Skip to main content

Quantifying Collision Frequency and Intensity in Rugby Union and Rugby Sevens: A Systematic Review

Abstract

Background

Collisions in rugby union and sevens have a high injury incidence and burden, and are also associated with player and team performance. Understanding the frequency and intensity of these collisions is therefore important for coaches and practitioners to adequately prepare players for competition. The aim of this review is to synthesise the current literature to provide a summary of the collision frequencies and intensities for rugby union and rugby sevens based on video-based analysis and microtechnology.

Methods

A systematic search using key words was done on four different databases from 1 January 1990 to 1 September 2021 (PubMed, Scopus, SPORTDiscus and Web of Science).

Results

Seventy-three studies were included in the final review, with fifty-eight studies focusing on rugby union, while fifteen studies explored rugby sevens. Of the included studies, four focused on training—three in rugby union and one in sevens, two focused on both training and match-play in rugby union and one in rugby sevens, while the remaining sixty-six studies explored collisions from match-play. The studies included, provincial, national, international, professional, experienced, novice and collegiate players. Most of the studies used video-based analysis (n = 37) to quantify collisions. In rugby union, on average a total of 22.0 (19.0–25.0) scrums, 116.2 (62.7–169.7) rucks, and 156.1 (121.2–191.0) tackles occur per match. In sevens, on average 1.8 (1.7–2.0) scrums, 4.8 (0–11.8) rucks and 14.1 (0–32.8) tackles occur per match.

Conclusions

This review showed more studies quantified collisions in matches compared to training. To ensure athletes are adequately prepared for match collision loads, training should be prescribed to meet the match demands. Per minute, rugby sevens players perform more tackles and ball carries into contact than rugby union players and forwards experienced more impacts and tackles than backs. Forwards also perform more very heavy impacts and severe impacts than backs in rugby union. To improve the relationship between matches and training, integrating both video-based analysis and microtechnology is recommended. The frequency and intensity of collisions in training and matches may lead to adaptations for a “collision-fit” player and lend itself to general training principles such as periodisation for optimum collision adaptation.

Trial Registration PROSPERO registration number: CRD42020191112.

Key Points

  • In this systematic review of collision frequency and intensity in rugby union and rugby sevens, only four studies quantified collision frequencies and/or intensities in training, three focused on both training and match-play, while 66 studies quantified frequencies and/or intensities of collisions in matches. Further investigation is needed to improve and understand the relationship between training and matches.

  • Per minute, rugby sevens players perform more tackles and ball carries into contact than rugby union players and forwards experienced more impacts and tackles than backs. Forwards also perform more very heavy impacts and severe impacts than backs in rugby union.

  • Integrating video-based analysis and microtechnology is recommended, and the metrics and grouping variables between training and matches should be consistent.

  • The frequency and intensity of collisions in training and matches may lead to adaptations for a “collision-fit” player and lend itself to general training principles such as periodisation for optimum collision adaptation.

Background

Rugby union and rugby sevens (henceforth called sevens) are invasion team sports that are characterised by frequent high speed running and physical collisions [1, 2]. Although the two rugby codes differ in match duration (sevens = 14 min; rugby union = 80 min) and player numbers (sevens = 7 players; rugby union = 15 players) [36], the type of collisions are similar (i.e., tackles, scrums, rucks and mauls) [6]. Winning these collisions is associated with overall team success and player performance [79]. For example, Ortega et al. (2009) identified that winning teams complete more tackles than losing teams [7]. These collisions are also physically and technically demanding for players with an associated high injury incidence and burden (injury incidence rate X mean severity) [1013]. For instance, in senior professional male rugby union players, 29.0 injuries per 1000 player hours occur when being tackled, 19.0 injuries per 1000 player hours occur when tackling and 17.0 injuries per 1000 player hours occur in the ruck/maul [14]. In sevens, 40.4 injuries per 1000 player hours occur when tackling, with 1.2 injuries per 1000 player hours occurring in the mauls and scrums [15].

Given the high injury incidence and burden, and the positive performance outcomes associated with winning collisions in rugby union and sevens, it is important for coaches and practitioners to adequately prepare players for competition. To do this, they need to know the frequency and intensity of these collisions in both training and matches [16]. In matches and training, the frequency and intensity of collisions have been quantified primarily using two methods: video-based analysis and microtechnology. Quantifying the frequency and intensity of collisions using video-based analysis requires the systematic observation and interpretation of video from matches and/or training [17, 18]. Analysing collisions can occur while the matches or training session(s) are underway, although most detailed analyses occur post-match [17]. Previously, video-based analysis was the main method used to quantify collisions in both rugby cohorts [17]. Quantifying collisions in this manner however, is based on human observation, and as such, it is labour intensive and requires reliability checking to reduce bias and subjectivity [16]. For these reasons, a shift to automated methods of collecting collision data through the use of microtechnology has occurred.

In sport, microtechnology typically incorporates global positioning systems (GPS) and micro-electrical mechanical systems (MEMs) that capture the external physical demands of competition and training [19]. Commercially available microtechnology devices for team sports are designed to be unobstructive, so players can wear them during competition and training. One of the first studies using microtechnology to determine physical demands in rugby union was published in 2009 [20], and since then, research using these devices has grown [19]. Initially, GPS was only used to provide information on distance and speed [21, 22]. Since then, MEMs have been built into GPS devices which now house triaxial accelerometers, gyroscopes and magnetometers [22]. Triaxial accelerometers measure acceleration in three different axes (anterior–posterior, medial–lateral and vertical) [16, 22], and the sum of the acceleration in these three axes provides a vector magnitude (g force). This vector magnitude can be used to quantify the intensity of the collision [19, 22]. Each manufacturer has a different algorithm that is used to quantify collisions [23]. As a consequence, validating collision metrics for these devices has been challenging [23]. Although quantifying collisions using microtechnology may be more time efficient than video-based methods, the validity and reliability of microtechnology in rugby union and sevens requires further investigation [16, 24] due to the ambiguity in the current results [25].

To benefit coaches and practitioners, and aid injury prevention and injury management strategies, a synthesis of the frequency and intensity of collisions in rugby union and sevens to date, both in training and matches, is required. For example, a coach who understands the positional match tackle frequencies and intensities can optimise tackle training sessions to meet those position specific match demands. Since one of the roles of coaches and practitioners is to ensure positive adaptations to training and reduce maladaptation, understanding the frequency and intensity of collisions may also aid optimising recovery between training and matches. Therefore, the aim of this systematic review to synthesise the collision frequencies and intensities for rugby union and rugby sevens based on video-based analysis and microtechnology.

Methods

Search Strategy

The search strategy was based on a similar systematic review in rugby league [16]. The current systematic review was carried out in accordance with the PRISMA guidelines [28]. The search was conducted from 1 January 1990 to 1 September 2021 on four different electronic databases (PubMed, Scopus, SPORTDiscus and Web of Science). The search used the following combined key terms for collisions (‘tackl*’ OR ‘collision’ OR ‘impact*’) AND (‘dose’ OR ‘frequency’ OR ‘intensity’ OR ‘demands’) AND rugby union (‘rugby’ OR ‘rugby union’ OR ‘rugby sevens’). For example, in PubMed the search was (((tackl* OR collision OR impact* OR collisions)) AND (dose OR frequency OR intensity OR demands)) AND (rugby OR rugby union OR rugby sevens). The reference list of the final full-text articles (n = 73) was also examined.

Selection of Studies

After consolidating the studies from the different electronic databases, LP removed the duplicates and screened the titles and abstracts (Fig. 1) for eligibility before retrieving the full text [28]. The review was registered with PROSPERO (registration number: CRD42020191112). The full text articles were further screened for eligibility by LP and MN. Any discrepancies in the screening process were discussed until agreed upon. A third researcher was available if consensus on the inclusion of an article could not be reached; however this was not required. The inclusion criteria were (i) any publication that quantified collisions in terms of frequency or intensity in rugby union and/or sevens (ii) study participants within each study had to be over 18 years of age. When collisions were based on ‘impact metrics’, only impacts > 8 g were included in the data to eliminate possible confusion with running demands (i.e., high intensity accelerations or decelerations) unless stated otherwise [25]. Publications from conferences and annual meetings were excluded. Only peer-reviewed publications were included. Any publication that could not be translated into English was excluded. Authors were contacted for detailed information if necessary. The final full-text articles went through the data extraction process.

Fig. 1
figure 1

Literature selection process for the systematic review

Collisions were broadly defined as any physical contact made with another player (teammate or opposition), which resulted in an alteration to the player’s momentum. This included collisions such as the tackle (tackling and being tackled), scrums, rucks and mauls [26, 27]. For this review the studies did not need to have a definition to be included.

Data Extraction

Data relating to participant characteristics (i.e., number, age, height, weight, level of competition, sex, cohort), context (i.e., match play or training), method used to quantify the collisions (i.e., video or microtechnology), the model and specifics of the device (i.e., GPS device rate, inertial sensors, number of files, software), video-based analysis characteristics (i.e., camera system, number of cameras, location of the devices and software), and collision characteristics were extracted from the final 73 full-text articles. Collision characteristics included type of collision, number of matches or training sessions, year of competition, absolute frequency (number), collisions in relation to playing time (number of collisions per minute) and the intensity of each collision. Collision intensity was commonly classified as very heavy (8–10 g), severe (> 10 g) or another range that was specific to the device based on the nature of the collision [29].

Assessment of Methodological Quality

The quality of the included studies was assessed using the checklist of Downs and Black’s assessment of methodological quality [30]. Questions 5, 8, 9, 13–15, 19, 21–28 were inapplicable due to the nature of the studies. The assessment was done by LP and MN (Additional file 1: Table S1). No studies were eliminated based on the methodological quality.

Data Analysis

All data were reported in the tables as mean ± standard deviation (SD) unless stated otherwise. Where possible, a meta-analysis (OpenMeta[Analyst]) was completed to produce a pooled mean and 95% confidence intervals (CI). An analysis was only conducted if there were at least two studies with mean and standard deviations. The DerSimonian-Laird continuous random-effects analysis method was used for the meta-analysis, with I-squared (I^2) used to assess the heterogeneity of the data. I^2 of 0–40% was considered low heterogeneity, 40–75%: moderate heterogeneity and > 70% was considered high heterogeneity [16]. The forest plots (mean and 95% CI) presented the results of the meta-analysis.

Results

Identification of Studies

The literature search captured 1114 papers (Fig. 1). After the screening process, 73 publications were included in the final review [3, 5, 8, 20, 2325, 29, 3195].

Study Characteristics

In total, 6212 participants were recorded throughout the seventy-three studies (Table 1). Fifteen studies explored sevens (21%) [3, 5, 3538, 47, 51, 60, 62, 67, 7072, 78] while fifty-eight studies investigated rugby union (79%) [8, 20, 2325, 29, 3134, 3946, 4850, 5259, 61, 6366, 68, 69, 7377, 7995]. Four studies (5%) focused on training (three in rugby union [32, 80, 90] and one in sevens [47]), while two studies investigated training and matches in rugby union (4%) [34, 42] and one in sevens (1%) [51]. The other sixty-six studies (90%) focused on match-play only [3, 5, 8, 20, 2325, 29, 31, 33, 3541, 4346, 4850, 5279, 81,82,83,84,85,86,87,88,89, 91,92,93,94,95]. The studies included, provincial, national, international, professional, experienced, novice and collegiate players. Studies were recorded from the Super Rugby competition [29, 31, 41, 43, 49, 50, 55, 59, 73, 75], Six Nations Championship [8, 33, 88], English Premiership [45, 46, 48, 68], World Rugby Sevens World Series [3, 51, 72], Bledisloe Cup [63], Pro14 [23], and the Rugby World Cup [92, 93].

Table 1 Characteristics of studies that were included

Twenty-four studies used microtechnology as a method to record collision demands (33%) [20, 29, 32, 35, 36, 38, 42, 47, 48, 51, 53, 58, 59, 61, 62, 76, 77, 8084, 91, 95]

and thirty-seven studies used video-based analysis (51%) [3, 5, 8, 31, 33, 40, 41, 4346, 49, 50, 54, 55, 57, 60, 6365, 6875, 79, 8589, 9294] (Table 1). Twelve studies used both microtechnology and video-based analysis to capture collision demands (16%) [2325, 34, 37, 39, 52, 56, 66, 67, 78, 90]. Seven studies (21%) used the GPSports’ SPI Pro device [29, 39, 8183, 90, 91] and GPSports’ SPI HPU [3438, 42, 59], 18% used Catapult Minimax S4 [32, 47, 52, 53, 56, 58] and 12% used the StatSports GPS technology [25, 48, 61, 84]. Specifics of both the microtechnology device and software used are provided in Additional file 1: Table S2. Similarly, camera specifics and the video-based analysis system used can be found in Additional file 1: Table S3.

Microtechnology

Rugby Union Match-Play

Ten studies recorded collision frequency using microtechnology in match-play (14%) [20, 2325, 39, 52, 53, 58, 84, 91] (Table 2). Two studies in rugby union recorded collisions per match [23, 39], while two recorded per position [24, 25]. One study recorded the impacts per min (0.7 ± 0.4 impacts per min) [58]. Macleod et al. (2018) recorded the frequency of collisions per minute per position [25]. Tackles per match [39, 52] and impacts per match [52] for forwards and backs were recorded [20, 39]. Three studies recorded load per collision [25, 39, 84].

Table 2 Characteristics of collision frequency detected by microtechnology in rugby union and rugby sevens

Sixteen studies recorded the intensity of collisions by using microtechnology (22%) (Table 3) [20, 25, 29, 39, 42, 48, 59, 61, 76, 77, 81,82,83, 90, 91, 95]. Forwards on average (frequency) experience 52.5 (29.8–75.2) very heavy impacts and 10.8 (4.4–17.1) severe impacts per match (Fig. 2) [29, 76, 77]. Backs experience on average 41.7 (26.4–57.0) very heavy impacts and 6.7 (5.1–8.4) severe impacts per match [29, 76, 77] (Fig. 2). Three studies recorded the relative frequency of collisions by intensity [81,82,83]. On average, forwards experience 9.1 (7.5–10.8) impacts > 5 g per min [81, 83] (Fig. 3). Backs experience on average 9.5 (8.1–10.1) impacts > 5 g per min [81, 83]. Note, Tee et al. only included > 5 g impact since it included > 8 g impacts [83]. Players experienced the highest amount of contacts in the first 20–30 min of a match and the least amount of contacts between 60 and 70 min [82]. Forwards experience more very heavy contacts in the second half of the match in comparison to the first half of the match. Backs experience fewer impacts in the second half of the match in comparison to the first half of the match [29]. There was no difference in impacts > 8 g per min for backs and forwards across the match [81]. Forwards experience more impacts > 5 g per min in 0–10 and 50–60 min and experienced the least amount in the 20–30 min, 40–50 min and 60–70 min intervals of the match. Backs experience more impacts > 5 g in the 0–10 min interval of the match and the 20–30 min interval of the match and the least in the 70–80 min interval [81].

Table 3 Characteristics of collision intensity detected by microtechnology in rugby union and rugby sevens
Fig. 2
figure 2

Meta-analysis of studies reporting absolute very heavy and severe impacts per match (n) from microtechnology in rugby union. The forest plot (mean and 95% confidence interval (CI)) presents the results of the meta-analysis of the pooled data estimates for the absolute very heavy and severe impact frequency for a forwards, b backs, c forwards and d backs. The squares and horizontal lines represent individual study mean and 95% CI and the diamond presents the pooled mean and 95% CI. The bigger the square the larger the sample size

Fig. 3
figure 3

Meta-analysis of studies reporting relative > 5 g impacts frequency per match (n min−1) from microtechnology in rugby union. The forest plot (mean and 95% confidence interval (CI)) presents the results of the meta-analysis of the pooled data estimates for the > 5 g impacts per min per match frequency for forwards. The squares and horizontal lines represent individual study mean and 95% CI and the diamond presents the pooled mean and 95% CI. The bigger the square the larger the sample size

Rugby Union Training

Two studies recorded collision frequency using microtechnology during training (3%) [32, 80]. Bradley et al. (2015) recorded the contact number of weekly training sessions of forwards and backs. Note, match data were also included in this training week [32]. Takeda et al. (2014) recorded 10.4 ± 2.5 tackles and 37.6 ± 3.0 contacts during a training simulated match [80].

Sevens Match-Play

Eight studies (11%) reported collision frequency using microtechnology during match-play [3538, 47, 51, 62, 78]. One study reported positional groupings (forwards and backs) [78], another study reported the level of play [36] and another study reported collision frequency by sex [37] (Table 2). Collision types included impacts, collisions, tackles, rucks and scrums. Only one study recorded the relative frequency of tackles, ball carries in contact and rucks [62] and another study recorded relative frequency of impacts for forwards and backs [51]. Of the eight studies, only five reported the intensity of collisions (63%) (Table 3) [35, 36, 38, 51, 78]. Three studies recorded 16.9 (12.5–21.2) impacts > 10 g per match (Fig. 4) [35, 36, 38].

Fig. 4
figure 4

Meta-analysis of studies reporting absolute > 10 g impacts per match (n) from microtechnology in sevens. The forest plot (mean and 95% confidence interval (CI)) presents the results of the meta-analysis of the pooled data estimates for the absolute > 10 g impacts frequency per match. The squares and horizontal lines represent individual study mean and 95% CI and the diamond presents the pooled mean and 95% CI. The bigger the square the larger the sample size

Sevens Training

Only one study reported tackle frequency during training (on average 17.8 ± 4.4 tackles per week) [47].

Video-Based Analysis

Rugby Union Match-Play

Thirty-seven studies recorded the collision frequency using video-based analysis methods (51%) [8, 24, 31, 33, 34, 40, 41, 43,44,45,46, 49, 50, 52, 5457, 6366, 68, 69, 7375, 79, 8590, 9294] (Table 4). Thirty-five studies were conducted during matches (95%) [8, 24, 31, 33, 40, 41, 4346, 49, 50, 52, 5457, 6366, 68, 69, 7375, 79, 8589, 9294], one investigated training (3%) [90] and one study investigated matches and training (3%) [34]. On average (frequency) a total of 22.0 (19.0–25.0) scrums [33, 41, 44, 52, 63, 74, 94], 116.2 (62.7–169.7) rucks [8, 63], and 156.1 [121.2–191.0] tackles occur per match (Fig. 5) [8, 49, 50, 63, 64, 8789]. On average, forwards experience 12.8 (7.5–18.1) tackles [41, 43, 52, 68, 74] and backs experience 7.6 [4.3–10.9] tackles (Fig. 6) [41, 43, 52, 68, 74]. On average front row forwards perform 10.5 (5.7–15.2) tackles [31, 34, 43], back row forwards perform 15.9 (10.1–21.8) tackles [31, 43], inside backs perform 17.2 (3.6–30.9) tackles [31, 43] and outside backs perform 8.9 (2.0–15.7) tackles per match (Fig. 7) [31, 34, 43]. Props experience on average 5.5 [1.2–9.8] tackles per match [44, 65], locks experience 4.5 (3.6–5.4) tackles per match [44, 65], hookers experience 6.3 (5.2–7.4) tackles [44, 65] and scrumhalves experience 6.4 (1.8–11.0) tackles per match [44, 65] (Fig. 8).

Table 4 Characteristics of collision frequency detected by video-based analysis in rugby union and rugby sevens
Fig. 5
figure 5

Meta-analysis of studies reporting absolute total scrums, rucks, and tackles per match (n) from video-based analysis in rugby union. The forest plot (mean and 95% confidence interval (CI)) presents the results of the meta-analysis of the pooled data estimates for the total a scrums, b rucks and c tackles per match. The squares and horizontal lines represent individual study mean and 95% CI and the diamond presents the pooled mean and 95% CI. The bigger the square the larger the sample size

Fig. 6
figure 6

Meta-analysis of studies reporting absolute tackles per match (n) from video-based analysis in rugby union. The forest plot (mean and 95% confidence interval (CI)) presents the results of the meta-analysis of the pooled data estimates for the absolute tackle frequency for a forwards and b backs. The squares and horizontal lines represent individual study mean and 95% CI and the diamond presents the pooled mean and 95% CI. The bigger the square the larger the sample size

Fig. 7
figure 7

Meta-analysis of studies reporting absolute tackles per match (n) from video-based analysis in rugby union. The forest plot (mean and 95% confidence interval (CI)) presents the results of the meta-analysis of the pooled data estimates for the absolute tackle frequency for a front row forwards, b back row forwards, c inside backs and d outside backs. The squares and horizontal lines represent individual study mean and 95% CI and the diamond presents the pooled mean and 95% CI. The bigger the square the larger the sample size

Fig. 8
figure 8

Meta-analysis of studies reporting absolute tackles per match (n) from video-based analysis in rugby union. The forest plot (mean and 95% confidence interval (CI)) presents the results of the meta-analysis of the pooled data estimates for the absolute tackle frequency for a props, b locks, c hooker and d scrumhalf. The squares and horizontal lines represent individual study mean and 95% CI and the diamond presents the pooled mean and 95% CI. The bigger the square the larger the sample size

Rugby Union Training

Only one study reported collision frequency during training [90]. Vaz et al. (2012) reported that novice players perform an average of 28.2 ± 3.3 tackles during small-sided games, while experienced players perform 48.7 ± 3.3 tackles on average [90].

Sevens Match Play

Eight studies recorded the collision frequency by using video-based analysis (11%) (Table 4) [3, 5, 37, 60, 67, 7072]. Ross et al. (2015) recorded the relative frequency of rucks and tackles at provincial and international level [70]. Three studies recorded the frequency of collisions [37], contact actions [60], tackles, being tackled (ball-carrier) and scrums (in relation to high and low scoring matches) [67]. Clarke et al. (2016) recorded 51 collisions for males and 44 collisions for females in a single match [37]. On average, 14.1 (0–32.8) tackles occur per match [3, 67], 4.8 (0–11.8) rucks per match [5, 72] and 1.8 (1.7–2.0) scrums per match [5, 67, 71] (Fig. 9). Finally, backs and forwards experience more contacts in the second half of the match compared to the first half [60].

Fig. 9
figure 9

Meta-analysis of studies reporting absolute tackles, rucks, and scrums per match (n) from video-based analysis in sevens. The forest plot (mean and 95% confidence interval (CI)) presents the results of the meta-analysis of the pooled data estimates for the absolute frequency of a tackles, b rucks and c scrums per match. The squares and horizontal lines represent individual study mean and 95% CI and the diamond presents the pooled mean and 95% CI. The bigger the square the larger the sample size

Sevens Training

No video-based training studies were found for sevens.

Discussion

To our knowledge, this is the first systematic review on quantifying collision frequency and intensity in rugby union and rugby sevens. This review demonstrates that video-based analysis and microtechnology are the main methods used to quantify collisions in rugby union and sevens. Not surprisingly, the absolute collision frequency during sevens matches was lower than rugby union due to the shorter duration of the game and fewer players on the field. When comparing relative frequencies though, rugby union players seem to perform less tackles and ball carries into contact than sevens players, while rucks per minute were similar between the two rugby codes [55, 70]. Expressing collision frequencies relative to playing time provides coaches and players with the ‘collision density’ [96], a metric that can potentially be used in training to better prepare players for the collision demands of matches. With that said, only two studies expressed collisions or contact events per minute in sevens [62, 70], which highlights an area for further work. In rugby union match-play, forwards experience more tackles than backs (12.8 (7.5–18.1) tackles and 7.6 (4.3–10.9) tackles, respectively). Another key finding of this review is that forwards experience more very heavy impacts (52.5 (29.8–75.2) vs. 41.7 (26.4–57.0) very heavy impacts) and severe impacts (10.8 (4.4–17.1) vs. 6.7 (5.1–8.4) severe impacts) than backs in rugby union. Coaches are recommended to train players specific to their positional grouping for appropriate adaptations. In both rugby cohorts, only six studies were completed on females [35, 36, 62, 67, 77, 94] and two studies on both sexes [37, 38]. Overall, there was a lack of consistency on the definition of a collision. Also, grouping variables (i.e., how the positions were grouped) made it hard to make comparisons. It is recommended to integrate microtechnology and video-based analysis simultaneously to ensure maximal accuracy of metrics. Given the high injury incidence and burden of collision events, it is important that we adequately prepare athletes for collisions in training to meet the collision demands of matches.

To optimise training, researchers, trainers and sport practitioners typically study competition activities and demands, and attempt to replicate these demands in training [76, 78, 93, 97]. Training is subsequently monitored to ensure athletes meet said competition activities and demands [34]. Monitoring training also ensures athletes are not exposed to any unnecessary injury risks, and are positively adapting to training [34]. Only four studies quantified collision frequencies and/or intensities in training—three in rugby union [32, 80, 90] and one in sevens [47], while 66 studies quantified frequencies and/or intensities of collisions in matches. Three studies related the frequency and intensity of collisions during training to matches—two in rugby union [34, 42] and one in sevens [51]. In both studies, collision frequencies and intensities were lower in training, suggesting that players may not be adequately preparing for matches [34, 51]. Indeed, the adaptations for a “collision-fit” player are likely to respond to general training principles including the concept of periodization [98]. Using general training concepts, such as periodisation, and collision demands data from match-play, coaches and practitioners can develop training programmes to enhance players’ adaptability and capacity to repeatably engage in physical-technical contests without increasing their risk of injury; in other words, building a ‘collision-fit’ player. Recently, this has been suggested for skill training and Hendricks et al. (2018) described such a periodised plan for the rugby tackle [99]. Understanding the adaptations for a “collision-fit” player will also allow for safer return to play protocols for collision sport athletes and reduce the risk of re-injury. To inform collision preparation practice, more work on collision training and its relationship to match demands, player development, performance and/or (re)injury risk is required. Collision training studies of this nature should also ideally be collected over more than one season and from multiple teams.

Collision frequency and intensities have been quantified in studies using video-based analysis (n = 37), microtechnology (n = 24) or both methods (n = 12). Each method has its advantages and disadvantages. For example, video-based analysis is laborious and reliant on human observation, while it may capture more contextual detail of the collision event [16]. Conversely, microtechnology may be more efficient and objective, but its reliability and validity for quantifying collision demands is inconclusive at this stage [16, 24, 25]. Also, customised algorithms detect collisions, making study comparisons difficult [100]. With that said, studies are emerging to support collision metrics when used in conjunction with video-based analysis [23, 25]. Although some literature supports the use of microtechnology for collision monitoring, there is still a lack of validity regarding other metrics and therefore more investigation is needed [23]. As such, a superior approach to quantifying collision demands from a research and practitioner perspective may be to integrate video and microtechnology [18, 19]. Using both video and microtechnology, coaches, practitioners and researchers are able to cross check the microtechnology data with video, determine its accuracy and distinguish between collision events [18, 24, 25].

If the goal is to ensure players are well-prepared for matches by providing the optimal collision frequency and intensity dose, the metrics (i.e., collisions, contacts, scrums, tackles, rucks and mauls) and grouping variables (i.e., specific positions, forwards and backs) between training and matches need to be consistent and more accurate. In other words, how collision demands are reported for matches should be useful to the coach and practitioner, and transferable to a training setting. Therefore, metrics and grouping variables between the two settings need to be consistent to ensure this transfer. Strong engagement with the coach and practitioner when developing reporting metrics is therefore recommended [101]. Recently, a consensus document for the video-based analysis of contact events was published to improve the consistency and quality of video-based analysis work in rugby union and sevens [18]. A similar consensus-based approach may be required for microtechnology collision metrics [16, 22]. As mentioned, many studies report collisions differently, making study comparisons difficult between groups, methods used and between rugby cohorts. As a result, this limited the current synthesis. Collision intensity metrics in particular were inconsistent between studies. The lack of consistency between studies is a key factor limiting our understanding of collision loads [16]. Additionally, the intensity of collisions is difficult to compare longitudinally, given that technology is constantly evolving. More recent technology is likely more accurate as algorithms are improved over time ensuring MEMs have a high specificity and sensitivity, and are more likely to detect a collision when it occurs [23], although limited studies can confirm this [25].

The purpose of this review was to synthesise the frequency and intensity of collisions during training and matches in rugby union and sevens. In both rugby cohorts, future studies should investigate training in comparison to match-play. Additionally, future studies should explore women’s rugby. Many of these groups were understudied and are very important in our rugby community. A consensus-based approach for microtechnology is warranted since grouping variables and metrics were inconsistent throughout the studies. Beyond this, there are a number of other factors that can affect how players respond and adapt to different frequencies and intensities of contact. Collision events in rugby union and sevens are dynamic and have a major technical-skill component [102, 103]. The opposing players’ technical ability may also affect the perceived intensity of the collision event. The perceived physical and technical demands of collision events can also be captured using subjective ratings such as rating of perceived exertion (RPE) [104] and rating of perceived challenge (RPC) [98, 104], respectively. These subjective ratings are useful when planning and monitoring training [104]. Also, collisions are interspersed between periods of high intensity running (sprinting, accelerations, decelerations) and low-intensity activities (walking, jogging). As such, advanced collision training should also include periods of high-intensity running to mimic complete match demands and fatigue conditions [97].

Conclusion

In conclusion, this review found a discrepancy in the number of studies quantifying collision demands in training compared to matches. While more work on quantifying the collision demands of training is required, studies should also compare training and matches if we are to improve our understanding of the relationship between training and matches. Another key finding is that the main method for quantifying collisions was video-based analysis. To improve the relationship between matches and training, integrating both video-based analysis and microtechnology is recommended, and the metrics and grouping variables between training and matches should be consistent. Per minute, rugby sevens players perform more tackles and ball carries into contact than rugby union players and forwards experienced more tackles than backs (12.8 (7.5–18.1) tackles and 7.6 (4.3–10.9) tackles, respectively). Another key finding in this review is that forwards experience more very heavy impacts (52.5 (29.8–75.2) vs. 41.7 (26.4–57.0) very heavy impacts) and severe impacts (10.8 (4.4–17.1) vs. 6.7 (5.1–8.4) severe impacts) than backs in rugby union. The frequency and intensity of collisions in training and matches may lead to adaptations for a “collision-fit” player and lend themselves to general training principles such as periodisation for optimum collision adaptation. Subjective measures such as RPE and RPC should be incorporated into the monitoring and management of the collision section of training to understand the internal load.

Availability of Data and Materials

Not applicable.

References

  1. Hendricks S, Karpul D, Lambert M. Momentum and kinetic energy before the tackle in rugby union. J Sports Sci Med. 2014;13:557–63.

    PubMed  PubMed Central  Google Scholar 

  2. Hendricks S, Lambert M. Tackling in rugby: coaching strategies for effective technique and injury prevention. Int J Sports Sci Coach. 2010;5(1):117–35.

    Google Scholar 

  3. Hendricks S, Sin DW, van Niekerk T, den Hollander S, Brown J, Maree W, et al. Technical determinants of tackle and ruck performance in international rugby sevens. Eur J Sport Sci. 2019;20:1–27.

    Google Scholar 

  4. Fuller CW, Taylor A, Molloy MG. Epidemiological study of injuries in international rugby sevens. Clin J Sport Med. 2010;20(3):179–84.

    Google Scholar 

  5. Higham DG, Hopkins WG, Pyne DB, Anson JM. Performance indicators related to points scoring and winning in international rugby sevens. J Sport Sci Med. 2014;13(2):358–64.

    Google Scholar 

  6. Ross A, Gill N, Cronin J. Match analysis and player characteristics in rugby sevens. Sports Med. 2013;44:357–67.

    Google Scholar 

  7. Ortega E, Villarejo D, Palao JM. Differences in game statistics between winning and losing rugby teams in the six nations tournament. J Sport Sci Med. 2009;8(4):523–7.

    Google Scholar 

  8. Hendricks S, van Niekerk T, Sin DW, Lambert M, den Hollander S, Brown J, et al. Technical determinants of tackle and ruck performance in international rugby union. J Sports Sci. 2018;36(5):522–8.

    Google Scholar 

  9. Wheeler K, Askew C, Sayers M. Effective attacking strategies in rugby union. Eur J Sport Sci. 2010;1:19–35.

    Google Scholar 

  10. Fuller CW. Injury risk (burden), risk matrices and risk contours in team sports: a review of principles, practices and problems. Sport Med. 2018;48:1597–606.

    Google Scholar 

  11. Brooks JHM, Fuller CW, Kemp SPT, Reddin DB. Epidemiology of injuries in English professional rugby union: part 1 match injuries. Br J Sports Med. 2005;39(10):757–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Schwellnus MP, Thomson A, Derman W, Jordaan E, Readhead C, Collins R, et al. More than 50% of players sustained a time-loss injury (> 1 day of lost training or playing time) during the 2012 Super Rugby Union Tournament : a prospective cohort study of 17,340 player-hours. Br J Sports Med. 2014;1:1306–15.

    Google Scholar 

  13. Roberts SP, Trewartha G, England M, Shaddick G, Stokes KA. Epidemiology of time-loss injuries in English community-level rugby union. BMJ Open. 2013;3(11):1–7.

    Google Scholar 

  14. Williams S, Trewartha G, Kemp S, Stokes K. A meta-analysis of injuries in senior men’s professional rugby union. Sport Med. 2013;43(10):1043–55.

    Google Scholar 

  15. Lopez V, Galano GJ, Black CM, Gupta AT, James DE, Kelleher KM, et al. Profile of an American amateur rugby union sevens series. Am J Sports Med. 2012;40(1):179–84.

    Google Scholar 

  16. Naughton M, Jones B, Hendricks S, King D, Murphy A, Cummins C. Quantifying the collision dose in rugby league: a systematic review, meta-analysis, and critical analysis. Sport Med Open. 2020;6(1):1–26.

    Google Scholar 

  17. den Hollander S, Jones B, Lambert M, Hendricks S. The what and how of video analysis research in rugby union: a critical review. Sport Med Open. 2018;4(1):27.

    Google Scholar 

  18. Hendricks S, Till K, Den HS, Savage TN, Roberts SP, Tierney G, et al. Consensus on a video analysis framework of descriptors and definitions by the Rugby Union Video Analysis Consensus group. Br J Sports Med. 2020;54:566–72.

    Google Scholar 

  19. Whitehead S, Till K, Weaving D, Jones B. The use of microtechnology to quantify the peak match demands of the football codes: a systematic review. Sport Med. 2018;48(11):2549–75.

    Google Scholar 

  20. Cunniffe B, Proctor W, Baker J, Davies B. An evaluation of the physiological demands elite rugby union using global positioning system tracking software. J Strength Cond Res. 2009;23(4):1195–203.

    PubMed  PubMed Central  Google Scholar 

  21. Roe G, Halkier M, Beggs C, Till K, Jones B. The use of accelerometers to quantify collisions and running demands of rugby union match-play. Int J Perform Anal Sport. 2016;16(2):590–601.

    Google Scholar 

  22. Cummins C, Orr R, Connor HO. Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sports Med. 2013;43:1025–42.

    Google Scholar 

  23. Tierney P, Blake C, Delahunt E. The relationship between collision metrics from micro-sensor technology and video-coded events in rugby union. Scand J Med Sci Sport. 2020;30(11):2193–204.

    Google Scholar 

  24. Reardon C, Tobin DP, Tierney P, Delahunt E. Collision count in rugby union: a comparison of micro-technology and video analysis methods. J Sports Sci. 2017;35(20):2028–34.

    Google Scholar 

  25. MacLeod SJ, Hagan C, Egaña M, Davis J, Drake D. The use of microtechnology to monitor collision performance in professional rugby union. Int J Sports Physiol Perform. 2018;13(8):1075–82.

    Google Scholar 

  26. Gabbett T, Jenkins D, Abernethy B. Physical collisions and injury during professional rugby league skills training. J Sci Med Sport. 2010;13(6):578–83.

    Google Scholar 

  27. Hulin BT, Gabbett TJ, Johnston RD, Jenkins DG. Wearable microtechnology can accurately identify collision events during professional rugby league match-play. J Sci Med Sport. 2017;20(7):638–42.

    Google Scholar 

  28. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339(7716):332–6.

    Google Scholar 

  29. McLellan C. Performance analysis of super 15 rugby match-play using portable micro-technology. J Athl Enhanc. 2013;02(05):2–5.

    Google Scholar 

  30. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Austin D, Gabbett T, Jenkins D. The physical demands of Super 14 rugby union. J Sci Med Sport. 2011;14(3):259–63.

    PubMed  PubMed Central  Google Scholar 

  32. Bradley WJ, Cavanagh B, Douglas W, Donovan TF, Twist C, Morton JP, et al. Energy intake and expenditure assessed ‘in-season’ in an elite European rugby union squad. Eur J Sport Sci. 2015;15(6):469–79.

    PubMed  PubMed Central  Google Scholar 

  33. Bradley E, Hogg B, Archer D. Effect of the PreBind Engagement Process on scrum timing and stability in the 2013 to 2016 six nations authors. Int J Sports Physiol Perform. 2017;13:903–9.

    Google Scholar 

  34. Campbell PG, Peake JM, Minett GM. The specificity of rugby union training sessions in preparation for match demands. Int J Sports Physiol Perform. 2017;13(4):496–503.

    Google Scholar 

  35. Clarke AC, Anson JM, Pyne DB. Neuromuscular fatigue and muscle damage after a women’s rugby sevens tournament. Int J Sports Physiol Perform. 2015;10(6):808–14.

    Google Scholar 

  36. Clarke A, Anson J, Pyne D. The effect of running demands and impacts on post-tournament markers of inflammation and haemolysis in women’s rugby sevens. Annu Rev CyberTherapy Telemed. 2015;11:63.

    Google Scholar 

  37. Clarke A, Anson J, Pyne D. Proof of concept of automated collision detection technology in rugby sevens. J Strength Cond Res. 2016;31:1116–20.

    Google Scholar 

  38. Clarke AC, Anson JM, Pyne DB. Game movement demands and physical profiles of junior, senior and elite male and female rugby sevens players. J Sports Sci. 2017;35(8):727–33.

    PubMed  PubMed Central  Google Scholar 

  39. Coughlan GF, Green BS, Pook PT, Toolan E, Connor SPO. Physical game demands in Elite Rugby Union: a global positioning system analysis and possible implications for rehabilitation. J Orthop Sports Phys Ther. 2011;41(8):10–2.

    Google Scholar 

  40. Deutsch MU, Maw GJ, Jenkins D, Reaburn P. Heart rate, blood lactate and kinematic data of elite colts (under-19) rugby union players during competition. J Sports Sci. 1998;16(6):561–70.

    CAS  Google Scholar 

  41. Deutsch MU, Kearney GA, Rehrer NJ. Time-motion analysis of professional rugby union players during match-play. J Sports Sci. 2007;25(4):461–72.

    CAS  Google Scholar 

  42. Dubois R, Lyons M, Paillard T, Maurelli O, Prioux J. Influence of weekly workload on physical, biochemical and psychological characteristics in professional rugby union players over a competitive season. J Strength Cond Res. 2020;34(2):527–45.

    Google Scholar 

  43. Duthie G, Pyne D, Hooper S. Time motion analysis of 2001 and 2002 super 12 rugby. J Sports Sci. 2005;23(5):523–30.

    Google Scholar 

  44. Eaton C, George K. Position specific rehabilitation for rugby union players. Part I: empirical movement analysis data. Phys Ther Sport. 2006;7(1):22–9.

    Google Scholar 

  45. Fuller CW, Brooks JHM, Cancea RJ, Hall J, Kemp SPT. Contact events in rugby union and their propensity to cause injury. Br J Sports Med. 2007;41(12):862–7.

    PubMed  PubMed Central  Google Scholar 

  46. Fuller CW, Ashton T, Brooks JHM, Cancea RJ, Hall J, Kemp SPT. Injury risks associated with tackling in rugby union. Br J Sports Med. 2008;44(3):159–67.

    PubMed  PubMed Central  Google Scholar 

  47. Gibson N, Boyd A, Murry A. Countermovement jump is not affected during final competition preparation periods in elite rugby sevens players. J Strength Cond Res. 2015;30(3):777–83.

    Google Scholar 

  48. Grainger A, McMahon JJ, Comfort P. Assessing the frequency and magnitude of match impacts accrued during an elite rugby union playing season. Int J Perform Anal Sport. 2018;18(4):507–22.

    Google Scholar 

  49. Hendricks S, Roode B, Matthews B, Lambert M. Defensive strategies in rugby union. Percept Mot Skills. 2013;117(1):65–87.

    Google Scholar 

  50. Hendricks S, Matthews B, Roode B, Lambert M. Tackler characteristics associated with tackle performance in rugby union. Eur J Sport Sci. 2014;14(8):753–62.

    Google Scholar 

  51. Higham DG, Pyne DB, Anson JM, Hopkins WG, Eddy A. Comparison of activity profiles and physiological demands between international rugby sevens matches and training. J Strength Cond Res. 2016;30(5):1287–94.

    Google Scholar 

  52. Jones MR, West DJ, Harrington BJ, Cook CJ, Bracken RM, Shearer DA, et al. Match play performance characteristics that predict post-match creatine kinase responses in professional rugby union players. BMC Sports Sci Med Rehabil. 2014;6(1):1–7.

    Google Scholar 

  53. Jones MR, West DJ, Crewther BT, Cook CJ, Kilduff LP. Quantifying positional and temporal movement patterns in professional rugby union using global positioning system. Eur J Sport Sci. 2015;15(6):488–96.

    Google Scholar 

  54. Lacome M, Piscione J, Hager J-P, Carling C. Analysis of running and technical performance in substitute players in international male rugby union competition. Int J Sport Physiol Perform. 2016;11(6):783–92.

    Google Scholar 

  55. Lindsay A, Draper N, Lewis J, Gieseg SP, Gill N. Positional demands of professional rugby. Eur J Sport Sci. 2015;15(6):480–7.

    Google Scholar 

  56. Lindsay A, Lewis JG, Gill N, Draper N, Gieseg SP. No relationship exists between urinary NT-proBNP and GPS technology in professional rugby union. J Sci Med Sport. 2017;20(8):790–4.

    Google Scholar 

  57. McIntosh AS, Savage TN, McCrory P, FréchÈde BO, Wolfe R. Tackle characteristics and injury in a cross section of rugby union football. Med Sci Sports Exerc. 2010;42(5):977–84.

    Google Scholar 

  58. McLaren SJ, Weston M, Smith A, Cramb R, Portas MD. Variability of physical performance and player match loads in professional rugby union. J Sci Med Sport. 2015;19(6):493–7.

    Google Scholar 

  59. Owen SM, Venter RE, Du TS, Kraak WJ. Acceleratory match-play demands of a super rugby team over a competitive season. J Sports Sci. 2015;33(19):2061–9.

    Google Scholar 

  60. Peeters A, Carling C, Piscione J, Lacome M. In-match physical performance fluctuations in international rugby sevens competition. J Sports Sci Med. 2019;18(3):419–26.

    PubMed  PubMed Central  Google Scholar 

  61. Pollard BT, Turner AN, Eager R, Cunningham DJ, Cook CJ, Hogben P, et al. The ball in play demands of international rugby union. J Sci Med Sport. 2018;21(10):1090–4.

    Google Scholar 

  62. Portillo J, Del Coso J, Abian-vicén J. Effects of caffeine ingestion on skill performance during an international female rugby sevens competition. J Strength Cond Res. 2016;31:3351–7.

    Google Scholar 

  63. Quarrie KL, Hopkins WG. Changes in player characteristics and match activities in Bledisloe Cup rugby union from 1972 to 2004. J Sports Sci. 2007;25(8):895–903.

    Google Scholar 

  64. Quarrie KL, Hopkins WG. Tackle injuries in professional rugby union. Am J Sports Med. 2008;36(9):1705–16.

    Google Scholar 

  65. Quarrie KL, Hopkins WG, Anthony MJ, Gill ND. Positional demands of international rugby union: evaluation of player actions and movements. J Sci Med Sport. 2012;16(4):353–9.

    Google Scholar 

  66. Reardon C, Tobin DP, Tierney P, Delahunt E. The worst case scenario: locomotor and collision demands of the longest periods of gameplay in professional rugby union. PLoS ONE. 2017;12(5):1–11.

    Google Scholar 

  67. Reyneke J, Hansen K, Cronin JB, Macadam P. An investigation into the influence of score differential on the physical demands of international women’s rugby sevens match play. Int J Perform Anal Sport. 2018;18(4):523–31.

    Google Scholar 

  68. Roberts SP, Trewartha G, Higgitt RJ, El-Abd J, Stokes KA. The physical demands of elite English rugby union. J Sports Sci. 2008;26(8):825–33.

    Google Scholar 

  69. Roberts SP, Trewartha G, England M, Stokes KA. Collapsed scrums and collision tackles: what is the injury risk? Br J Sports Med. 2014;49(8):536–40.

    Google Scholar 

  70. Ross A, Gill ND, Cronin JB. A comparison of the match demands of international and provincial rugby sevens. Int J Sports Physiol Perform. 2015;10(6):786–90.

    Google Scholar 

  71. Ross A, Gill N, Cronin J. The match demands of international rugby sevens. J Sports Sci. 2015;33(10):1035–41.

    Google Scholar 

  72. Ross A, Gill N, Cronin J, Malcata R. Defensive and attacking performance indicators in rugby sevens. Int J Perform Anal Sport. 2016;16(2):569–80.

    Google Scholar 

  73. Schoeman R, Coetzee D, Schall R. Positional tackle and collision rates in super rugby. Int J Perform Anal Sport. 2015;15(3):1022–36.

    Google Scholar 

  74. Smart DJ, Gill ND, Beaven CM, Cook CJ, Blazevich AJ. The relationship between changes in interstitial creatine kinase and game-related impacts in rugby union. Br J Sports Med. 2008;42(3):198–201.

    CAS  Google Scholar 

  75. Smart D, Hopkins WG, Quarrie KL, Gill N. The relationship between physical fitness and game behaviours in rugby union players. Eur J Sport Sci. 2014;14:S8–17.

    Google Scholar 

  76. Suárez-Arrones LJ, Portillo LJ, González-Ravé JM, Muoz VE, Sanchez F. Match running performance in Spanish elite male rugby union using global positioning system. Isokinet Exerc Sci. 2012;20(2):77–83.

    Google Scholar 

  77. Suarez-Arrones L, Portillo J, Pareja-Blanco F, De Villareal ES, Sánchez-Medina L, Munguía-Izquierdo D. Match-play activity profile in elite women’s rugby union players. J Strength Cond Res. 2013;28(2):452–8.

    Google Scholar 

  78. Suarez-Arrones L, Arenas C, López G, Requena B, Terrill O, Mendez-Villanueva A. Positional differences in match running performance and physical collisions in men rugby sevens. Int J Sports Physiol Perform. 2014;9(2):316–23.

    Google Scholar 

  79. Takarada Y. Evaluation of muscle damage after a rugby match with special reference to tackle plays. Br J Sports Med. 2003;37(5):416–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Takeda M, Sato T, Hasegawa T, Shintaku H, Kato H, Yamaguchi Y, et al. The effects of cold water immersion after rugby training on muscle power and biochemical markers. J Sport Sci Med. 2014;13(3):616–23.

    Google Scholar 

  81. Tee JC, Coopoo Y. Movement and impact characteristics of South African professional rugby union players. S Afr J Sports Med. 2015;27(March):33–9.

    Google Scholar 

  82. Tee JC, Lambert MI, Coopoo Y. Impact of fatigue on positional movements during professional rugby union match play. Int J Sports Physiol Perform. 2017;12(4):554–61.

    Google Scholar 

  83. Tee JC, Coopoo Y, Lambert M. Pacing characteristics of whole and part-game players in professional rugby union. Eur J Sport Sci. 2020;20(6):722–33.

    Google Scholar 

  84. Tierney P, Blake C, Delahunt E. Physical characteristics of different professional rugby union competition levels. J Sci Med Sport. 2021;24:1267–71.

    Google Scholar 

  85. Tucker R, Raftery M, Fuller GW, Hester B, Kemp S, Cross MJ. A video analysis of head injuries satisfying the criteria for a head injury assessment in professional rugby union: a prospective cohort study. Br J Sports Med. 2017;51(15):1147–51.

    Google Scholar 

  86. van Rooyen M, Rock K, Prim S, Lambert M. The quantification of contacts with impact during professional rugby matches. Int J Perform Anal Sport. 2008;8(1):113–26.

    Google Scholar 

  87. Van Rooyen MK. A statistical analysis of tackling performance during international rugby union matches from 2011. Int J Perform Anal Sport. 2012;12(3):517–30.

    Google Scholar 

  88. van Rooyen M, Yasin N, Viljoen W. Characteristics of an ‘effective’ tackle outcome in six nations rugby. Eur J Sport Sci. 2014;14(2):123–9.

    Google Scholar 

  89. Vaz L, van Rooyen M, Sampaio J. Rugby game-related statistics that discriminate between winning and losing teams in IRB and super twelve close games. J Sport Sci Med. 2010;9(1):51–5.

    Google Scholar 

  90. Vaz L, Leite N, João PV, Gonçalves B, Sampaio J. Differences between experienced and novice rugby union players during small-sided games. Percept Mot Skills. 2012;115(2):594–604.

    Google Scholar 

  91. Venter R, Opperman E, Opperman S. The use of global positioning system (GPS) tracking devices to assess movement demands and impacts in under-19 rugby union match play. African J Phys Heal Educ Recreat Danc. 2011;17(1):1–8.

    Google Scholar 

  92. Villarejo D, Palao JM, Toro EO. Match profiles for establishing position specific rehabilitation for rugby union players. Int J Perform Anal Sport. 2013;13(2):567–71.

    Google Scholar 

  93. Villarejo D, Palao JM, Ortega E, Gomez-Ruano MÁ, Kraak W. Match-related statistics discriminating between playing positions during the men’s 2011 Rugby World Cup. Int J Perform Anal Sport. 2015;15(1):97–111.

    Google Scholar 

  94. Virr JL, Game A, Bell GJ, Syrotuik D. Physiological demands of women’s rugby union: time-motion analysis and heart rate response. J Sports Sci. 2014;32(3):239–47.

    Google Scholar 

  95. Yamamoto H, Takemura M, Iguchi J, Tachibana M, Tsujita J, Hojo T. In-match physical demands on elite Japanese rugby union players using a global positioning system. BMJ Open Sport Exerc Med. 2020;6(1):1–10.

    Google Scholar 

  96. Waldron M, Jones C, Melotti L, Brown R, Kilduff LP. Collision monitoring in elite male rugby union using a new instrumented mouth-guard. J Sport Exerc Sci. 2021;5(3):179–87.

    Google Scholar 

  97. Ziv G, Lidor R. On-field performances of rugby union players—a review. J Strength Cond Res. 2016;30(3):881–92.

    Google Scholar 

  98. Mujika I, Halson S, Burke LM, Balagué G, Farrow D. An integrated, multifactorial approach to periodization for optimal performance in individual and team sports. Int J Sports Physiol Perform. 2018;13(5):538–61.

    Google Scholar 

  99. Hendricks S, Till K, Oliver JL, Johnston RD, Attwood M, Brown J, et al. Technical skill training framework and skill load measurements for the rugby union tackle. Strength Cond J. 2018;40(5):44–59.

    Google Scholar 

  100. Chambers RM, Gabbett TJ, Gupta R, Josman C, Bown R, Stridgeon P, et al. Automatic detection of one-on-one tackles and ruck events using microtechnology in rugby union. J Sci Med Sport. 2019;2018:1–6.

    Google Scholar 

  101. Nosek P, Brownlee TE, Drust B, Andrew M, Nosek P, Brownlee TE, et al. Feedback of GPS training data within professional English soccer: a comparision of decision making and perceptions between coaches, players and performance staff. Sci Med Footb. 2020;5:35–47.

    Google Scholar 

  102. West SW, Williams S, Kemp SPT, Cross MJ, Fuller CW, Taylor A, et al. Patterns of training volume and injury risk in elite rugby union : An analysis of 1.5 million hours of training exposure over eleven seasons. J Sports Sci. 2019;38:1–10.

    Google Scholar 

  103. Hendricks S, Davidow D, Viljoen W, Burger N, Lambert M, Readhead C, et al. Video analysis of contact technique during head collisions in rugby union. BJSM. 2017;51:328.

    Google Scholar 

  104. Hendricks S, Till K, Oliver JL, Johnston RD, Attwood MJ, Brown JC, et al. Rating of perceived challenge as a measure of internal load for technical skill performance. Br J Sports Med. 2019;53(10):611–3.

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the University of Cape Town and the National Research Foundation for the funding and support from the Vice-Chancellor award, the UCT Master’s scholarship and the National Research Foundation Postgraduate Scholarship during the study.

Funding

University of Cape Town (Vice Chancellor Award, Master's Research Scholarship) and National Research Foundation (NRF) (NRF Postgraduate Award).

Author information

Authors and Affiliations

Authors

Contributions

MN, BJ, SH and LP conceptualized the idea for the manuscript. LP conducted the systematic search. The full text articles were screened for eligibility by LP and MN. LP and MN completed the quality assessment. LP drafted the manuscript and all authors contributed to the final draft. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Lara Paul.

Ethics declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

Lara Paul, Mitchell Naughton, Ben Jones, Demi Davidow, Amir Patel, Mike Lambert and Sharief Hendricks declare that they have no competing interests relevant to the content of this review.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Methodological quality assessment of the final full text articles according to Downs et al. [30]. Table S2. Characteristics of studies using microtechnology to record collisions during match-play or training sessions. Table S3. Characteristics of studies using video-based analysis to record collisions during match-play or training sessions.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Paul, L., Naughton, M., Jones, B. et al. Quantifying Collision Frequency and Intensity in Rugby Union and Rugby Sevens: A Systematic Review. Sports Med - Open 8, 12 (2022). https://doi.org/10.1186/s40798-021-00398-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40798-021-00398-4

Keywords

  • Rugby
  • Microtechnology
  • Video-based analysis
  • Collisions
  • Training
  • Injury prevention