Performance Assessments During Soccer Matches

Numerous methods have been developed over the last few decades which enable a soccer player’s performance to be assessed during a competitive match. Some of these methods include time-motion analysis, global positioning systems (GPS), semi-automated

computerised systems; which have enabled a physiological profile to be developed across standards of play (Lago-Penas et al., 2011; Andersson et al., 2010; Ayllon et al., 2010; Bradley et al., 2010; Rampinini et al., 2009; Mohr et al., 2008; Mohr et al., 2003), male and female sports (Goto et al., 2013; Silva et al., 2013; Dwyer and Gabbett, 2012; Harley et al., 2011) with special references to playing position (Wehbe et al., 2014; Andrzejewski et al., 2013; Di Mascio and Bradley, 2013; Vigne et al., 2013; Andrzejewski et al 2012; Gomez- Piriz et al., 2011; Lago-Penas et al., 2011; Andersson et al., 2010; Ayllon et al., 2010;

Bradley et al., 2010; Dellal et al., 2010a; Gregson et al., 2010; Mohr et al., 2008; Mohr et al., 2003), and senior and youth levels (Wehbe et al., 2014; Andrzejewski et al., 2013; Di Mascio and Bradley, 2013; Goto et al., 2013; Silva et al., 2013; Vigne et al., 2013; Andrzejewski et al 2012; Dwyer and Gabbett, 2012; Gomez-Piriz et al., 2011; Harley et al., 2011; Lago-Penas et al., 2011; Andersson et al., 2010; Ayllon et al., 2010; Bradley et al., 2010; Dellal et al., 2010a; Gregson et al., 2010; Rampinini et al., 2009; Mohr et al., 2008; Mohr et al., 2003).

The common key variables assessed throughout the literature include the total distance covered, and the distance covered whilst walking, jogging, low-intensity running, moderate intensity running, high-intensity running and sprinting; with each category containing a specific speed zone. However, the speed zones across the literature are not normalised and remain inconsistent which hinders the value of normative data and makes it very difficult to compare and contrast data. Table 2.5 highlights that on average, Prozone contain greater

sprinting speed zones than 80% of other methods (>25.2 km/h vs. 18- 24 km/h), suggesting research data using this method to assess match performance may be an under estimation of actual sprint performance.

Table 2.5. Showing the speed zone categories from senior soccer match research (male and female) Research Walk (km/h) Jog (km/h) Low-Speed Run (km/h) Moderate- Speed Run (km/h) High- Speed Run (km/h) Sprint (km/h) Di Mascio and Bradley (2013); Bradley et al. (2010); Gregson et al. (2010) Prozone 0.7 -7.1 7.2 -14.3 14.4 -19.7 19.8 -25.1 > 25.2 Andrzejewski et al. (2013); Andrzejewski et al. (2012); Lago- Penas et al. (2011) Amisco Pro 0 -11 11 -14 14 -19 19 -23 > 23

Dellal et al. (2010a) Amisco Pro

21 -24 > 24

Silva et al. (2013); Mohr et al. (2003) Time motion analysis

0.1 -6 6 -8 8 -12 12 -15 15 -18 18 -30

Andersson et al. (2010); Mohr et al. (2008)

Time motion analysis

0.1 -6 6.1 -8 8.1 -12 12.1 -15 15.1 -18 18 -25

Vigne et al. (2013) Time motion analysis

< 5 5.1 -13 13 -16 16.1 -19 > 19 Rampinini et al. (2009) SICS > 14 > 19 Owen et al. (2014) GPS (Catapult, 5 Hz) 0 -7.2 7.3 -14.3 14.4 -21.5 21.6 -25.2 > 25.3 Souglis et al. (2013) GPS (Garmin) 0 -7.15 7.16 - 11.39 11.40 - 13.79 13.80 - 19.31 19.32 - 24.14 > 24.15 Wehbe et al. (2014) GPS (GPSports, 5 Hz) 0.7 -7.1 7.2 -14.3 14.4 -19.7 19.8 -25.1 > 25.1

Speed zones have also differed between studies even though they used the same technology analysis software. The use of Amisco Pro in Dellal et al. (2010a) showed speed zones of 21- 24 km/h and >24 km/h for high-intensity running and sprinting, respectively; whilst other multiple investigations show different high-intensity speed zones 19-23 km/h and sprint speed zones >23 km/h (Andrezejewski et al., 2013; Andrezejewski et al., 2012; Lago-Penas et al., 2011). It is important to note that the literature tends to report absolute values from the speed zones rather than normalising results, which may not reflect each player’s individual work rate capabilities due to differences in sprint ability. For example, if a slow individual sprints to their full maximal speed, this may not be actually fast enough to register as a ‘sprint’ in the speed zones created. On the other hand, an individual who can sprint fast will find it easier to produce work rate values in the higher speed zone categories. In spite of this, absolute values allow us to compare players and other research with each other; normalising speed categories to each player’s sprint ability would make these comparisons difficult and could allow a poorly conditioned player to appear considerably better than a greater

conditioned player.

In addition, the use of different technological equipment and analysis software also limits the reliability between research data. Comparing soccer match performance assessment data from time-motion analysis (Silva et al., 2013; Vigne et al., 2013; Andersson et al., 2010; Mohr et al., 2008; Mohr et al., 2003; Helgerud et al., 2001), semi-automative computer systems (Andrezejewski et al., 2013; Di Mascio and Bradley, 2013; Andreezejewski et al., 2012; Lago-Penas et al., 2011; Bradley et al., 2010; Dellal et al., 2010a; Gregson et al., 2010; Rampinini et al., 2009) and GPS (Owen et al., 2014; Wehbe et al., 2014) methods may not be valid.

Due to the nature of the sport, research has reported coefficient of variation and ICC values for all match activity assessments with considerable large ranges (r=0.898-0.980, CV 1-13%) (Silva et al., 2013; Castagna et al., 2010; Castagna et al., 2003; Mohr et al., 2003; Helgerud et al., 2001). This large variability between the matches could be explained by the different opposition the team plays, different tactics performed on that particular day, different set of players in the starting 11, score of the game, the pressure on the outcome of the game.

Further inconsistencies between research data may exist due to different models of

equipment, such as the GPS manufacturer (e.g. GPSports vs. Catapult), and various sampling frequencies available from each GPS merchant. Each GPS unit is created at a specific

sampling frequency: the speed at which a GPS unit can collect movement information. Currently there are GPS units manufactured with sampling rates of 1-, 5-, 10- and 15-Hz with evidence that the lower sampling rates (1-Hz) provide a poor degree of inter-reliability when measuring athletic movement than the greater sampling rates (Johnston et al., 2012; Coutts et al., 2010). These assumptions could be made particularly for high intense activities such as high speed running and sprinting, particularly over short distances which have suggested to increase the level of error with GPS methodology (Johnston et al., 2012; Portas et al., 2010; Duffield et al., 2010). For example, if an individual sprints 5- metres in less than 1- second this may not be recorded in the lower sampling GPS units (1-Hz). Moreover, a female soccer match consists of 1326-1379 changes in activity and these changes happen at such a high intensity that a low sampling frequency (1-Hz), which samples just once per second, may miss a great amount of these changes in activity (Mohr et al., 2008). Whereas, a high sampling frequency GPS unit (5-15- Hz) is more likely to capture these changes in activity due to sampling >5 times per second. Portas et al. (2010) stated 5-Hz sampling frequency provides a lower range of error at higher speed intensities when compared to 1-Hz GPS

frequencies (SEE: 2.2-4.4% vs. 1.8-6.8%) which also supports the findings that 1-Hz

underestimates soccer-specific performance activity. Thus, it may be possible to suggest that a greater sampling frequency GPS unit (>1-Hz) would be more beneficial to use when assessing multiple interchangeable- and high- speed intensities involved in soccer.

GPS units have been used across numerous research studies in order to detect physical demands and movements involved in sports such as rugby league (Austin and Kelly, 2014; Austin and Kelly, 2013; McLellan and Lovell, 2013; McLellan et al., 2011), rugby union (Suarez-Arrones et al., 2014; Cunniffe et al., 2009), long distance running (Nielsen et al., 2013), field hockey (Dwyer and Gabbett, 2012; Gabbett, 2010), futsal (Dogramaci et al., 2011), Australian rules football (Dwyer and Gabbett, 2012; Young et al., 2012), and other court and field-based sports (Vickery et al., 2014) with limited studies examining competitive soccer matches at senior level (Wehbe et al., 2014; Harley et al., 2011; Ayllon et al., 2010). These research studies investigating soccer match performance were published on Australian, Spanish and English male soccer teams at the elite level (Wehbe et al., 2014; Harley et al., 2011; Ayllon et al., 2010), respectively. These findings not only highlight the lack of published research on soccer match performance within England using GPS but it also identifies, at this point in time, there are no match assessments of female soccer using GPS methods.