Physical Performance

The physical demands of soccer match-play have been widely researched (Andersson et al., 2010; Barnes, Archer, Hogg, Bush, & Bradley, 2014;

Bradley et al., 2009; Bradley, Carling, et al., 2013; Bradley, Dellal, et al., 2014; Di Salvo et al., 2010, 2013; Krustrup, Mohr, Ellingsgaard, & Bangsbo, 2005), position-specific requirements of match-play (Bloomfield, Polman, &

O’Donoghue, 2007; Di Salvo et al., 2007, 2010) along with the effects of fatigue on performance (Bradley & Noakes, 2013; Carling, 2013; Carling et al., 2008; Carling & Dupont, 2011; Mohr et al., 2003, 2012; Weston, Batterham, et al., 2011; Weston, Castagna, Impellizzeri, Rampinini, & Abt, 2007). It is widely accepted that players cover a minimum of 10 km during a match, irrespective of position, although players can cover up to 13-14 km (Barros et al., 2007; Bradley, Carling, et al., 2013; Di Salvo et al., 2007; Drust et al., 2007). The majority of the total distance is covered at low intensities,

≈80% is performed when standing, walking, jogging or at low running intensities (Bradley, Carling, et al., 2013; Bradley, Dellal, et al., 2014; Dellal et al., 2011; Di Salvo et al., 2013). Information on the total distance covered during soccer matches provides an understanding of the total physical workload, nevertheless research has identified that work at low intensities has little to no impact on match outcome. In contrast, the physical work completed at higher intensities and whilst sprinting has greater impact on match outcome (Akenhead, Hayes, Thompson, & French, 2013; Andersson et al., 2010; Bradley et al., 2009; Bradley, Carling, et al., 2013; Di Salvo et al., 2009; Faude, Koch, & Meyer, 2012). Although academic researchers agree on the proportions of low-intensity vs. high-intensity work completed

during matches, the data must be handled with caution as different research papers and analytical software use differing speed thresholds to measure movements (Bradley et al., 2009; Bradley, Carling, et al., 2013; Dellal et al., 2011; Di Salvo et al., 2007; Mackenzie & Cushion, 2013). Although the differences between some of these speed thresholds are minor, over the course of a match, or multiple matches, there will be measureable effect on the distances covered in different speed categories. More importantly variations in speed categories will affect the ability to compare and contrast findings between different articles and the respective participant groups (Mackenzie & Cushion, 2013). In addition, more clarification and greater universal acceptance is required for the classification of speed thresholds (Table 2.1 and 2.2), for example some articles classify high-intensity to be

>14 km^.h^-1, thus including classifications of medium-speed running, high-speed running and sprinting (Andersson, Raastad, et al., 2008; Bradley et al., 2009; Bradley, Carling, et al., 2011; Bradley & Noakes, 2013; Carling &

Dupont, 2011; Krustrup & Bangsbo, 2001; Mohr et al., 2003; Rampinini et al., 2007), whilst others use a threshold at 19 km^.h^-1 (Barnes et al., 2014; Di Salvo et al., 2007, 2009, 2013; Gregson et al., 2010; Weston, Batterham, et al., 2011; Weston et al., 2007) and other articles choose not to categorise into high-intensity categories for methodological purposes (Barros et al., 2007; Bradley, Dellal, et al., 2014). There have been proposals to adopt more individualised speed thresholds for each player based on the transition between moderate to high-intensity actions around the second ventilatory threshold (VT2; Abt & Lovell, 2009). Nevertheless researchers have not widely adopted this strategy due to the complexity, time required and lack of

access to players in order to measure ventilatory thresholds. As a result some research articles suggest players cover as much as ≈2500 m (20-25%

of total distance) at high-intensity (Bradley et al., 2009; Bradley, Carling, et al., 2011), although the majority of research indicates players cover ≈1000 m (5-11% of total distance) at running speeds >19 km^.h^-1 (Barros et al., 2007;

Bradley et al., 2009; Bradley, Carling, et al., 2011; Bradley, Dellal, et al., 2014; Carling et al., 2012; Weston et al., 2007), although these results are also dependant upon the data collection methods (Akenhead et al., 2013;

Portas et al., 2010; Randers et al., 2010; Varley et al., 2012).

Table 2.1: Summary of Physical Match performance findings.

Table 2.2: Summary of Physical Match performance findings continued

In extension to the distances covered at both high-intensity and whilst sprinting, the acceleration profiles in attaining these high speeds has been identified as a key factor within match performance and training, particularly in identifying and minimising injury risk (Daly, 2013; Opar et al., 2012;

Petersen, Thorborg, Nielsen, Budtz-Jørgensen, & Hölmich, 2011; Small et al., 2009). Research groups have noted that players cover, on average, 20-40 sprints per game (Andersson, Ekblom, et al., 2008; Ingebrigtsen, Dalen, Hjelde, Drust, & Wisløff, 2015), averaging less than 10 m per sprint, thus suggesting short, sharp sprint actions (Barnes et al., 2014). However, measuring sprint distances only measures the physiological requirements once players attain the speed threshold and does not take into account the physical work they must complete to attain the speeds, nor does it take into account the sudden, sharp movements which causes velocity changes but without causing changes in speed thresholds (Castellano & Casamichana, 2013). In contrast to the low number of sprints, players can perform over 100 accelerations, or up to 8 times the number of sprints in a game (Bradley, Di Mascio, Peart, Olsen, & Sheldon, 2010; Ingebrigtsen et al., 2015; Varley, Gabbett, & Aughey, 2014). This high number of accelerations can result in players covering over 1000 m and spend over 500 seconds accelerating during matches, with a similar profile when players decelerate (Akenhead et al., 2013; Osgnach, Poser, Bernardini, Rinaldo, & Di Prampero, 2010). The majority of accelerations by players (>90%) begin from low running speeds (Bradley et al., 2010; Varley & Aughey, 2013; Varley et al., 2014), whilst a high proportion of accelerations do not result in players breaking the high-speed running thresholds (Varley et al., 2014). Players in wide positions (full

backs and wide midfielders) demonstrate a higher number of accelerations compared to other positions whilst attackers demonstrate the highest number of hard accelerations (>3 m^.s^-2), (Ingebrigtsen et al., 2015; Varley & Aughey, 2013; Wehbe, Hartwig, & Duncan, 2014). In addition, the recovery time between high-intensity bouts is lowest in soccer compared to other football codes (Varley et al., 2014), with players often having moderate (30-120 seconds) to long (>120 seconds) recovery times between high-intensity bouts (Bradley et al., 2010; Varley et al., 2014). These findings show it is important to take into account the rapid and explosive movements conducted by soccer players when analysing the physical output during soccer matches.

Without taking these factors into account a true representation of physical output is not accounted for, nor is it possible to design training programmes which replicate match performance to maximise physical preparation of players. Negating this information could lead to ineffective preparation and training programmes as well as an increased injury risk for players.

The information on sprints needs to be analysed with caution. As with the speed thresholds used to calculate distances covered during a match, researchers use differing acceleration thresholds when analysing changes in speed. Some researchers have classified accelerations into low (1-2 m^.s^-2), moderate (2-3 m^.s^-2) and high (>3 m^.s^-2), (Akenhead et al., 2013; Hodgson, Akenhead, & Thomas, 2014), whilst other research groups have used thresholds of medium (2.5-4 m^.s^-2) and high (>4 m^.s^-2), (Bradley et al., 2010).

These differences in classifications add difficulties when analysing and comparing data, with the importance of the data and the practical findings, it is essential that researchers begin to use common thresholds. In addition to

these issues, the method of collecting data needs to be accounted for. Some research articles have used 1Hz (Buchheit, Mendez-Villanueva, Simpson, &

Bourdon, 2010; Castagna, Manzi, Impellizzeri, Weston, & Barbero Alvarez, 2010; Mendez-Villanueva, Buchheit, Simpson, Peltola, & Bourdon, 2011) and 5Hz (Varley & Aughey, 2013) GPS devices to analyse accelerations. This is compared to other research that have used 10Hz GPS devices (Akenhead et al., 2013; Hodgson et al., 2014), automated motion tracking devices which typically record at 25Hz (Bradley et al., 2010; Osgnach et al., 2010) and accelerometers measuring at 100Hz (Castellano & Casamichana, 2013). The 1Hz and 5 Hz GPS systems have been shown to be less sensitive to sports movements (Barbero-Alvarez et al., 2010; Coutts & Duffield, 2010; Harley et al., 2011; Randers et al., 2010; Varley et al., 2012) and therefore may not accurately measure changes in velocities. In addition, the variability of GPS devices can affect the data recorded depending upon the number of satellites available during capture and location of capture (indoors vs. outdoors and obstructions) and must be taken into account (Portas et al., 2010; Randers et al., 2010; Varley et al., 2012).