Rugby union is a physically demanding, high-intensity intermittent team sport that requires well-developed technical, tactical, and physical capabilities (10). Given the limited time available to train these qualities, small-sided games (SSGs) have been suggested as an alternative to traditional generic training methods for rugby union (16) and other team sports (3,13,20,24). Indeed, SSGs are now commonly used to improve technical and tactical abilities in team sport athletes (18) and provide an aerobic stimulus comparable with that of traditional interval training methods (13,24). It has been shown that the perceptual (rating of perceived exertion [RPE]) and physiological load (heart rate [HR], blood lactate) experienced during soccer-specific SSGs can be controlled by manipulating exercise prescription factors such as field size, player number, rule modifications, and coach encouragement (19,21,22,28). However, it is unclear how manipulating prescriptive variables such as field size and player number affects the training response in rugby-specific SSGs. A better understanding of these variables is necessary to optimize the training stimulus for rugby-specific SSGs.
Although rugby-specific SSGs have been recommended as a method for improving aerobic capabilities in rugby union (16) and rugby league players (13), there have been relatively few studies to examine the influence of prescriptive variables on the training stimulus (12,14). Indeed, both these studies have used ‘offside’ (i.e., the ball may be passed in any direction) SSG formats. In studies of 2 groups of elite junior rugby league players (12–13 and 15–16 years), it was reported that although the HR response was sufficient to provide an aerobic training stimulus, it did not vary between ‘offside’ SSGs conducted on various sized fields (12). However, decreasing the number of players in the SSGs elicited a higher HR response, albeit only in the older age group. Other researchers have investigated the effects of rule modification on time-motion and technical skill demands of rugby-based SSGs (14). It was found that changing the rules of the SSG to permit ‘offside’ play increased both the time-motion and technical demands of the SSG (14). Further research is required to examine the effect of player number and field size on the time-motion, physiological, and perceptual responses to traditional ‘onside’ rugby-specific SSGs.
Previous research in soccer-specific SSGs has demonstrated that physiological and perceptual responses can be altered by varying the number of players and the field size. Specifically, reducing the number of players has been shown to increase HR, blood lactate concentration, and RPE in soccer-specific SSGs (21,27,28). In contrast, modifying the number of players may not result in significant changes in the time-motion demands of SSGs (21). It also has been suggested that the field size can be manipulated to modify the intensity of soccer-specific SSGs. It has been reported that there is a higher HR, blood lactate and RPE response, and increased time-motion demands during soccer-specific SSGs played on large fields in comparison to those on smaller fields (4,28). In contrast, other studies have found that HR response was not significantly greater for SSGs conducted on large-sized fields (25). Although it appears that a greater field size results in increased physiological demands, care must be taken when interpreting these results because of the methodological differences between investigations of SSGs. Furthermore, as there are significant differences in the nature of rugby and soccer-specific SSGs, it may not be appropriate to directly compare the physiological and perceptual responses and time-motion demands between different sport-specific SSG formats.
To our knowledge, no studies have systematically examined the effects of player number and field size on the training response in rugby-specific SSGs. Therefore, to address this problem, this study was designed to examine the influence of player number (4 vs. 4, 6 vs. 6, and 8 vs. 8) and field size (small and large) on physiological and perceptual responses and time-motion demands of rugby-specific SSGs.
Experimental Approach to the Problem
This study examined the impact of modifying field size and player number on physiological, perceptual, and time-motion responses of rugby-specific SSGs (Table 1). All the subjects were part of a single experimental group that completed a series of SSGs of varying player number and field size. The SSGs were conducted under 3 different player number formats (i.e., 4 vs. 4, 6 vs. 6, and 8 vs. 8) and 2 field sizes (i.e., small and large). The small and large field dimensions were 32 × 24 and 64 × 48 m (length by width), respectively. Each game format was completed twice in a randomized order over an 8-week period during the competition phase of the season. Two games were played per week, with each game being separated by at least 48 hours. The SSGs were played at the beginning of the training session after a standardized 15-minute warm-up.
Twenty semiprofessional male rugby union players (age: 21.3 ± 1.2 years, mass: 89 ± 8 kg, stature: 183 ± 5 cm), Yo-Yo Intermittent Recovery Test (level 1) performance: 1,679 ± 302 m) from the same club participated in the study. The project was approved by an Institutional Ethics Committee, and appropriate written informed consent was obtained from each subject.
The SSGs were played under modified touch football rules that are commonly used in rugby codes. The SSG required players to pass the ball backwards to a team mate in an ‘onside’ position. Each team was permitted to have 6 ‘plays’ while in possession of the ball. Defending players were only required to touch their opponent with one hand. After a successful touch, play would restart with a ‘play the ball.’ The defensive line was set at 5 m from the position of each ‘play the ball.’ Each match consisted of two 9-minute halves that were separated by a 2-minute half-time period. The composition of teams, coach encouragement, and referee were standardized for all SSGs. In addition, all SSGs were played at the same time of the day (16:00–17:00), and the players were asked to standardize their food and fluid intake in the 24 hours before each testing day. The study was conducted from June to July, which was in the middle of the competitive rugby union season.
To avoid skill and fitness mismatches in opposing SSG teams, player allocations were based on a composite skill and fitness ranking (21). An independent coach provided an overall subjective skill rating of each player using a 5-point (1 = “below average,” 5 = “outstanding”) Likert scale (21). The players were rated on their game sense, ball skills, and evasiveness based on performance during SSGs conducted during preseason training. Before participating in the study, all the players completed the Yo-Yo Intermittent Recovery Test (Yo-Yo IRT1) to assess each player's aerobic performance (26) and determination of each participant's maximum HR (HRmax). All players were assigned an aerobic fitness rating based on their performance in the Yo-Yo IRT1. The 3 skill ratings and the fitness rating were combined to produce a composite score and overall ranking, which was used to allocate players into balanced SSG teams.
Global RPE was recorded immediately after each SSG using the 6–20 scale (2). Standardized instructions for RPE were provided, and the participants were asked to refer their RPE to the exercise bout just completed rather than their perceived exertion at the time of rating (11).
The HR was recorded at 5-second intervals during the SSGs via short range radio telemetry (Polar RS 800, Oy, Kempele, Finland). The HR monitors were also worn during the Yo-Yo IRT1 to determine each participant's HRmax (26). The HRmax was taken as the highest 5-second sample recorded during this test. Exercise intensity during each SSG was assessed using the HR, expressed as a percentage of maximum HR and classified into 3 previously defined intensity zones: zone 1 (<75% HRmax), zone 2 (75–84% HRmax), and zone 3 (>85% HRmax) (8). After each SSG, the HR data were downloaded using the proprietary software (Polar Protrainer 5, Oy, Kempele, Finland). Each individual file was ‘cleaned’ so that only data obtained during each 9-minute half was included for analyses. Both the actual time and percentage of time spent in each zone for each player during all SSGs was obtained.
Blood lactate concentration was obtained from the capillary blood sample drawn from the fingertip during the 2-minute half-time period and again within 3 minutes after each SSG (LactatePro, ArkRay Inc., Kyoto, Japan). The blood lactate analyzers were regularly calibrated according to manufacturer guidelines.
Player movements during the SSGs were measured using portable global position system (GPS) units (SPI-10, 1 Hz, GPSports, Canberra, ACT, Australia). The SPI-10 unit (70 g: 45 mm × 90 mm × 34 mm) was carried in a harness that positioned the unit between the player's shoulder blades. The units were fitted to the players before the warm-up, which allowed the units to begin collecting data for at least 30 minutes before the start of the SSGs. All the subjects had previously been familiarized with the GPS units during training sessions to ensure that there was no impedance to their performance. The SPI-10 GPS devices have previously been shown to provide valid and reliable measures for total distance and low speed movements. However, these units displayed poor intramodel reliability (coefficient of variation = 32.4%) at higher speeds (i.e., >14.5 km·h−1), which suggests that they may not provide precise information regarding high-speed activities (5).
After each match, the GPS data were downloaded to the TeamAMS proprietary software (V2.1 GPSports). Each file was then ‘cleaned’ so that only samples recorded during the SSGs were included for analysis. The ‘clean’ data were then exported to Microsoft Excel (Microsoft, Redmond, WA, USA) for detailed analysis. Data were categorized into distance covered in 4 locomotor categories; standing and walking (0.0–6.9 km·h−1), jogging (7–14.4 km·h−1), high-speed running (HSR) (14.5–23 km·h−1), and sprinting (>23 km·h−1) (6). The mean speed (meters per minute) and HSR (>14.5 km·h−1) distance were calculated. The peak speed and frequency of high-speed zone (sprints >23 km·h−1) entries during games were also collected.
A main effects 2-way analysis of variance was used to determine the effects of field size (small and large) and player number (4 vs. 4, 6 vs. 6, and 8 vs. 8) on each dependent variable (HR, blood lactate, RPE, and time-motion characteristics). When significant interaction effects were observed, Bonferroni post hoc tests were applied. Pearson's correlations were used to examine the relationships between the relative pitch area per player in the various SSGs and the movement demands and physiological responses to the various SSG designs. Correlation coefficients of 0.1, 0.3, 0.5, 0.7, and 0.9 were described as small, moderate, large, very large, and extremely large, respectively (23). Intraclass correlation coefficients (ICCs) were used to determine the test-retest reproducibility for the movement demands for each SSG design. All statistical analyses were performed using the STATISTICA (version 7.0, StatSoft, Tulsa, OK, USA) software package. Data are presented as the mean (±SD). Statistical significance was set at p ≤ 0.05.
Table 2 shows the time-motion characteristics for various SSG formats. There were significant differences between the 4 vs. 4, 6 vs. 6, and 8 vs. 8 SSG formats in mean speed (114 ± 16, 110 ± 15, and 100 ± 16 m·min−1, respectively; p < 0.05) and HSR distance (273 ± 179, 199 ± 129, and 153 ± 115 m, respectively; p < 0.05). There were significantly more sprints in the 4 vs. 4 format compared with that in the 8 vs. 8 (2.1 ± 2.4 and 1.0 ± 1.2, respectively; p < 0.05). The peak sprint speed did not differ between SSG formats.
The time-motion demands were affected by field size (Table 2). The mean speed (121 ± 10 and 94 ± 9 m·min−1), HSR distance (316 ± 120 and 88 ± 49 m), number of sprints (2.5 ± 2.0 and 0.4 ± 0.7), and peak speed (21.3 ± 2.7 and 25.8 ± 2.7 km·h−1) were all significantly different between large and small field size, respectively (all p < 0.05).
Physiological and Perceptual Responses
The physiological and perceptual responses to the SSG formats are shown in Table 2. There were no significant difference between game formats (4 vs. 4, 6 vs. 6, and 8 vs. 8) for either percent HRmax or time spent >85% HRmax. Significant differences were observed in the RPE between all game formats (17.4 ± 1.4, 15.0 ± 1.8, and 12.7 ± 2.5; p < 0.05). There was also a significant difference in blood lactate concentration between 4 vs. 4 and 8 vs. 8 SSG formats (8.9 ± 3.2 and 6.0 ± 3.7 mmol·L−1).
There was no difference in either percent HRmax or time spent >85% HRmax between small and large-sized fields (86.7 ± 6.0, 89.4 ± 4.8 and 64.4 ± 36.4, 79.9 ± 23.8%, respectively; all p < 0.05). Table 2 shows that significant differences were evident between small- and large-sized fields for blood lactate concentrations (5.7 ± 3.3 and 8.2 ± 3.4 mmol·L−1) and RPE (13.7 ± 2.7 and 15.8 ± 2.2, all p < 0.05).
The correlations between the pitch area available for each player in the various SSGs and the player movement, perceptual and blood lactate responses were large to very large (total distance: r = 0.88, HSR distance: r = 0.87, mean speed: r = 0.87, number of sprints: r = 0.67, peak speed: r = 0.63, session-RPE = 0.59, and blood lactate: r = 0.48, all p < 0.05). In contrast, the correlation between pitch area per player and HR responses were trivial (<75% HRmax = 0.03, 75–85% HRmax = 0.09, >85% HRmax = 0.08, %HRmax = 0.08, all p > 0.05). Finally, there was a high level of reproducibility for the movement demands of each SSG when repeated (total distance: ICC (±90% confidence intervals): 0.90 (0.82–0.95), mean speed 0.87 (0.76–0.93), HSR 0.90 (0.82–0.95).
This study examined the physiological and perceptual responses and time-motion demands associated with 3 specific rugby-specific SSG formats (4 vs. 4, 6 vs. 6, and 8 vs. 8) when conducted on different sized fields (small and large). The main finding was that prescriptive variables such as player number and field size can be manipulated to alter the training stimulus in rugby-specific SSGs. In particular, there was an overall increase in physiological, perceptual, and movement demands as the number of players in the SSG format decreased and as the field size increased.
The RPE has been shown to be a valid indicator of global exercise intensity during prolonged high-intensity intermittent exercise (7). In this study, the highest RPE was observed for games involving the least number of players (4 vs. 4). These results support previous research that reported increased perceptual load as the number of players involved in soccer-specific SSGs decreased (21,27,28). The increased perception of effort is likely because of more frequent interactions with the ball and opponents in SSG formats involving fewer players. An increased RPE was also observed for SSGs conducted on the large field when compared with that on the small field. This finding is in agreement with those of previous soccer SSG research (28) and is likely related to increased time-motion demands associated with SSGs conducted on larger playing fields. Collectively, the present results and previous research suggest that decreasing the number of players and increasing the field size increases the player's perception of effort during rugby-specific SSGs.
Manipulating prescriptive variables such as player number and field size did not alter the HR response to SSG formats. This is in contrast to most previous studies of soccer that have shown that SSGs with fewer players (4,21,28) and larger field sizes (4) produce higher HR responses. However, the present results are supported in part by Kelly et al. (25), who also failed to find a difference in the HR response between soccer-specific SSGs conducted on differing field sizes. The present results may be attributable to a large variation in the individual response to the rugby-specific SSGs. For example, ball players responsible for distributing the ball to the outside backs in attack may have higher HR responses because they are typically more involved in the play than players who are positioned on the periphery of the attacking line. The variability of the HR response could arise from the different positional demands during SSGs. The percentage time spent in the high-intensity HR zone (>85%) for all SSG formats were equal to, or higher than, those reported for semielite under-19 rugby union players (41–52 and 72% for backs and forwards, respectively) (9). It has been suggested that high HR responses are important factors for training aerobic fitness in team sport athletes (17). The present results suggest that rugby-specific SSGs of varying player numbers and field sizes are appropriate for improving aerobic fitness in rugby players. However, because of the high variability of the HR response observed in the current investigation, individual responses should be monitored to ensure that all players receive the intended training stimulus.
This study is the first to report on the blood lactate responses to various rugby SSG designs. In agreement with the findings of previous studies on soccer (21,28), the blood lactate concentrations in this study were greater in SSG formats that involved fewer players. Additionally, the blood lactate concentration was elevated after SSGs conducted on large-sized fields in comparison with those on small sized fields. Similar blood lactate responses to an increased field size have also been reported in soccer-specific SSGs (28). The elevated blood lactate concentrations observed in the smaller player number and larger field size SSG formats may be because of increased HSR and sprint demands observed in SSGs with fewer players and greater field size. It has been well established that brief sprint efforts promote anaerobic glycolysis, which results in elevated blood lactate concentrations (15). Furthermore, the elevated blood lactate concentration may also be related to the higher technical load associated with a lower player density.
The time-motion demands of the rugby-specific SSGs differed between game format and field size. Specifically, the mean speed, HSR distance, and number of sprints but not peak speed increased as the number of players decreased. These results are partly in contrast to those of other studies of soccer-specific SSGs, in which no difference in exercise intensity, HSR distance, or sprint number was observed between different SSG formats (21). The increased movement demands observed in this study is likely because of greater relative field space available per player in the smaller SSG formats. In contrast, the relative field size per player was kept constant in the previous similar soccer study Hill-Haas et al (21). This, combined with the obvious technical-tactical differences between soccer and rugby, may explain the differences in the time-motion characteristics between these studies. In addition, an increase in exercise intensity, HSR distance, sprint number, and peak sprint speed was observed as field size increased. Previous studies examining the influence of field size in soccer-specific SSGs have reported similar results (4). Indeed, the greater available field space per player is likely to be responsible for the increased movement demands on the large field in comparison to the small field. This is supported by the large to very large correlations between the relative pitch area for each player for the different SSGs and the various movement responses in this study. Additionally, the exercise intensities reported in this study, particularly those SSGs involving fewer players and larger fields, are higher than those reported for elite junior (9) and senior professional rugby players (1,29). Collectively, these results suggest that well-designed SSGs can provide a training stimulus in excess of competition match demands and therefore may be a practical method for training aerobic capabilities for rugby players.
The time-motion results in this study should be interpreted with caution, because the 1-Hz GPS devices have previously been shown to have a poor reliability for assessing high-speed movements (>14.5 km·h−1) (5). Nevertheless, we have confidence in the present results because there was strong reproducibility in the HSR between the various SSGs (ICC 0.90 [0.82–0.95]). However, we recommend that future studies use GPS devices with higher sampling rates to examine high-speed movement demands of rugby-specific SSGs.
It has been suggested that SSGs are useful to concurrently improve physical and technical qualities. Although the current investigation has systemically examined the time-motion, physiological, and perceptual responses to different SSG formats, the technical demands were not examined in this study. Future research is required to understand the influence of prescriptive variables on the technical requirements of rugby-specific SSGs.
In conclusion, the main finding of this study was that decreasing player number resulted in greater time-motion demands and increased physiological and perceptual responses. Specifically, the time-motion demands, RPE, and blood lactate were higher in smaller SSG formats (4 vs. 4) compared with those in larger SSGs (8 vs. 8), whereas HR responses were unchanged. Furthermore, field size also altered the time-motion, physiological and perceptual responses, with SSGs conducted on the large size field eliciting greater movement demands, higher blood lactate concentrations, and RPE but not HR responses. In addition, the present results suggest that rugby-specific SSGs are an effective method for training aerobic capabilities of rugby players. Collectively, the results of this study provide new information about influence of prescriptive variables such as player number and field size on the training stimulus provided by rugby-specific SSGs. This information may be useful when designing SSGs for rugby union players.
This study systematically examined the influence of player number and field size on the physiological, perceptual, and time-motion demands of rugby-specific SSGs. The results of the study indicate that prescriptive variables such as player number and field size can alter the training stimulus. The SSG formats with fewer players and large-sized fields elicited the highest time-motion demands (meters per minute and HSR distance). Similarly, the RPE and blood lactate concentrations were higher during SSGs with fewer players and large-sized fields. The HR response was similar between all SSG formats and appears to be sufficient to develop aerobic capabilities in team sport athletes. However, because of the high individual variability in the HR response, it is suggested that real-time individual HR monitoring may be important to ensure each player achieves the desired training dose. These rugby-specific SSGs required relatively few sprint efforts, especially when involving greater player numbers and when conducted on small fields. Therefore, generic sprint training methods should be used in conjunction with SSGs to enhance these qualities separately. Coaches should take care when designing SSGs for training because both the physiological and perceptual responses and time-motion demands can be altered by factors such as player number and field size.
No external financial support was received for this study.
1. Austin D, Gabbett T, Jenkins D. The physical demands of Super 14 rugby union. J Sci Med Sport 14: 259–263, 2011.
2. Borg G. Perceived exertion as an indicator of somatic stress. Scand J Rehabil Med 2: 92–98, 1970.
3. Buchheit M, Laursen PB, Kuhnle J, Ruch D, Renaud C, Ahmaidi S. Game-based training in young elite handball players. Int J Sports Med 30: 251–258, 2009.
4. Casamichana D, Castellano J. Time-motion, heart rate, perceptual and motor behaviour demands in small-sides soccer games: effects of pitch size. J Sports Sci 28: 1615–1623, 2010.
5. Coutts AJ, Duffield R. Validity and reliability of GPS units for measuring movement demands of team sports. J Sci Med Sport 13: 133–135, 2010.
6. Coutts AJ, Quinn J, Hocking J, Castagna C, Rampinini E. Match running performance in elite Australian Rules Football. J Sci Med Sport 13: 543–548, 2010.
7. Coutts AJ, Rampinini E, Marcora SM, Castagna C, Impellizzeri FM. Heart rate and blood lactate correlates of perceived exertion during small-sided soccer games. J Sci Med Sport 12: 79–84, 2009.
8. Coutts AJ, Reaburn PRJ, Abt GA. Heart rate, blood lactate concentrations, and estimated energy expenditure in a semi-professional rugby league team during match play: A case study. J Sports Sci 21: 97–103, 2003.
9. Deutsch MU, Maw GD, Jenkins DJ, Reaburn PRJ. Heart rate, blood lactate and kinematic data of elite colts (under-19) rugby union players during competition. J Sports Sci 16: 561–570, 1998.
10. Duthie G, Pyne D, Hooper S. Applied physiology and game analysis of rugby union. Sports Med 33: 973–991, 2003.
11. Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P, Dodge C. A new approach to monitoring exercise training. J Strength Cond Res 15: 109–115, 2001.
12. Foster CD, Twist C, Lamb KL, Nicholas CW. Heart rate responses to small-sided games among elite junior rugby league players. J Strength Cond Res 24: 906–911, 2010.
13. Gabbett TJ. Skill-based conditioning games as an alternative to traditional conditioning for rugby league players. J Strength Cond Res 20: 309–315, 2006.
14. Gabbett TJ, Jenkins DG, Abernethy B. Physiological and skill demands of ‘on-side’ and ‘off-side’ games. J Strength Cond Res 24: 2979–2983, 2010.
15. Gaitanos GC, Williams C, Boobis LH, Brooks S. Human muscle metabolism during intermittent maximal exercise. J Appl Physiol 75: 712–719, 1993.
16. Gamble P. A skill-based conditioning games approach to metabolic conditioning for elite rugby football players. J Strength Cond Res 18: 491–497, 2004.
17. Helgerud J, Christian Engen L, Wisløff U, Hoff J. Aerobic endurance training improves soccer performance. Med Sci Sports Exerc 33: 1925–1931, 2001.
18. Hill-Haas SV, Coutts AJ, Dawson B, Impellizzeri FM. Small-sided games training in football: A systematic review. Sports Med 41: 199–220, 2011.
19. Hill-Haas SV, Coutts AJ, Dawson BT, Rowsell GJ. Time-motion characteristics and physiological responses of small-sided games in elite youth players: The influence of player number and rule changes. J Strength Cond Res 24: 2149–2156, 2010.
20. Hill-Haas SV, Coutts AJ, Rowsell GJ, Dawson BT. Generic versus small-sided game training in soccer. Int J Sports Med 30: 636–642, 2009.
21. Hill-Haas SV, Dawson BT, Coutts AJ, Rowsell GJ. Physiological responses and time-motion characteristics of various small-sided soccer games in youth players. J Sports Sci 27: 1–8, 2009.
22. Hill-Haas SV, Rowsell GJ, Dawson BT, Coutts AJ. Acute physiological responses and time-motion characteristics of two small-sided training regimes in youth soccer players. J Strength Cond Res 23: 111–115, 2009.
23. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 41: 3–13, 2009.
24. Impellizzeri FM, Marcora SM, Castagna C, Reilly T, Sassi A, Iaia FM, Rampinini E. Physiological and performance effects of generic versus specific aerobic training in soccer players. Int J Sports Med 27: 483–492, 2006.
25. Kelly DP, Drust B. The effect of pitch dimensions on heart rate responses and technical demands of small-sided soccer games in elite players. J Sci Med Sport 12: 475–479, 2009.
26. Krustrup P, Mohr M, Amstrup T, Rysgaard T, Johansen J, Steensberg A, Pedersen PK, Bangsbo J. The Yo-Yo intermittent recovery test: Physiological response, reliability, and validity. Med Sci Sports Exerc 35: 697–705, 2003.
27. Little T, Williams AG. Measures of exercise intensity during soccer training drills with professional soccer players. J Strength Cond Res 21: 367–371, 2007.
28. Rampinini E, Impellizzeri FM, Castagna C, Abt G, Chamari K, Sassi A, Marcora SM. Factors influencing physiological responses to small-sided soccer games. J Sports Sci 25: 659–666, 2007.
29. Roberts SP, Trewartha G, Higgitt RJ, El-Abd J, Stokes KA. The physical demands of elite English rugby union. J Sports Sci 26: 825–833, 2008.