The goal of rugby is to win each match. To increase the odds of success, from a physical preparation perspective, there are often two areas of focus: physical performance and player availability. Players with superior physical attributes are associated with higher levels of competition (Argus et al., 2012; Olds, 2001) and it can, therefore, be surmised that possessing superior attributes might lead to better performance. Lower injury rates result in higher player availability, which positively influences the success of a team (Hägglund et al., 2013). These two focus areas can be improved by developing one underlying theme: robustness. Robustness of a system is defined by the Oxford dictionary as “the ability to withstand or overcome adverse conditions or rigorous testing” (Simpson & Weiner, 1989, para. 2). The player’s body is considered the system. The adverse conditions or rigorous testing is considered the demands required to compete in an optimal manner to ensure success and avoid injury.
Research taking into account various training stimuli and their relationship to injury have often found a similar “J-shape” relationship, visualised in Figure 2.3. It is well documented that a change in training load that is too high, relative to an athlete’s current capacity, will increase the likelihood of injury (Malone et al., 2017; Blanch & Gabbett, 2016). A more novel finding is that when a change in training stimulus relative to an athlete’s current capacity over a given period is too low, there is not only a risk of inferior performance, as a result of detraining, but
29 | P a g e an increased likelihood of injury. Blanch and Gabbett (2016) demonstrated this relationship in a series of studies involving three different sports (cricket, rugby league and Australian rules football) (Figure 2.3). Malone and colleagues (2017) examined the relationship between chronic training loads, number of exposures to maximal velocity, the distance covered at maximal velocity, percentage of maximal velocity in training and match-play and subsequent injury risk in 37 Gaelic footballers. Results from the study show that players who were under- and overexposed to maximal velocity efforts have an increased risk of injury. In order to develop robust athletes, it is important to identify the demands of competition. This will provide a basis of what specific stimuli are required by athletes to compete optimally, and it is then the coaching staff’s task to progress them safely to a point where they can withstand these demands of competition.
Figure 2.3. The relationship between change in training load and likelihood of subsequent injury. Acute:Chronic Workload Ratio is defined by Blanch and Gabbett (2016) as a comparison of the acute load (ie, the training that had been performed in the current week) with the chronic load (ie, the training that had been performed as a rolling average over the previous 4 weeks). Reproduced, with permission form Blanch and Gabbett (2016).
30 | P a g e As discussed in previous sections, it is beneficial to apply a training stimulus that will produce optimal results and develop robust athletes. Currently, available technology (GPS and video-based analysis) provides a starting point to capture data that describes the external loads of competition. As data is gathered, it becomes essential to interpret and present the data in such a way that it can be applied in a practical setting. Three methods of interpretation, each providing practical information, are used for the current study: full match, temporal pattern and peak period analysis.
F.1. FULL MATCH ANALYSIS
Full match analysis describes the total demands required of a player who has completed the entire duration of the match. Limited literature has described the demands of a full rugby match, with most setting a cut-off time, generally around 60 minutes, and reporting values for this time played (Jones et al., 2015; Tee and Coopoo, 2015). While this cut-off does not provide an absolute measure of the entire match, positional group comparisons can be drawn. Full match analysis is useful in a practical setting, enabling decisions around the volume of specific training. These practical applications include: providing requirements for match replacement sessions, a relative marker off which to base training sessions and drills, decisions around load
“top-ups” post-game for those who do not complete the full match, and benchmark goals for injured players returning to play, amongst others.
F.2. TEMPORAL PATTERN ANALYSIS
Temporal pattern analysis involves reporting a variable for various stages of a match. These stages are often split into eight equal periods, equating roughly 10 minutes each, as performed
31 | P a g e by Jones and colleagues (2015). Splitting the match into equal periods allows comparison of variables in different stages of a match and the identification of fluctuations in player performance. These fluctuations can be analysed to determine the effects of fatigue and pacing strategies throughout a match. In team sport, Waldron and Highton (2014) defined pacing as the distribution of energy resources that optimise match-running performance, and fatigue as a uni-directional construct that relates to the eventual reduction in physical performance compared with baseline values. It is suggested that team sport players regulate their efforts during match-play through macro-, meso- and micro-pacing strategies (Waldron & Highton, 2014). The macro-pacing strategy refers to the planned use of energy stores over an entire match, which is modulated through meso- (between halves) and micro-strategies (on a continual basis). In rugby league players (n = 52), differences were found between whole-game players and interchanged players. Interchanged players covered greater low-speed distances and total distances per quartile of a match than whole-game players. Different pacing strategies also existed between winning and losing teams (Black & Gabbett, 2014). Common pacing strategies previously found in rugby union include ‘slow-positive’ (higher intensity start and lower intensity finish) and ‘flat’ (no change throughout the match) strategies, where a ‘slow-positive’ strategy has been suggested to optimise running performance in all team sports (Waldron & Highton, 2014). Tee and colleagues (2017) showed different pacing strategies between positional groups in 46 professional rugby players. Forwards displayed a ’slow-positive’ pacing strategy, while Backs had a ‘flat’ strategy. Identifying pacing strategies and periods of fatigue should contribute to the decision-making process for substitution times and enhance position-specific conditioning.
32 | P a g e F.3. PEAK PERIOD ANALYSIS
Peak period analysis involves splitting match-play data into shorter (e.g. one minute) periods.
These periods are then used to calculate the 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10-minute moving rolling-averages for the most-intense periods of match-play. Moving average durations were chosen for the purpose of this study, as fixed duration analysis has been shown to underestimate the peak periods of play (Varley et al., 2012). The Power Law is used to provide a prediction of the peak intensities of each variable as a function of time. This method has been utilised in other contact (Delaney et al., 2017a; Delaney et al., 2015) and non-contact sports (Delaney et al., 2017b). Delaney and colleagues (2017a) investigated the duration-specific running intensities of 40 Australian Football players during 30 games. Similarly, Delaney and colleagues (2015) assessed 32 rugby league players across a single season. This method was also applied to a non-contact sport, soccer, where 24 players were studied over 40 professional matches (Delaney et al., 2017b). All three of the aforementioned studies identified the peak intensities of 1–10 minute durations of play in their respective sports, where the study on rugby league went on to predict the peak running values as a function of time using power law.
Although research has been undertaken in various sports, there is limited literature focussing on professional rugby (Cunningham et al., 2018; Read et al., 2018; Delany et al., 2017c). Peak period analysis provides a tool where training session intensities can be prescribed and monitored to reflect the peak periods of competition. Delaney and colleagues (2017a) suggest if a player is unable to cope with the peak match-play demands in a training context, the player may benefit from isolated training practices to improve work capacity in the specific area.
33 | P a g e F.4. SUMMARY
In order to work towards the goal of winning in rugby, coaches should strive to improve individual performance and reduce injury risk through the development of robust athletes. The collection of external load data can aid in this development by utilising different methods of interpretation: full match, temporal pattern and peak period analysis.
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