of pacing within3,12 and between matches.13 There are a number of variables that may influence a player’s activity and pacing strategy during competition. For example, player activity in Australian football and rugby league can be influenced by match Murray is with Aspire Academy, Doha, Qatar. Varley is with the Inst of Sport, Exercise and Active Living, Victoria University, Melbourne, Australia. Address author correspondence to Andrew Murray at andrew. [email protected].
score line14 and the ranking of the opposition.15 In rugby sevens, substitute players cover a greater total distance (TD) and highspeed-running (HSR) distance per minute than those who play a full match.6 Knowledge of how the different factors can influence match activity in rugby sevens can assist coaches in their tactical decisions before and during competition (eg, squad rotation and player substitutions). The aim of this study was to determine how match score line, the ranking of the opposition, and the use of substitutes may influence match activity. A secondary aim was to use rolling periods to establish peak periods of activity in rugby sevens players and to determine whether these periods are influenced by the aforementioned variables.
Methods Participants and Experimental Design Data were collected as part of an applied athlete support package in elite sport by support staff for the purpose of informing training and coaching decisions. Data were analyzed retrospectively for this study; therefore, ethical approval was not obtained but informed consent was.16 Player velocity was measured via global positioning system (GPS) units (10-Hz MinimaxX S4, Catapult Innovations, Australia) from 17 international-level male rugby sevens players on 2 to 20 occasions over 4 international tournaments (24 matches, 143 individual files). The GPS units were applied as previously reported.12 The mean ± SD number of available satellite signals during matches was 11.3 ± 1.4. Although we did not analyze positional differences of the 17 players primarily, 10 were backs and 7 were forwards. It is common in sevens for players to play a number of positions, and, as such, 3 players played across both lines. 791
792 Murray and Varley
Activity-Profile Measurements
Substitutes
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HSR17
Player-movement data were reported as TD and (4.17– 10.0 m/s) distance. In addition, occurrences of maximal accelerations (Accel, ≥2.78 m/s2) were recorded over a minimum duration of 0.4 seconds.12 Velocity data were calculated using the Doppler shift method, as opposed to the differentiation of positional data, as it is associated with a higher level of precision.18 The 10-Hz GPS is able to detect instantaneous changes of velocity during constant movement and accelerations, with a percentage bias of –3.6% to –0.6%, compared against a laser as the criterion measure, and a typical error expressed as a coefficient of variation of 3.1% to 8.3%.19 When assessed for measuring total distance during a team-sport simulation circuit these 10-Hz GPS units have a reported error of 25% to 75%, possibly; >75% to 95%, likely; >95% to 99%, very likely; >99%, almost certainly. To deal with the inflation of error in declaring a large number of effects as substantial, we adopted a strategy adapted from previous literature.23 First, no adjustment was made for effects that were only possibly substantial; these are regarded as potentially substantial but requiring more data from a larger sample before they can be implemented practically. Second, for each effect with a higher likelihood of being substantial, the probability that the effect was substantial but of opposite magnitude was calculated. These probabilities were then rank ordered, lowest to highest, and were summed sequentially until the probability exceeded 5%. The last effect contributing to this sum and all remaining effects were relabeled as possibly substantial. The overall type I error for the effects
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7s Activity Profile With Rolling Periods 793
labeled as likely, very likely, and almost certainly was therefore less than 5% (very unlikely), which is consistent with the acceptable error rate for a study with only a single effect. The magnitude of a given clear effect was determined from its observed standardized value (the difference in means divided by the between-subjects standard deviation) using the following scale: 7
26.7 ± 8.9
18.7 ± 8.2
–1.08 (–1.39 to –0.78)****
lose >7
27.4 ± 12.1
18.7 ± 8.5
–1.17 (–1.86 to –0.48)***
close
94 ± 36
75 ± 21
–0.77 (–1.17 to –0.37)***
win >7
83 ± 26
66 ± 24
–0.77 (–1.12 to –0.42)****
lose >7
79 ± 24
57 ± 16
–1.13 (–1.96 to –0.30)***
close
42 ± 20
46 ± 26
0.23 (–0.24–0.70)
win >7
22 ± 18
33 ± 24
0.81 (0.32–1.31)***
lose >7
41 ± 27
31 ± 25
–0.56 (–1.53–0.41)
close
103 ± 17
84 ± 17
–1.35 (–1.72 to –0.99)****
win >7
102 ± 13
87 ± 20
–1.10 (–1.40 to –0.80)****
lose >7
103 ± 17
83 ± 21
–1.45 (–2.14 to –0.77)****
close
188 ± 35
168 ± 26
–0.78 (–1.20 to –0.36)***
win >7
182 ± 28
165 ± 29
–0.66 (–1.01 to –0.32)***
lose >7
176 ± 20
142 ± 16
–1.48 (–2.30 to –0.66)***
close
119 ± 33
123 ± 41
0.13 (–0.32–0.58)
win >7
89 ± 31
101 ± 38
0.53 (0.11–0.95)**
lose >7
119 ± 21
105 ± 17
–0.55 (–1.42–0.32)
close
1.3 ± 0.6
1.1 ± 0.5
–0.34 (–0.66 to –0.01)**
win >7
1.3 ± 0.6
1.1 ± 0.6
–0.35 (–0.62 to –0.08)**
lose >7
1.1 ± 0.5
1.1 ± 0.6
–0.13 (–0.76–0.49)
close
3.8 ± 1.6
4.0 ± 1.9
0.11 (–0.28–0.49)
win >7
3.9 ± 1.6
3.5 ± 1.6
–0.22 (–0.55 to –0.10)*
lose >7
3.7 ± 1.4
3.6 ± 1.3
–0.08 (–0.82–0.67)
close
1.8 ± 1.3
2.1 ± 1.7
0.19 (–0.33–0.72)
win >7
1.5 ± 1.4
1.8 ± 1.4
0.30 (–0.16–0.77)*
lose >7
1.9 ± 1.7
1.3 ± 1.2
–0.58 (–1.66–0.50)
Average HSR (m/min) close
Peak HSR (m)
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First HSR (m)
Average TD (m/min)
Peak TD (m)
First TD (m)
Average Accel (count/min)
Peak Accel (count)
First Accel (count)
Abbreviations: HSR, high-speed running; TD, total distance’ Accel, acceleration; ES, effect size; CL, confidence limits. Close data = 8 matches, 10 players, 31 individual match files; win >7 data = 13 matches, 15 players, 46 individual match files; lose >7 data = 3 matches, 6 players, 9 individual match files. Clear substantial effects: *possibly, **likely, ***very likely, ****almost certainly.
794
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Table 3 Activity Profiles in the First and Second Halves of Male Full-Match Players During International Rugby Sevens Matches at Different Full-Time Score Lines 1st half, mean ± SD
2nd half, mean ± SD
2nd vs 1st half, ES (90% CL)
29.7 ± 10.9
17.5 ± 7.3
–1.62 (–1.97 to –1.28)****
win >7
25.3 ± 8.7
22.2 ± 7.9
–0.40 (–0.82–0.01)*
lose >7
28.8 ± 11.4
19 ± 7.2
–1.27 (–1.61 to –0.93)****
close
90 ± 34
67 ± 27
–1.04 (–1.44 to –0.63)****
win >7
81 ± 23
70 ± 22
–0.47 (–0.98–0.04)*
lose >7
86 ± 31
69 ± 19
–0.78 (–1.19––0.37)***
close
30 ± 17
41 ± 28
0.64 (0.14–1.14)**
win >7
26 ± 21
26 ± 15
0.00 (–0.73–0.73)
lose >7
36 ± 26
41 ± 25
0.31 (–0.18–0.80)*
close
103 ± 16
82 ± 17
–1.57 (–1.92 to –1.21)****
win >7
105 ± 12
97 ± 17
–0.54 (–0.96 to –0.12)**
lose >7
102 ± 16
82 ± 19
–1.44 (–1.80 to –1.08)****
close
184 ± 35
165 ± 30
–0.73 (–1.14 to –0.33)***
win >7
181 ± 21
174 ± 29
–0.28 (–0.78–0.23)
lose >7
185 ± 30
156 ± 23
–1.21 (–1.63 to –0.79)****
close
100 ± 33
112 ± 50
0.48 (0.02–0.95)*
win >7
100 ± 38
106 ± 22
0.24 (–0.35–0.82)
lose >7
109 ± 33
109 ± 34
–0.46 (–0.45–0.47)
close
1.2 ± 0.5
0.9 ± 0.5
–0.60 (–0.93 to –0.27)***
win >7
1.2 ± 0.6
1.2 ± 0.5
–0.10 (–0.49–0.28)
lose >7
1.3 ± 0.6
1.2 ± 0.6
–0.22 (–0.53–0.08)*
close
3.8 ± 1.7
3.4 ± 1.6
–0.30 (–0.69–0.09)*
win >7
3.8 ± 1.4
3.8 ± 1.6
0.00 (–0.48–0.48)
lose >7
3.9 ± 1.5
4.0 ± 1.9
0.06 (–0.32–0.44)
close
1.6 ± 1.5
2.2 ± 1.6
0.51 (–0.01–1.04)*
win >7
1.4 ± 1.2
1.7 ± 1.3
0.30 (–0.40–1.01)
lose >7
1.9 ± 1.4
1.7 ± 1.4
–0.26 (–0.79–0.27)
Average HSR (m/min) close
Peak HSR (m)
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First HSR (m)
Average TD (m/min)
Peak TD (m)
First TD (m)
Average Accel (count/min)
Peak Accel (count)
First Accel (count)
Abbreviations: HSR, high-speed running; TD, total distance’ Accel, acceleration; ES, effect size; CL, confidence limits. Close data = 8 matches, 11 players, 33 individual match files; win >7 data = 8 matches, 11 players, 21 individual match files; lose >7 data = 8 matches, 12 players, 32 individual match files. Clear substantial effects: *possibly, **likely, ***very likely, ****almost certainly.
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Figure 1 — Standardized differences between average high-speed running (HSR), total distance covered (TD), and count of accelerations (Accel) of male full-match players during international rugby sevens matches at various halftime (HT) and full-time (FT) score lines in the first and second halves. (A) Average HSR (m/min), (B) average TD (m/min), and (C) average Accel (counts/min). Close = 7 points or less difference between team scores, lose >7 = reference team was losing by more than 7 points, win >7 = reference team was winning by more than 7 points. Shaded column represents the smallest important difference. Quantitative chances of higher or lower differences are evaluated according to thresholds identified in statistical analysis: *possibly, **likely, ***very likely, ****almost certainly. 796
Figure 2 — Standardized differences between peak high-speed running (HSR), total distance covered (TD), and number of accelerations (Accel) performed in a 1-minute period (peak) of male full-match players during international rugby sevens matches at various halftime (HT) and full-time (FT) score lines in the first and second halves. (A) Peak HSR (m), (B) peak TD (m), (C) peak Accel (count). Close = 7 points or less difference between team scores, lose >7 = reference team was losing by more than 7 points, win >7 = reference team was winning by more than 7 points. Shaded column represents the smallest important difference. Quantitative chances of higher or lower differences are evaluated according to thresholds identified in statistical analysis: *possibly, **likely, ***very likely, ****almost certainly.
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Substitutes The activity-profile data for players who played a full match or were early or late subs are presented in Table 5. Early subs were on the field for 430 ± 86 seconds (ie, came on at or around halftime) while late subs were on for 196 ± 79 seconds (ie, final 3–4 min). Players who came on as late subs performed greater average HSR and Accel of a small magnitude and a greater average TD of a moderate magnitude than those who played a full match (31%; 5, 65, 34%; 6, 69 and 16%; 5, 28). Late subs performed a lower peak HSR by a moderate magnitude compared with those who played a full match (–24%; –38, –7) and compared with early subs (–28%; –42, –12). Late subs performed a lower peak TD by a moderate magnitude compared with early subs and those who played a full match (–10%; –18, –2 and –15%; –22, –7).
Discussion
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In this study we characterized the activity profiles of internationallevel rugby sevens players using rolling periods and for the first time described peak periods of activity. In addition, the results demonstrate that score line, the level of opposition, and the time at which a player is introduced into the match can influence match activity profile.
Full-Match Activity Profile
Figure 3 — Standardized differences between high-speed running (HSR), total distance covered (TD), and count of accelerations (Accel) performed in the first 1-minute period at the start of each half (first) of male full-match players during international rugby sevens matches at various halftime (HT) and full-time (FT) score lines in the first and second halves. (A) First HSR (m), (B) first TD (m), (C) first Accel (count). Close = 7 points or less difference between team scores, lose >7 = reference team was losing by more than 7 points, win >7 = reference team was winning by more than 7 points. Shaded column represents the smallest important difference. Quantitative chances of higher or lower differences are evaluated according to thresholds identified in statistical analysis: *possibly, **likely, ***very likely, ****almost certainly.
Using rolling periods we have shown, for the first time, the peak TD and HSR distances covered in a 1-minute period (183 ± 30 and 86 ± 30 m/min, respectively). These peak distances are influenced by variables such as the quality of the opponent (higher against better opponents, Table 4). Thus, while the peak periods represent only the average of the true peak distances a player may cover and are likely to be subject to variation across individuals and positions, they offer insight into the high running demand for which rugby sevens players must be physically prepared. The Scottish international players in this study covered an average TD similar to those of Australian international25 and Spanish domestic26 rugby sevens players (~100 m/min). Furthermore, when the distances covered above ~4 m/s in the aforementioned studies were combined to represent a threshold similar to that used in this study, all players covered a similar average HSR distance. The average number of Accel per minute reported in this study (1.3/ min) was similar to that in Australian international players (~1.6/ min6; using a threshold of >2 m/s2 compared with 2.78 m/s2 in this study). Notably, the players in this study performed a threefold higher number of Accel per minute in a peak period than the average (Table 1). As it is more energetically demanding to accelerate than it is to move at a constant velocity,27 this information further highlights the high-intensity requirements of international rugby sevens. Further work may investigate the impact of collisions or high-intensity bouts28 during peak periods of activity.
Relationship Between Score Line and Activity Profile Research is equivocal in other football codes as to whether a close score line leads to an increased amount of running. In rugby league, moderate and large winning margins were associated with greater relative distances compared with losing.15 In Australian football, HSR distance per minute, sprints per minute, and peak speed were higher for players in losing quarters.14 Furthermore, smaller score
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798 Murray and Varley
Table 4 Activity Profiles in the First and Second Halves of Male Full-Match Players During International Rugby Sevens Matches When Playing Against a Higher- or Lower-Ranked Opponent 1st half, mean ± SD
2nd half, mean ± SD
2nd vs 1st half, ES (90% CL)
higher-ranked opponent
31.5 ± 9.8
20.5 ± 6.4
–1.31 (–1.63 to –0.99)****
lower-ranked opponent
25.8 ± 10.7
18.2 ± 8.3
–1.07 (–1.38 to –0.77)****
higher-ranked opponent
95 ± 27
72 ± 20
–0.98 (–1.35 to –0.61)****
lower-ranked opponent
80 ± 31
66 ± 25
–0.67 (–1.02 to –0.32)***
higher-ranked opponent
39 ± 24
40 ± 26
0.09 (–0.36–0.54)
lower-ranked opponent
26 ± 18
35 ± 24
0.68 (0.23–1.14)***
higher-ranked opponent
107 ± 14
89 ± 15
–1.25 (–1.58 to –0.92)****
lower-ranked opponent
99 ± 15
83 ± 21
–1.23 (–1.53 to –0.93)****
higher-ranked opponent
192 ± 26
166 ± 28
–1.00 (–1.38 to –0.61)****
lower-ranked opponent
177 ± 31
162 ± 28
–0.62 (–0.96 to –0.28)***
higher-ranked opponent
118 ± 33
116 ± 37
–0.08 (–0.49–0.34)
lower-ranked opponent
92 ± 31
105 ± 40
0.54 (0.14–0.94)**
higher-ranked opponent
1.2 ± 0.5
1.2 ± 0.6
0.04 (–0.25–0.32)
lower-ranked opponent
1.3 ± 0.6
1.0 ± 0.5
–0.63 (–0.90 to –0.37)
higher-ranked opponent
3.7 ± 1.4
4.1 ± 1.8
0.25 (–0.10–0.60)*
lower-ranked opponent
3.9 ± 1.7
3.4 ± 1.5
–0.36 (–0.68 to –0.05)**
higher-ranked opponent
2.0 ± 1.5
1.9 ± 1.5
–0.09 (–0.57–0.38)
lower-ranked opponent
1.4 ± 1.3
1.8 ± 1.54
0.42 (–0.04–0.88)*
Average HSR (m/min)
Peak HSR (m)
First HSR (m)
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Average TD (m/min)
Peak TD (m)
First TD (m)
Average Accel (count/min)
Peak Accel (count)
First Accel (count)
Abbreviations: HSR, high-speed running; TD, total distance’ Accel, acceleration; ES, effect size; CL, confidence limits. Higher-ranked opponent data = 10 matches, 11 players, 37 individual match files; lower-ranked opponent data = 14 matches, 15 players, 49 individual match files. Clear substantial effects: *possibly, **likely, ***very likely, ****almost certainly.
differentials were associated with increased TD and HSR distance. This pattern is similar to our finding that players covered the greatest peak HSR in the second half when the halftime score differential was close (Figure 2[A]). Notably, when the halftime score was close, players performed more HSR in the first minute of the second half than when winning (Figure 3[A]). Similarly, players performed greater peak HSR in both the first and second halves when the halftime score was close. This finding supports the idea that a close score line will encourage players to work harder, as they may feel that the outcome of the match is still in contention. This higher work rate may not be to the team’s benefit as it may result in players fatiguing earlier in the second half. This may explain why a higher HSR pattern was not associated with a close full-time score. Thus, the effect of score line on match running may be influenced by a combination of the time at which a certain score line occurs and the match running performed to
that point (eg, the longer a score line stays close the longer a player may run at a greater intensity, which may influence the player’s ability to respond to further changes in score line). There may be implications for the use of tactical substitutions late in the match to maintain a high level of match running that pressures opponents in both attack and defense.6 There were not enough data available to explore the relationship between the activity profile across the whole match based on the real-time relative score line.
Effect of Opposition When playing against higher- compared with lower-ranked opponents the players in this study covered a greater TD in the first minute of the first half (Figure 4). Regardless of opponent ranking, peak and average TD and HSR were reduced in the second half compared with the first (Table 4). However, when playing lower-ranked opponents,
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with the first minute of the match, variables other than fatigue (ie, opponent and halftime score line) may have a greater influence on movement at the start of the second half. Future research should look at the interaction between score line and opposition ranking on match running.
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Impact of Substitutes Players who were late subs covered a greater average TD than players who played a full match. Late subs had higher average HSR and Accel than players who played a full match. This pattern is in agreement with previous findings that substitutes (on the field
