Variability in Soccer Performance

Performance variability can adopt different perspectives, it can refer to either the overall performance (technical or physical) on a match-to-match or even a half-by-half basis (Gregson et al., 2010; Rampinini et al., 2007; Weston, Drust, et al., 2011), or it can refer to a player’s ability to execute individual actions (Bartlett et al., 2007; Kellis, Katis, & Vrabas, 2006). Researchers have often assumed that inconsistency in performance, whether physical or

biomechanical has been a result of errors or noise within the sampling process, i.e. methodological issues or errors in testing equipment (Bartlett et al., 2007), however more recently research has demonstrated that there is a level of variability inherent within sporting performances (Bartlett et al., 2007;

Gregson et al., 2010), which can be affected by factors such as age and experience (Weston, Drust, et al., 2011), opposition and match variables (Mohr et al., 2003; Rampinini et al., 2007) and fatigue (Kellis et al., 2006).

The limited research available on movement variability suggests that variability is inherent within performance, irrespective of age or experience of the performer. This variability may be due to several factors, as suggested in dynamical systems theory, an experienced performer may introduce variability (minor changes in the movement patterns) in order to complete an identical task under differing circumstances with the maximal achievable result (Davids et al., 2003; Glazier & Davids, 2009; Newell, 1986). For example, passing a football may be affected by the location of the individual passing the ball, the individual receiving the ball, opposition player positioning and environmental and pitch conditions, thus changing the movement pattern in order to pass the ball from one player to another whilst taking the above factors into account. Alternatively, it has also been proposed that variability may be an involuntary measure to reduce repeated biomechanical loading on the same tissues which would potentially lead to overuse injuries over a period of time (Bartlett et al., 2007). In addition to overloading of tissues, biomechanical changes in both kicking and running can be caused by fatigue. Fatigue has been shown to induce changes in the strength, and therefore velocity, around the knee joint when performing

kicking actions and whilst running at higher intensities (Kellis et al., 2006;

Small et al., 2009). As previously discussed, fatigue can occur during a match, either temporarily or permanently, combining the biomechanical changes and the enduring technical requirements there is an increased injuries risk when fatigue occurs (Small et al., 2009). The occurrence of fatigue during a match may be one cause of the variability observed within performance and therefore must be taken into account when measuring variability.

Stability in sporting performance is essential for the reliable and accurate assessment of individual, unit and team performance (Hughes et al., 2001; Mytton, Archer, Gibson, & Thompson, 2014), stability is assumed when a level of consistency can be observed between multiple performances (Mytton et al., 2014). Comparative methods have been proposed to assess the stability between performances, including calculating coefficients of variation, 90 and 95% confidence limits and t tests (Atkinson & Nevill, 1998).

A separate method was proposed by Hughes et al. (2001), who suggested calculating cumulative means for successive performances and measuring the number of matches required before performance ‘stabilised’. Once the cumulative means levelled within 10, 5 or 1% error limits it was assumed that performance has become consistent, the lower limits of error achieved the more accurate the assessment of performance (Hughes et al., 2001). The cumulative method has previously been applied in a variety of racket sports and team sports including rugby union (Hughes et al., 2001) as well as lap times in both swimming and running (Mytton et al., 2014). Although limited research has been conducted using stability measures, there has been

general agreement in the findings. Typically performance parameters stabilise to within 10% limits of error after 2 or 3 matches, this includes parameters such as the number of shots per rally, the number of rallies per game and per match, the number of winners and errors per match (Hughes et al., 2001) and the lap times for 400 metre swimmers and 1500 metre runners (Mytton et al., 2014). However the rate of stabilisation is dependent upon the frequency of performance parameters, parameters which occur more often, or parameters which are ratios stabilise quicker than less frequent parameters (Hughes et al., 2001). This was displayed in the number of variables that rapidly stabilised to 10% error limits but took over 10 observations to stabilise to 5% or 1% error limits, alternatively other variables such as the total number of shots and rallies in a game do not stabilise to the lower error limits at all (see Figure 2.8). In contrast, Mytton et al. (2014) found all lap times for 400 metre swimmers and 1500 metre runners stabilised after 2 races, except the 1^st lap of a 1500 metre event, which took 16 observations to stabilise to 5% error limits. The 400m swimming events also observed lap times stabilised to 1% error limits before 10 observations whilst lap times for 1500 metre runners took up to 45 observations to stabilise to the same error limits. The rate of stabilisation appeared to be reflected by the coefficients of variation (CVs), 400 metre swimmers recorded CVs less than 2% across all lap times, whilst 1500 metre runners recorded CVs up to 5%, in particular for the first lap of a race (Mytton et al., 2014). Interestingly, this was the same variable that took the longest to stabilise using the cumulative mean method. No research has attempted to apply this method of stability assessment to match parameters in soccer;

neither has research found empirical evidence for the relationship between stability and reliability.

Figure 2.8: Example of stability profiles for the number of rallies per match in tennis (Adapted from Hughes, Evans and Wells, 2001)

Studies assessing performance variability, typically measured on a match-to-match basis through calculating coefficients of variation, have only measured the variability in physical performance and have not assessed the inherent variability of technical variables. Due to the vast distances covered during a match, total distance covered has been recorded to vary as little as 3% (Rampinini et al., 2007; Weston, Drust, et al., 2011), although it is

unclear how positional changes affect the variability of total distance. The coefficient of variation for high-intensity running distance have been measured at approximately 15% (Gregson et al., 2010; Rampinini et al., 2007; Weston, Drust, et al., 2011), although this is affected by the distance covered at high intensities with and without possession of the ball, with the high-intensity running distance with possession recording match-to-match variability up to 40% (Gregson et al., 2010). Total sprint distance has recorded match-to-match variability ≈30% (Gregson et al., 2010), although the number of sprints show greater variability ≈50% (Weston, Drust, et al., 2011). Whilst the research has begun to analyse the variability on a match-to-match basis, so far research has given little focus to positional or contextual effects on physical or technical performance.