Skip to main content

Table 1 Summary of senior rugby GPS studies

From: The Use of Global Positioning and Accelerometer Systems in Age-Grade and Senior Rugby Union: A Systematic Review

Study Participants Device details Method Results
Beard et al. [63] 188 rugby union players from the Pro12 and an international team. Catapult Optimeye S5 Data were collected from GPS tracking devices (10 Hz) over the course of one Pro12 season and one international season. Total distance (m), relative distance (m.min−1), high-speed running (m.min−1) and max velocity was recorded for 6 positional groups and separated into club vs. international-level players. Significant differences were found for repeated high-intensity locomotor efforts between club and international players in all position groups. Significantly greater total distance and relative distance was reported in international compared to club players for the outside back position.
Cahill [28] 120 professional rugby union players from the English Premiership. GPSports SPI Pro Data were collected from GPS tracking devices (5 Hz) during the 2010/11 competition. 8 professional clubs took part in the study. Total distance (m), relative distance (m.min−1), maximum speed (km h−1), average speed (km h−1) and total distance at different percentages of max velocity were recorded. Results showed that the matches were played at a relatively slow pace with little distance covered in sprinting by both backs (50 ± 76 m) or forwards (27 ± 64 m). Backs covered greater absolute and relative distances compared to forwards (p < 0.05). Scrum halves covered the most distance during matches (7098 ± 778 m) and front row forwards the least (5158 ± 200 m).
Campbell et al. [52] 32 club rugby union players. GPSports SPI HPU Data were collected from GPS tracking devices (15 Hz) during a 19-week in competition period (training and matches). Greater total distance (m), low-intensity activity, maximal speed and metres per minute were recorded during matches compared to training in all positions (p < 0.02).
Chambers et al. [64] 30 elite forwards. Catapult Optimeye S5 Data were collected from GPS tracking devices (10 Hz) during both matches and training sessions. This allowed for the development of an algorithm to detect scrum events. Across all positions the algorithm showed good sensitivity and specificity for training and match play. The algorithm displayed greater accuracy for match play than training (93.6 vs 87.6 %).
Chambers et al. [65] 12 elite rugby union players. Catapult Optimeye S5 Data were collected from GPS tracking devices (10 Hz) during match play. Ruck and tackle data were synchronised with video footage of the games. The authors then developed an algorithm to detect tackles and rucks. The algorithm was able to detect rucks and tackle for all positions. However, it does not provide the impact forces of these events.
Coughlan et al. [23] 2 players (1 back and 1 forward) from an international team. GPSports Team AMS Data were collected from GPS tracking devices (5 Hz) during 1 international rugby match for 1 back and 1 forward. Players completed an average of 6715 m and spent the majority of the match standing or walking interspersed with medium and high-intensity running activities. The back performed a higher number of sprints and reached a greater maximal speed. Body load data showed high levels of G force are sustained during tackling and scrummaging.
Cousins et al. [66] 89 professional rugby union players from the top two leagues in England. STATSport Apex Data were collected from two GPS tracking devices (5 and 10 Hz) over 2 seasons. Total distance (m) and high-speed running distance (m) were recorded. Distance covered had a significant influence on time-loss incidence (p < 0.001). For every 100 m extra distance covered there was a 1% increase in time-loss incidence. High-speed running distance also had a significant influence on time-loss incidence. For each 100 m increase in high-speed running distance there was a 21% increase in time-loss incidence.
Cunningham et al. [3] 119 elite professional players from three different international performance squads. STATSport Viper Pod Data were collected from GPS tracking devices (10 Hz) over a 3-year period (Jan 2014–March 2017).
Two types of sampling-epoch were utilised. Rolling (ROLL) and fixed (FIXED) length epochs.
An example of the use of the ROLL method is 60 s rolling-epoch algorithm is calculated using the current, and 599 preceding samples. For the fixed time method epochs were located at samples 1–600, 601–1200, 1201–1800 and so on.
Using both methods as the epoch length increased values for intensity of running decreased. Movement demands were underestimated consistently by the FIXED method.
Delaney et al. [6] 67 players from two international rugby union teams. GPSports SPI HPU Data were collected from GPS tracking devices (15 Hz) across 33 international matches. A moving average was used to identify the peak relative distance, average acceleration/deceleration (AveAcc: m s−2) and average metabolic power (Pmet) for a range of durations (1–10 min). Peak running intensity increased as the length of the length of the moving average increased.
Likely small to moderate increases in relative distance and AvcAcc for outside backs, half backs and loose forwards compared to the tight 5 group across all moving average durations (ES = 0.27–1.00).
Metabolic power demands were at least greater for outside backs and half backs when compared to the tight 5 (0.86–0.99). Half-backs demonstrated greatest relative distance and Pmet outputs but were similar to outside backs and loose forwards in AveAcc demands.
Dubois et al. [4] 14 professional rugby union players from the French Top 14. GPSports Team AMS Data were collected from GPS tracking device (5 Hz) from 5 European Cup games. Total distances, high-speed running distance, peak speed, number of sprints, number of accelerations and number of decelerations were reported. Back covered greater distances at high-speed than forwards (p < 0.01). Forwards covered greater distances in the moderate speed zone (p < 0.05) than backs. No sig. differences in high-metabolic power distance were found between backs and forwards.
Dubois et al. [43] 8 professional rugby union players (all backs) from the D2 Championship in France. Catapult, Minimaxx S4 Data were collected from GPS tracking devices (10 Hz) during training session over the course of the season. Total distance (m) and distance at moderate to high-speed (> 13 km h−1) were recorded. Total distance covered per week was 19316 ± 2923 m and distance performed at moderate to high-speed was 3996 ± 701 m.
Grainger et al. [53] 38 professional rugby union players from the English Premiership. STATSport Viper Pod Data were collected from GPS tracking devices (10 Hz) over a 9-month in-season period. Both locomotor and collision data were reported. No difference in the number of impacts > 9.01 G were observed between forwards and backs (229 ± 160 vs 226 ± 151). However, forwards had a greater absolute (p = 0.03) and relative (p = 0.003) number of impacts over 13 G. Full backs experienced the greatest frequency of absolute impacts > 9.01 G. and hookers experienced the greatest frequency of relative impacts > 9.01 G.
Jones et al. [30] 36 professional rugby union players. Catapult, Minimaxx S4 Data were collected from GPS tracking devices (10 Hz) following 4 European Cup group matches during the 2012–2013 season. Both locomotor and collision data were reported. Backs covered significantly greater total distance (m) compared to forwards (5959 ± 1013 vs 4906 ± 902, p < 0.01), greater distance per minute (67.8 ± 8.2 vs 60.4 ± 7.8, p <  0.01), performed a greater number of sprints (18 ± 6 vs 7 ± 6, p <  0.001), covered more distance (m) at high-speed (509 ± 150 vs 231 ± 167, p < 0.001) and covered more sprint distance (m) than forwards (333 ± 122 vs 121 ± 112, p < 0.001). However, forwards had a greater total number of contacts compared to backs (31 ± 14 vs 16 ± 7, p <  0.001).
Jones et al. [31] 33 professional rugby union players from a Pro 12 team. Catapult, Minimaxx S4 Data were collected from GPS tracking devices (10 Hz) from 6 European Cup games and 7 Pro 12 games. Distances, velocities, accelerations, exertion index, player load, contacts, sprinting and repeated high-intensity efforts were reported. Inside and outside back have the greatest high-speed running demands. Repeated high-intensity efforts and contact demands are greater in the loose forwards.
Lindsay et al. [32] 37 professional rugby union players from a Super Rugby squad. Catapult, Minimaxx S4 Data were collected from GPS tracking devices (10 Hz) over 5 home games. Total distance (m) and distance covered in the following speed bands > 7 km h−1, 16 km h−1, > 20 km h−1 and > 25 km h−1was recorded. Backs covered more metres per minute than forwards. Inside and outside backs covered a similar distance that was more than all the forward positions (p < 0.05). Backs covered significantly more distance per minute than forwards above 16, 20, 25 km h−1 (p < 0.01). Loose forwards covered more distance than locks and front rowers above 16, 20, 25 km h−1 (p < 0.01). Inside backs and outside backs covered more distance per min than all forward positions (p < 0.001).
MacLeod et al. [55] 37 professional rugby union players from the Pro12 competition. STATSport Viper Pod Data were collected from GPS tracking devices (10 Hz) from same team over 11 competitive matches. Collisions were automatically recorded using the GPS units. Collision loads were significantly greater during dominant compared with neutral and passive collisions, tackles and carries (p < 0.001). Overall forwards reported a greater number and frequency of collisions but lower loads per collision and velocities at the point of collision compared to backs.
McLaren et al. [18] 28 professional rugby union players from the English Championship. Catapult, Minimaxx S4 Data were collected from GPS tracking devices (10 Hz) from same team over 15 competitive matches during the 2012/13 season. Total distance (m), low speed running (0–14.9 km h−1), high-speed running (15.0–19.9 km h−1), and very-high-speed running (20.0–36.0 km h−1), PlayerLoad and PlayerLoad slow were reported. Large between match variation (within-player) for high-speed and very-high-speed running and repeated high-intensity efforts for backs and forwards. PlayerLoad and PlayerLoad slow were reported to be more stable.
Owen et al. [33] 33 professional rugby union players from a Super Rugby squad. GPSports SPI HPU Data were collected from GPS tracking devices (15 Hz) by player position group over the first half of match play from 14 Super Rugby matches. Accelerations and decelerations, impacts, and aggregated body demands were reported. Forwards had more high-intensity impacts (d = 0.44) and greater aggregated body demands (d = 0.26), while backs had more moderate (d = 0.55) and heavy accelerations (d = 0.76) and more moderate (d = 0.23) and heavy decelerations (d = 0.54).
Pollard et al. [58] 22 players from an international rugby team. STATSport Viper Pod Data were collected from GPS tracking devices (10 Hz). An Opta sportscode timeline was used in conjunction with GPS to split data into ball in play (BiP) times. Metres per min, high-metabolic load per min (HML), accelerations per min (Acc) high-speed running per min and collisions per min. Coll were expressed relative to BiP periods over the whole match. Whole match metrics were sig lower than all BiP metrics (p < 0.001). Mean and max BiP HML (p < 0.01) and HSR (p < 0.05) were sig. higher for backs. Collisions were sig. higher for forwards (p < 0.01). In plays lasting 61 s or longer, max BiP m.min−1 were higher for backs. Max BiP m.min−1, HML, HSR and Coll were all time dependent. Movement metrics and collisions differ as length of play continues.
Reardon et al. [34] 36 professional rugby union players from a Pro 12 team. Catapult Optimeye S5 Data were collected from GPS tracking devices (10 Hz). Total distance and total distance relative to playing time were calculated. Maximum velocity (Vmax) was calculated from all match and training data during the season to allow for the calculation of individual speed thresholds. When comparing absolute to individualised HSR thresholds, there was a significant underestimation for forwards HSR distance (p <  0.001), HSR% (p < 0.001) and HSR efforts (p < 0.001). In contrast there was a sig. overestimation of the HSR metrics for backs with the use of an absolute threshold (p < 0.001 for all metrics).
Reardon et al. [47] 39 professional rugby union players from a Pro12 team. Catapult Optimeye S5 Data were collected from GPS tracking devices (10 Hz) over 6 European Rugby Championship games and 11 games in the Pro 12. Worst-case scenario (WCS) periods are played at a far higher pace than previously reported average game demands. Within WCS periods backs covered greater total distance than forwards (318 m vs 289 m), carried out more high-speed running (11.1 m.min−1 vs 5.5 m.min−1 and achieved the highest MaxVel values (6.84 m sec−1). Tight five and back row forwards had sig. more collisions than inside and outside backs (0.73 and 0.89 collisions m.min−1 vs 0.28 and 0.41 collisions m.min−1 respectively.
Reardon et al. [46] 36 professional rugby union players from a Guinness Pro 12 team. Catapult Optimeye S5 Data were collected from GPS tracking devices (10 Hz) to monitor collision counts during match play. Collision thresholds were set between 2 and 5.5 g in 8 increments of 0.5 g. The upper threshold for all bands was 15 g. Collision may be over or underestimated via GPS compared to expert video analysis. The use of 0.5 g increments of force did not provide a reliable tool for coding collisions.
Reid et al. [29] 8 professional rugby union players from a Magners League team. GPSports SPI Pro Data were collected from GPS tracking devices (5 Hz) during one league. Total distance (m), relative distance (m.min−1), time and distance in different speed zones and frequency of entry into each speed zone was recorded. The backs covered a greater total distance than forwards, with the scrum half completing the most (7183.7 m) and the loose head prop the least (6206.2 m). The winger had the highest peak speed (31.1 km h−1) and most entries into the maximal speed zone (17). Backs spent less time and covered less distance walking than forwards.
Roe et al. [48] 9 professional rugby union players. Catapult Optimeye S5 Players completed 3 maximal 40 m sprints with their maximum velocity assessed via timing gates, radar and a GPS tracking device (10 Hz). The results of this study indicate that when compared with radar GPS was able to provide a valid measure of 40 m maximum velocity.
Suarez-Arrones et al. [26] 9 international rugby union players. GPSports SPI Elite Data were collected from GPS tracking devices (1 Hz) in forwards and backs during 3 competitive games. The frequency and duration of locomotor efforts were evaluated using distance covered in 6 zones. Backs covered significantly greater total distance than forwards (6162 ± 313 m vs 5853 ± 205 m, p < 0.001). The forwards average speed during the games was 4.3 km h−1 and the backs 4.7 km·h-1.
Swaby et al. [39] 14 professional rugby union players from an English Premiership team. STATSport Viper Pod Data were collected from GPS tracking devices (10 Hz) during the first 6 matches of a season. Total distance (m) was the metric of interest. No significant differences were observed on total distance between games. Greater distances were covered by backs during a game compared to forwards (6544 ± 573 m vs 4872 ± 857 m, p = 0.001). Maximum aerobic speed (MAS) performance showed a strong relationship with distance covered during match play (r = 0.746, p < 0.001).
Tee et al. [40] 53 professional rugby union players from a South African rugby team. GPSports SPI Pro Data were collected from GPS tracking devices (5 Hz) over 96 training sessions and 24 matches. GPS data were used to compare traditional rugby training activities (endurance, high-intensity interval, game-based and skills training) compared to match play. Movement patterns were measured as relative distance, distance walking, jogging, striding and sprinting and sprint and acceleration frequency High-intensity interval training was the most similar to match play. Game based training failed to meet match intensity in all positions (ES = medium to large).
Tee et al [68] 19 professional rugby union players from a South African rugby team. GPSports SPI Pro Data were collected from GPS tracking devices (5 Hz) over 24 matches over the 2013 season. Movement patterns were measured as relative distance, distance walking, jogging, striding and sprinting and sprint and acceleration frequency. An inbuilt triaxial accelerometer (sampling at 100 Hz) measured total impacts > 5G and > 8G. No difference between forwards and backs in relative distance covered (m.min−1). Backs covered more distance than forwards in high-intensity (striding and sprinting) speed zones. There were no differences in impact variables between forwards and backs.
Tee et al. [50] 19 professional rugby union players from a South African rugby team. GPSports SPI Pro Data were collected from GPS tracking devices (5 Hz) over a first-class professional season. Total relative distance (m.min−1), maximum speed, sprint frequency and acceleration frequency were reported. Total relative distance (m.min−1) was decreased in the 2nd half for both forwards and backs (ES = very likely large). A larger reduction in high-intensity running distance in the 2nd half was observed in forwards.
Tee et al. [67] 19 professional rugby union players from a South African rugby team. GPSports SPI Pro Data were collected from GPS tracking devices (10 Hz) from 23 matches over the 2013 rugby season to assess pacing characteristics of whole or part-game players. For forwards finishers who entered the game had significantly higher high-speed running distance (m) and acceleration frequency compared to whole game players. In the backs players who started but were later substituted displayed greater high-speed running distances compared to while game players (not statistically significant). Forwards were reported to show “slow positive” pacing strategies while backs had a “flat: pacing” strategy. Forward were reported to have greater decrements in performance as the match goes on.
Tierney et al. [51] 43 professional rugby union players from a Pro 12 team. Catapult Optimeye S5 Data were collected from GPS tracking devices (10 Hz) over 11 European Rugby Championship and 11 Pro 12 games. Running intensity was calculated for total distance, running distance, high-speed running and very-high-speed running. The study also investigated attacking entries into the oppositions 22. Forwards achieved greater high-speed running in successful (3.6 m.min−1) compared to unsuccessful (1.8 m.min−1) attacking 22 entries.
Vaz et al. [27] 40 rugby union players (20 experienced and 20 novice). GPSports SPI Pro Data were collected from GPS tracking devices (5 Hz) during eight 6 vs 6 matches over a 4-week period. Locomotor characteristics and impacts were recorded during these sessions. Results showed no significant differences between experience and novice players.
Vaz et al. [41] 14 professional rugby union players. GPSports SPI Pro Data were collected from GPS tracking devices (5 Hz) during small-sided games during an in-season competition period. Four sessions were assessed during this study (1 vs 1, 2 vs 1, small-sided match 7 vs 7 and a match 7 vs 7). Speed zones, impacts, and relative distance (m.min−1) were recorded. Different small-sided game set-ups resulted in different levels of physical performance.
Weaving et al. [62] 21 professional rugby union players. Catapult, Minimaxx S4 Data were collected from GPS tracking devices (10 Hz) during training sessions over an entire season. Total distance (m), high-speed distance and PlayerLoad were calculated. Mean total distance during training sessions was 3096 ± 675 m, high-speed distance was 127 ± 202 m and PlayerLoad was 292 ± 87 AU. For an individual total distance and PlayerLoad responded similarly to session RPE across training sessions. However, high-speed running provides unique information on the load.