European Journal of Sport Science The effects of ...

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The effects of situational variables on distance covered at various speeds in elite soccer Carlos Lago a; Luis Casais a; Eduardo Dominguez a; Jaime Sampaio b a Facultad de CC da Educacion e o Deporte, Universidad de Vigo, Pontevedra, Spain b Department of Sport Sciences, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal Online publication date: 08 February 2010

To cite this Article Lago, Carlos, Casais, Luis, Dominguez, Eduardo and Sampaio, Jaime(2010) 'The effects of situational

variables on distance covered at various speeds in elite soccer', European Journal of Sport Science, 10: 2, 103 — 109 To link to this Article: DOI: 10.1080/17461390903273994 URL: http://dx.doi.org/10.1080/17461390903273994

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European Journal of Sport Science, March 2010; 10(2): 103109

ORIGINAL ARTICLE

The effects of situational variables on distance covered at various speeds in elite soccer

CARLOS LAGO1, LUIS CASAIS1, EDUARDO DOMINGUEZ1, & JAIME SAMPAIO2 Facultad de CC da Educacion e o Deporte, Universidad de Vigo, Pontevedra, Spain, and 2Department of Sport Sciences, University of Tra´s-os-Montes e Alto Douro, Vila Real, Portugal

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1

Abstract The aim of this study was to examine the effect of match location, quality of opposition, and match status on distance covered at various speeds in elite soccer. Twenty-seven Spanish Premier League matches played by a professional soccer team were monitored in the 20052006 season using a multiple-camera match analysis system. The dependent variables were the distance covered by players at different intensities. Data were analysed using a linear regression analysis with three independent variables: match status (i.e. whether the team was winning, losing or drawing), match location (i.e. playing at home or away), and quality of the opponents (strong or weak). The top-class players performed less high-intensity activity (19.1 km × h 1) when winning than when they losing, but more distance was covered by walking and jogging when winning. For each minute winning, the distance covered at submaximal or maximal intensities decreased by 1 m (P B0.05) compared with each minute losing. For each minute winning, the distance covered by walking and jogging increased by 2.1 m (P B0.05) compared with each minute losing. The home teams covered a greater distance than away teams during low-intensity activity (B14.1 km × h 1) (P B0.01). Finally, the better the quality of the opponent, the higher the distance covered by walking and jogging. Our findings emphasize the need for match analysts and coaches to consider the independent and interactive effects of match location, quality of opposition, and match status during assessment of the physical component of football performance.

Keywords: Physical performance, contextual factors, work rate, soccer

Introduction The physiological demands of soccer have been studied intensively in male players. Timemotion analysis research has demonstrated that elite players typically cover distances of 914 km during a match (Barros et al., 2007; Di Salvo et al., 2007; Mohr, Krustrup, & Bangsbo, 2005; Rampinini, Coutts, Castagna, Sassi, & Impellizzeri, 2007). The type of exercise in soccer is intermittent, with a change in activity every 46 s (Bangsbo, 1994; Mohr et al., 2005). Thus, an international top-class player performs approximately 1330 activities during a match, including about 220 runs at high speed (Barros et al., 2007; Di Salvo et al., 2007; Mohr et al., 2005; Rampinini et al., 2007). Playing a high-level match can elicit up to 75% of maximal oxygen uptake, with the anaerobic system contributing greatly during intense periods (Bangsbo, 1994; Mohr et al.,

2005). Several studies have shown decrements in physiological performance during matches. In particular, it has been suggested that high-intensity running and sprinting decrease from the first to the second half, probably due to physical fatigue (Barros et al., 2007; Mohr et al., 2005; Rampinini et al., 2007; Rampinini, Impellizzeri, Castagna, Coutts, & Wisløff, 2009). Given that soccer is dominated by strategic factors, it is reasonable to suggest that situational variables may somehow influence the teams’ and players’ activities. Empirical evidence suggests that the situational variables of match location (i.e. playing at home or away), match status (i.e. whether the team was winning, losing or drawing), and the quality of the opposition (strong or weak) are the most important factors for soccer performances (James, Mellalieu, & Holley, 2002; Jones, James, &

Correspondence: C. Lago, Facultad de CC da Educacion e o Deporte, Universidad de Vigo, Av. Buenos Aires s/n, 36002 Pontevedra, Spain. E-mail: [email protected] ISSN 1746-1391 print/ISSN 1536-7290 online # 2010 European College of Sport Science DOI: 10.1080/17461390903273994


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Mellalieu, 2004; Lago & Martin, 2007; Taylor, Mellalieu, James, & Shearer, 2008; Tucker, Mellalieu, James, & Taylor, 2005). According to Bloomfield and colleagues (Bloomfield, Polman, & O’Donoghue, 2005a) and Taylor et al. (2008), the importance of these situational factors is reflected in changes in team strategies in response to score-line. However, despite the importance of accounting for match location, quality of opposition, and match status during the assessment of tactical aspects of soccer performance (Carling, Williams, & Reilly, 2005; Taylor et al., 2008), very few studies have examined the relationships between physical performance during the match and these situational variables (Bloomfield, Polman, & O’Donoghue, 2005b; Di Salvo, Gregson, Atkinson, Tordoff, & Drust, 2009; O’Donoghue & Tenga, 2001; Rampinini et al., 2009; Shaw & O’Donoghue, 2004). Moreover, the effects of these situational factors on distance covered at various speeds in elite soccer are unclear, given that previous research used small sample sizes and analysed situational variables independently, thereby neglecting to account for the complex and dynamic nature of soccer performance (McGarry & Franks, 2003; Reed & O’Donoghue, 2005). Given these shortcomings, the aim of the present study was to examine the independent and interactive effects of match location, quality of the opposition, and match status on the distance covered at various speeds in elite soccer. Methods Twenty-seven Spanish Premier League matches played by a professional soccer team were monitored during the 20052006 season using a multiplecamera match analysis system (Amisco Pro† , version 1.0.2, Nice, France). The movements of all 10 outfield players (goalkeepers were excluded) of the sampled team were observed throughout matches by means of eight stable, synchronized cameras positioned at the top of the stadium (sampling frequency 25 Hz). Only data for those players completing entire matches (i.e. 90 min) were included in the analysis. A total of 182 individual items of data from 19 players were used in the study (see Table I). Signals and angles obtained by the encoders were sequentially converted into digital data and recorded on six computers for post-match analysis. Zubillaga and colleagues (Zubillaga, Gorospe, Hernandez, & Blanco, 2009) have recently evaluated the reliability and validity of Amisco Pro† for quantifying displacement velocities during match-related activities relative to data obtained using timing gates. [For previous applications of the Amisco system, see Di Salvo et al. (2007; Di Salvo, Benito, Calderon Montero, Di Salvo, & Pigozzi, 2008).] The sampled

Table I. Summary statistics for the observed outfield players

Player Player Player Player Player Player Player Player Player Player Player Player Player Player Player Player Player Player Player Player

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Positional role

Number of matches observed

EM EM CM ED CD F ED F F CD F CM CM EM EM CD ED ED CD

5 3 13 19 13 5 4 8 11 21 3 20 12 2 7 6 10 8 12

Note: CDcentral defender, EDexternal defender, CM central midfield player, EMexternal midfield player, F forward.

match results (17 home and 10 away matches) consisted of 7 wins, 7 draws and 13 losses, with 25 goals scored and 38 conceded by the sampled team. The team’s overall record for the sampled season was 10 wins, 11 draws, 17 losses, 36 goals scored, and 56 goals conceded. We only analysed 27 matches because not all the teams of the Spanish Premier League have the Amisco system. Moreover, the sampled team did not use the Amisco system until the third home match. Voluntary informed consent was obtained from all players before the study began. Ethics approval for all experimental procedures was granted by the University Human Research Ethics Committee. Written permission from the sampled club was received to record and analyse data. From the stored data, the distance covered, the time spent in five different intensity categories, and the frequency of occurrence of each activity for players in different positions were obtained by specially developed software (Athletic Mode Amisco Pro† , Nice, France). Match analyses were performed, distinguishing between the following five intensity categories (Di Salvo et al., 2007, 2008): 011 km × h1 (standing, walking, jogging); 11.114.0 km × h1 (low-speed running); 14.119.0 km × h1 (moderatespeed running); 19.123.0 km × h 1 (high-speed running); 23 km × h1 (sprinting). Outfield players in this investigation were assigned to one of five positional groups according to their activity on the pitch: central defenders (n 52), external defenders (n 41), central midfield players (n 45), external midfield players (n 17), and forwards (n 27).

Situational variables in elite soccer


Data analysis To examine how much unique variance in the dependent variable was explained by each independent variable, a standard multiple regression was used. When estimating the regression models, we found no evidence of heteroscedasticity in residuals or multicollinearity among regressors. Moreover, the RESET test (Ramsey, 1969) did not reveal specification problems. The detection of heteroscedasticity was done according to White’s test. White’s test is used to establish whether the residual variance of a variable in a regression model is constant. To test for constant variance, one regresses the squared residuals from a regression model onto the regressors, the cross-products of the regressors, and the squared regressors. One then inspects the R2-value. Multicollinearity was checked using Klein’s rule, which states that serious multicollinearity is present if the R2-value of the regression of a predictor variable on other predictor variables is higher than the R2-value of the original regression. When interpreting the statistical results, positive or negative coefficients indicate a greater or lower propensity to increase/decrease distance covered by players. The independent variables were the situation variables: 1. Match status, measured as the total number of minutes observed in each score-line state (winning, losing or draw). The comparison group is losing. This means that the panel match status in the regression model presents two coefficients from the comparison of drawing and losing and from the comparison of winning and losing. 2. Match location, a dummy variable indicating if the game was played at home or away. Playing at home is the comparison group. 3. Quality of opposition, the difference in the final ranking (in the current season) of the considered team and the opponent, i.e. Quality of opposition PAPB where PA is the final ranking of the sampled team and PB is the final ranking of the opponent. Statistical analysis was performed using STATA for Windows, version 10.0 (Stata Corp., Texas, USA). For all analyses, statistical significance was set at P B0.05.

Results The distances covered at different work intensities by players of different positional roles are presented in Table II. The effects of match location, quality of the opposition, and match status on distance

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covered at various speeds in elite soccer are displayed in Table III. Total distance covered The total distance covered was explained by match location (PB0.01) and quality of the opponent (P B0.05). In essence, playing away reduced the total distance covered by 262 m compared with playing at home. Players covered a greater distance when playing against better ranked teams. Each position difference in the end-of-season ranking between opposing teams increased the total distance covered by 15 m. When all the independent variables were zero that is, the team was losing throughout a match played at home the distance covered by players was 10,719 m. Distances covered at submaximal or maximal intensities The total distance covered at submaximal or maximal intensities (19.1 km × h1) was explained by match status. For each minute winning, the distance covered at maximal intensity decreased by 0.95 m (P B0.05) compared with each minute losing. For example, if the team was losing for the whole 90 min, the predicted distance covered at maximal intensity would be 86 m higher than if winning throughout the match. At submaximal intensity, for each minute winning the distance covered decreased by 1.1 m compared with each minute losing. When all the independent variables were equal to zero, the distance covered by players was 302 m (maximal intensity) and 618 m (submaximal intensity). Distances covered at medium intensities The total distance covered at medium intensities (14.119.0 km × h1) was not explained by the situational variables. When all independent variables were equal to zero, the distance covered by players was 1677 m. Distances covered at low intensities The total distance covered at low intensities (B14.1 km × h1) was explained by match status, match location, and quality of the opponent. For each minute winning, the distance covered walking and jogging (011 km × h1) increased by 2.2 m (P B0.05) compared with each minute losing. Accordingly, each minute winning increased by 2.1 m (P B0.01) the distance covered at low-speed running (11.114.0 km × h1) compared with each minute losing. Playing away decreased the total distance covered walking and jogging and at low-speed running by 144 m (P B0.01) and 66 m (PB0.05), respectively. Finally, players

Positional role Central defenders External defenders Central midfielders External midfielders Forwards

Total distance covered 10 11 11 11 10

491 050 320 425 686

(496) (482) (610) (354) (714)

Walking and jogging (011 km × h 1) 6864 6791 6941 6892 6813

(228) (245) (401) (261) (251)

Low-speed running (11.114.0 km × h 1) 1611 1621 1794 1671 1378

(181) (175) (210) (278) (232)

Medium intensities (14.119.0 km × h 1) 1441 1735 1903 1916 1567

(277) (247) (334) (161) (336)

Submaximal intensity (19.123.0 km × h 1) 388 576 502 609 584

Maximal intensity (23 km × h 1)

(114) (135) (132) (117) (116)

188 327 179 337 344

(84) (131) (84) (94) (112)

Table III. The influence of match location, quality of opposition, and match status on the total distance covered (m) during an entire match (standard errors in parentheses)

Total distance covered Variables

Coeff.

Beta

Walking and jogging (011 km × h 1) Coeff.

Beta

Low-speed running (11.114.0 km × h 1) Coeff.

Beta

Match status drawing 3.79 (2.48) 0.16 3.63** (1.10) 0.35 1.68* (0.84) 0.19 winning 2.10 (1.19) 0.10 2.18* (0.97) 0.23 2.13** (0.69) 0.28 Match location 262.47** (11.32) 0.19 143.93** (42.96) 0.23 66.06* (37.85) 0.13 Quality of opposition 15.47* (11.32) 0.12 16.81** (5.27) 0.33 4.99 (3.43) 0.12 Intercept 10719.91** (190.52) 6619.73** (102.98) 1508.53** (58.09) R2 0.38 0.37 0.36 Note: Betastandardized coefficients. ** P B0.01; * PB0.05.

Medium intensities (14.119.0 km × h 1) Coeff.

0.29 (1.28) 1.65 (1.18) 18.27 (55.35) 2.49 (5.93) 1677.62** 95.58 0.32

Beta

Submaximal intensity (19.123.0 km × h 1) Coeff.

Beta

Maximal intensity (23 km × h 1) Coeff.

0.02 1.32* (0.55) 0.25 0.48 (0.51) 0.01 1.09* (0.51) 0.23 0.95* (0.38) 0.03 19.02 (23.55) 0.06 15.20 (20.16) 0.04 4.29 (2.71) 0.16 1.54 (2.14) 617.94** (39.96) 302.07** (33.69) 0.42 0.44

Beta

0.11 0.24 0.06 0.07

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Table II. Distance covered (m) at different work intensities by players of different positional roles (standard deviations in parentheses)

294 614 1633 1445 6390 10 373 309 633 1651 6674 10 636

1595

265 533 1594 1512 6713 10 667 280 552 1612 6997 10 930

1678

209 523 1656 1609 6587 10 562 224 542 1674 6871 10 925

1673

180 442 1609 1704 6911 10 862 195 461 1627 1770 7195 11 125

Strong (value 14) Weak (value 5) Strong (value 14) Weak (value 5) Winning 90 min Winning 90 min Losing 90 min Losing 90 min

23 km × h 1 19.123.0 km × h 1 14.119.0 km × h 1 11.114.0 km × h 1 23 km × h 1

Total

011 km × h 1

Away matches

19.123.0 km × h 1 14.119.0 km × h1 11.114.0 km × h1 011 km × h 1 Total Quality of opposition

The results of the present study appear to confirm that the distance covered at various speeds by elite soccer players is dependent on match contextual factors. The results were always influenced by one or more situational variables, especially match location and match status. Thus, elite soccer players performed less high-intensity activity when winning than when they were losing. A 50% decline in the distance covered at submaximal and maximal intensities (19.1 km × h1) when winning suggests that players do not always use their maximal physical capacity for the 90 min. In fact, given that winning is a comfortable status for a team, it is possible that players assume a ball contention strategy, keeping the game slower, which results in lower speeds (Bloomfield et al., 2005b). Accordingly, it is obvious that players performed less low-intensity activity when losing than when winning in an attempt to recover from an unfavourable position. Home teams covered a greater distance than visiting teams at low intensity (B14.1 km × h1), but no differences were observed at medium, submaximal or maximal intensities. Despite the fact that home advantage in soccer is a well-known and welldocumented fact (Brown et al., 2002; Clarke & Norman, 1995; Nevill & Holder, 1999; Pollard, 1986; Tucker et al., 2005), the precise causes and their simple or interactive effects on performance are still not clear. However, the most plausible

Match status

Discussion

Home matches

covered more distance when playing against better ranked teams. Each position difference in the end-ofseason ranking between opposing teams increased the total distance covered walking and jogging by 17 m (P B0.01). When all the independent variables were equal to zero, the distance covered by players walking and jogging was 6620 m and at low-speed running 1508 m. To clarify the impact of the results presented in the regression model, Table IV presents a simulated total distance covered by players at different speeds under different scenarios. What distance would be covered by players when the evolving match status differs? Is it similar when the team plays away against strong opposition or plays at home against weak opposition? In Table IV, different possibilities for each situation variable are showm. For example, the expected distance covered at maximal intensity (23 km × h1) by players differs considerably according to match status (by 31%). If the final result in a match were 10 to the sampled team and they scored the goal in the first minute (90 min winning), the distance covered by players would be 195 m. If the opponent won 10 and scored the goal in the first minute, the distance covered by players would be 280 m.

Table IV. Simulated distance covered (m) at different speeds depending on match location, quality of opposition, and match status



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explanations are: crowd effects, travel effects, familiarity, referee bias, territoriality, specific tactics, rule factors, and psychological factors (Pollard, 2008). The distance covered at the lowest intensities (011 km × h1) was also explained by the variable quality of the opponent. The better the quality of the opponent, the higher the distance covered by walking and jogging. These results are similar to the findings of Mohr and colleagues (Mohr, Krustup, & Bangsbo, 2003) and Rampinini et al. (2009). They found that more successful teams covered less distance at lower intensities than players from less successful teams. In conclusion, the results emphasize the importance of accounting for match location, quality of opposition, and match status during the assessment of the physical aspects of soccer performance (Carling et al., 2005; Taylor et al., 2008). Physical performance was influenced by the situational variables, either independently or interactively. The importance of these factors is reflected in changes in the teams’ and players’ activities as a response to match status. The implications for match analysts and coaches for evaluating performance and developing relevant training drills are obvious. Existing recommendations suggest that the scouting of upcoming opposition should be carried out under circumstances that are reflective of the conditions under which the future match will occur. However, such procedures are unlikely to be practical due to time and resource constraints. Consequently, by establishing the impact of particular situational variables on performance, teams can be observed, when possible, with appropriate adjustments being made to analyses based on knowledge of such effects (Taylor et al., 2008). Similarly, post-match assessments of the technical, tactical, and physical aspects of performance can be made more objective by factoring in the effects of situational variables (Carling et al., 2005; Kormelink & Seeverens, 1999). Finally, if a notational analyst or coach has identified that the technical, physical or tactical aspects of performance are adversely influenced by specific situational variables, possible causes can be examined and match preparation focused on reducing such effects.

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