Stability Results

The stability of match parameters depended on the parameter analysed and had minor effects according to playing position, all values reported are to within 10% of the mean unless otherwise stated. The results from the stability assessment indicated that total distance required the fewest number of matches (Figure 3.2) for analysis and stabilised to within both 10% and 5% of the mean after 2 matches (2.0±0.1 and 2.2±0.7 matches respectively), although required a greater number of matches to stabilise to within 1% of the mean (10.0±8.9 matches). Total high-intensity running distance stabilised within approximately 4 matches (4.4±3.5 matches), whilst the number of high-intensity actions and the recovery time between high-intensity actions stabilised within 5-6 matches (5.4±4.3 and 5.5±4.4 matches respectively).

Sprint distance and high-intensity running distance covered with and without possession required 7-8 matches to stabilise on average (7.0±4.9, 8.0±6.4 and 6.7±6.0 matches respectively). Centre backs required the highest number of games to stabilise for physical parameters compared to all other positions. Centre backs required 9±7 matches for sprint distance, 6±6 matches for high-intensity running distance, 11±10 matches for high-intensity running distance with possession and 7±6 matches for recovery time to stabilise. Data for technical variables recorded a greater number of matches required before stabilisation occurred (Figure 3.3). The number of passes performed stabilised within approximately 7 matches (7.2±6.2 matches) whilst the number of passes received stabilised after 8.8±8.1 matches, nevertheless the pass completion rate stabilised after a small number of

matches (3.2±2.0 matches). The number of possessions won per match stabilised after 8.0±6.0 matches, in contrast the number of possessions lost stabilised after 6.5±4.9 matches and the average number of touches per possession stabilised after 4.1±3.3 matches. Tackling variables recorded the highest number of matches required to stabilise within 10% of the mean our of all technical and physical variables, the number of tackles a player performed per match stabilised after 11.7±6.9 matches whilst the number of times a player was tackled per match stabilised after 11.8±7.8 matches.

Centre backs required the highest number of games to stabilise for the number of passes made and passes received (9±6 and 11±11 respectively) compared to all other positions. In contrast, wide midfielders and full backs required the lowest number of matches to stabilise for the number of passes made (6±6 and 6±3 passes respectively) and the number of passes received (8±6 and 7±5 passes respectively). Attackers in contrast required more matches to stabilise for defensive parameters including the number of tackles made (15±11 tackles), interceptions (12±12) and the number of possessions won (11±11). All variables, except total distance covered, the percentage of successful passes and the average number of touches per possession, required more than 10 matches to stabilise to within 5% of the mean and more than 20 matches to stabilise to within 1% of the mean. The highest number of matches required to stabilise to within 1% of the mean was 26.3±9.7 matches, which occurred for the number of tackles a player performs in a match, although sprint distance (25.3±17.0 matches) and high-intensity running distance WP (25.5±14.2 matches) also displayed high numbers of matches required for the highest level of accuracy.

Figure 3.2: The mean number of matches required for physical performance variables to stabilise to within 10% error limits for outfield positions.

Limited research has been conducted into the stability of match data.

Hughes et al. (2001) proposed a cumulative means method of investigating match stability although no research has subsequently used this or any method to establish the number of matches required to provide an accurate indication of performance. Hughes et al. (2001) concluded that stabilisation of performance indicators depended on the indicators being analysed and the standard of the match being played (i.e. international, national or non-professional). In racket sports, events which occurred more frequently such as shots and rallies in a game stabilised to within 10% of the mean quickest (<4 matches). These indicators would equate to passing events in soccer, which stabilised after 7±6 for the number of passes performed and 9±8 for the number of passes received. All physical performance parameters

0

matches to stabilise within 1% of the mean. Indicators which are also normalised such as average touches per possession (4±3) or percentage of successful passes (3±2) also stabilised to within 10% of the mean quicker, this was supported by Hughes et al. (2001) who suggested indicators such as the number of shots per rally stabilised quicker than non-normalised indicators, nevertheless, these factors took up to 16 matches to stabilise to within 1% of the mean.

Figure 3.3: The mean number of matches required for technical variables to stabilise to within 10% error limits for outfield positions.

The high numbers of matches required, and the very large variability (SDs) in the number of matches required, even to stabilise to within 10% of the mean, suggest that this method is less than ideal in quantifying the number of matches required for accurate assessment of performance. The method of applying a cumulative mean to the data is similar to adding an expanding

0 2 4 6 8 10 12 14 16

No. of Matches

CB's CM's F's WD's WM's

moving average filter to the data, as a result the data will eventually stabilise towards the arithmetic mean irrespective of the range of data or the number of matches analysed (Table 3.1). The data were highly susceptible to both minor and major changes in the performance variables, which then affected and effectively reset the stability profile, resulting in the requirement of a high number of matches to be analysed, especially for the highest level of accuracy. The nature of the data, and the multitude of interacting factors during a soccer game may lead to the data to be too variable for this method to be applicable and therefore appears unsuitable in this context. The method may have uses when analysing case studies, when analysing isolated movements, or sports that are manipulated less by external factors or are less inherently variable. Alternatively, a second proposal has recently been proposed by Gregson and collegues (2010) who applied the use of coefficients of variation to calculate the variability of performance parameters between matches, which may be a more appropriate calculation as the results are less susceptible to extremes in performance as CV calculations take into account the means and standard deviations of the data.

Table 3.1: The number of matches required to reach 10%, 5% and 1% error limits (mean±SD).

Number of matches

10% 5% 1%

Total Distance 2.0 ± 0.1 2.2 ± 0.7 10.0 ± 8.9

High-intensity Running Distance

4.4 ± 3.5 8.3 ± 6.0 19.5 ± 13.9

Sprint Distance 7.0 ± 4.9 10.9 ± 6.9 25.3 ± 17.0 High-intensity WP 8.0 ± 6.4 12.8 ± 10.2 25.5 ± 14.2 High-intensity WOP 6.7 ± 6.0 11.2 ± 8.5 20.1 ± 13.2 Number of HI actions 5.4 ± 4.3 8.8 ± 6.3 18.3 ± 14.0

Number of Passes 7.2 ± 6.2 12.1 ± 8.6 20.3 ± 12.9

Pass Success 3.2 ± 2.0 6.0 ± 4.7 15.7 ± 11.6

Number of Passes Received 8.8 ± 8.1 12.3 ± 10.5 16.1 ± 8.9 Number of Tackles 11.7 ± 6.9 14.6 ± 11.7 26.3 ± 9.7 Number of Times Tackled 11.8 ± 7.8 16.5 ± 11.6 22.4 ± 16.4 Number of Possessions won 8.0 ± 6.0 12.2 ± 7.8 22.7 ± 10.8 Number of Possessions lost 6.5 ± 4.9 10.5 ± 7.2 22.2 ± 14.2

Average Touches Per Possession

4.1 ± 3.3 7.9 ± 6.3 16.1 ± 11.1