A comparison of the physiological demands of rugby union match-play when determined by absolute (ABS) or individual (IND) velocity bands in Global Positioning System (GPS) analysis

Since turning professional in 1995 there have been considerable advances in the research on the demands of rugby union, largely using Global Positioning System (GPS) analysis over the last 10 years. A systematic review on the use of GPS, particularly the setting of absolute (ABS) and individual (IND) velocity bands in field based, intermittent, high-intensity (HI) team sports was undertaken. From 3669 records identified, 38 studies were included for qualitative analysis. Little agreement on the definition of movement intensities within team sports was found, only three papers, all on rugby union, had used IND bands, with only one comparing ABS and IND methods. Thus, the aim of this study was to determine if there is a difference in the demands within positions when comparing ABS and IND methods for GPS analysis and if these differences are significantly different between the forward and back positional groups. A total of 214 data files were recorded from 26 players in 17 matches of the 2015/2016 Scottish BT Premiership. ABS velocity zones 1-7 were set at 1) 0-6, 2) 6.1-11, 3) 11.1-15, 4) 15.1-18, 5) 18.1-21, 6) 21.1-15 and 7) 25.1-40km.h-1 while IND zones 1-7 were 1) <20, 2) 20-40, 3) 40-50, 4) 50-70, 5) 70-80, 6) 80-95 and 7) 95-100% of player’s individually determined maximum velocity (Vmax). A 40m sprint test measured Vmax using OptaPro S4 10 Hz (catapult, Australia) GPS units to derive IND bands. The same GPS units were worn during matches. GPS outputs analysed were % distance, % time, high intensity efforts (HIEs) over 18.1 km.h-1 / 70% max velocity and repeated high intensity efforts (RHIEs) which consists of three HIEs in 21secs. General linear model (GLM) analysis identified a significant difference in the measurement of % total distance covered, between the ABS and IND methods in all zones for forwards (p<0.05) and backs (p<0.05). This difference was also significant between forwards and backs in zones 1, shown as mean difference ± standard deviation (3.7±0.7%), 6 (1.2±0.4%) and 7 (1.0±0.0%) respectively (p<0.05). Percentage time estimations were significantly different between ABS and IND analysis within forwards in zones 1 (1.7±1.7%), 2 (-2.9±1.3%), 3 (1.9±0.8%), 4 (-1.4±0.8%) and 5 (0.2±0.4%), and within backs in zones 1 (-10±1.5%), 2 (-1.2±1.1%), 3 (1.8±0.9%) and 5 (0.6±0.5%) (p<0.05). The difference between groups was significant in zones 1, 2, 4 and 5 (p<0.05). The number of HIEs was significantly different between forwards and backs in zones 6 (6±2) and 7 (3±2). RHIEs were significantly different between ABS and IND for forwards (1±2, p<0.05) although not between groups. Until more research on the differences in ABS and IND methods is carried out, then neither can be deemed a criterion method. In conclusion, there are significant differences between the ABS and IND methods of GPS analysis of the physical demands of rugby union, which must be considered when used to inform training load and recovery to improve performance and reduce injuries.

Assessments of training load in elite youth soccer

One of the most popular sports globally, soccer has seen a rise in the demands of the game over recent years. An increase in intensity and playing demands, coupled with growing social and economic pressures on soccer players means that optimal preparation is of paramount importance. Recent research has found the modern game, depending on positional role, to consist of approximately 60% more sprint distance in the English Premier League, which was also found to be the case for frequency and success of discrete technical actions (Bush et al., 2015). As a result, the focus on soccer training and player preparedness is becoming more prevalent in scientific research. By designing the appropriate training load, and thus periodization strategies, the aim is to achieve peak fitness in the most efficient way, whilst minimising the risk of injury and illness. Traditionally, training intensity has been based on heart rate responses, however, the emergence of tracking microtechnology such as global positioning system (GPS) and inertial sensors are now able to further quantify biomechanical load as well as physiological stress. Detailed pictures of internal and external loading indices such as these then combine to produce a more holistic view of training load experience by the player during typical drills and phases of training in soccer. The premise of this research is to gain greater understanding of the physical demands of common training methodologies in elite soccer to support optimal match performance. The coaching process may then benefit from being able to prescribe the most effective training to support these. The first experimental chapter in this thesis began by quantify gross training loads of the pre-season and in-season phases in soccer. A broader picture of the training loads inherent in these distinct phases brought more detail as to the type and extent of external loading experienced by soccer players at these times, and how the inclusion of match play influences weekly training rhythms. Training volume (total distance) was found to be high at the start compared to the end of pre-season (37 kilometres and 28 kilometres), where high cardiovascular loads were attained as part of the conditioning focus. This progressed transiently, however, to involve higher-speed, acceleration and change-of-direction stimuli at the end of pre-season compared to the start and to that in-season (1.18 kilometres, 0.70 kilometres and 0.42 kilometres high-intensity running; with 37, 25 and 23 accelerations >3m/s2 respectively) . The decrease in volume and increase in maximal anaerobic activity was evident in the training focus as friendly matches were introduced before the competitive season. The influence of match-play as being a large physical dose in the training week may then determine the change in weekly periodisation and how resulting training loads applied and tapered, if necessary. The focus of research was then directed more specifically to the most common mode of training in soccer, that also featured regularly in the pre-season period in the present study, small-sided games (SSG). The subsequent studies examined numerous manipulations of this specific form of soccer conditioning, such as player numbers as well as absolute and relative playing space available. In contrast to some previous literature, changing the number of players did not seem to influence training responses significantly, although playing format in the possession style brought about larger effects for heart rate (89.9%HRmax) and average velocity (7.6km/h-1). However, the following studies (Chapters 5, 6 and 7) revealed a greater influence of relative playing space available to players in SSG. The larger area at their disposal brought about greater aerobic responses (~90%HRmax), by allowing higher average and peak velocities (>25km/h-1), as well as greater distance acceleration behaviour at greater thresholds (>2.8m/s2). Furthermore, the data points towards space as being a large determinant in strategy of the player in small-sided games (SSG), subsequently shaping their movement behaviour and resulting physical responses. For example, higher average velocities in a possession format (8km/h-1) reflects higher work rate and heart rate load but makes achieving significant neuromuscular accelerations at a high level difficult given higher starting velocities prior to the most intense accelerations (4.2km/h-1). By altering space available and even through intentional numerical imbalances in team numbers, it may be easier for coaches to achieve the desired stimulus for the session or individual player, whether that is for aerobic and neuromuscular conditioning. Large effects were found for heart rate being higher in the underloaded team (85-90%HRmax) compared to the team with more players (80-85%HRmax) as well as for RPE (5AU versus 7AU). This was also apparent for meterage and therefore average velocity. It would also seem neuromuscular load through high acceleration and deceleration efforts were more pronounced with less numbers (given the need to press and close down opponents, and in a larger area relative to the number of players on the underloaded team. The peak accelerations and deceleration achieved was also higher when playing with less players (3-6.2m/s2 and 3-6.1m/s2) Having detailed ways in which to reach desired physical loading responses in common small training formats, Chapter 8 compared SSG to larger 9v9 formats with full-size 11v11 friendly matches. This enabled absolute and relative comparisons to be made and to understand the extent to which smaller training formats are able to replicate the required movements to be successful in competition. In relative terms, it was revealed that relative acceleration distance and Player Load were higher in smaller 4v4 games than match-play (1.1m.min-1 and 0.3m.min-1 >3m/s2; 16.9AU versus 12AU). Although the smallest format did not replicate the high-velocity demands of matches, the results confirmed their efficacy in providing significant neuromuscular load during the training week, which may then be supplemented by high-intensity interval running in order to gain exposure to more maximal speed work. In summary, the data presented provide valuable information from GPS and inertial sensor microtechnology which may then be used to understand training better to manipulate types of load according to physical conditioning objectives. For example, a library of resources to direct planning of drills of varying cardiovascular, neuromuscular and perceptual load can be created to give more confidence in session outcomes. Combining external and internal load data of common soccer training drills, and their application across different phases and training objectives may give coaches a powerful tool to plan and periodize training.