For elite soccer players, the number of competitive matches per year, including domestic and international matches, has markedly increased in the last decade. Professional Spain-based soccer players competing in the domestic competition and Championship leagues are often required to play competition matches with only 1-2 days' recovery. The potential for residual fatigue in these matches is high with possible implications in terms of the movement behaviors of players competing in successive matches. There is reason to believe that too many matches can lead to lack of motivation and concentration can deteriorate, which can affect coordination, leading to underperformance and greater risk of injury (5,12, 13,14,15,16,19,28,29).
Existing research investigating fatigue during soccer match play has generally been focused to date on the influence of fatigue within matches (9,18,24) and over different phases of the season (17). However, the effects of a congested calendar on the physical performance of professional soccer players have not been extensively studied.
In a recent study, Odetoyinbo et al. (22) examined the effect of a succession of matches on the activity profiles of professional soccer players. In this study, the activity profile of United Kingdom-based professional soccer players were considered when 3 matches were played in 5 days. Overall, the results suggest that players were able to recover when the total distance is considered over 3 matches. The data, however, also indicate that some residual fatigue may be apparent that affects certain high-intensity aspects of play.
However, these findings are not conclusive given that some limitations or methodological problems can be observed. Most of the differences in activity profiles for individual players found in this study can be explained by the hugely varying positional demands in soccer, which differentiate the workload and therefore recovery requirements of players with differing tactical rules (8,9,10,11,24,34). Moreover, the potential impact of contextual factors such as match location (i.e., playing at home or away), match status (i.e., whether the team was winning, losing, or drawing) and the relative demands imposed by differing opposition over the matches has not been considered. However, previous studies (2,3,6,8,20,21,30-32) provide evidence to support the notion that contextual factors require consideration when evaluating soccer performance. Finally, the study is based on a small-sized sample.
Based on the limitations of the extant research, the aim of this study was to investigate recovery via analysis of activity profiles in a professional soccer team over an intense period of matches. It was hypothesized that the distances covered by players at different work intensities are not influenced by short recovery between matches. Moreover, it was hypothesized that the physical performance of elite soccer players is dependent on match contextual factors.
Experimental Approach to the Problem
The physical activities of soccer players during matches are well known, but the analysis of the influence of a succession of matches has been slightly studied. To analyze this, a total of 27 Spanish League matches played at weekends by a professional team during the 2005-2006 season were assessed. Players were divided into 2 groups according to the number of matches played per week (1 or 2) to test if differences exist in the distance covered at different work intensities. This research model provided 41 players for the first group (those players who played 2 matches a week) and 131 for the second (those players who played 1 match a week). Empirical evidence suggests that variables match location, match status, and quality of the opponent can affect soccer performances (2,3,6,8,20,21,30-32). These factors were included in the study as independent variables. (a) Match status, measured as the total number of minutes observed in each score-line state (winning, losing or draw). (b) Match location, a dummy variable indicating if the game was played at home or away. Playing at home is the comparison group. (c) Quality of opposition, the difference in the final ranking (at the end of the current season) of the considered team and the opponent, that is, Quality of opposition = PA − PB, where PA is the final ranking of the sampled team and PB is the final ranking of the opponent.
Finally, players were categorized into 1 of 5 individual playing positions: Central Defenders (CDs), External Defenders (EDs), Central Midfield players (CMs), External Midfield players (EDs), and Forwards (Fs).
Twenty-three elite soccer players belonging to a Spanish first division senior team volunteered to participate in the study. Players were categorized into 1 of 5 individual playing positions: CDs (n = 5), EDs (n = 5), CMs (n = 5), EMs (n = 4), and Fs (n = 4). The sample included only players that played in their customary position. Altogether, 172 observations of match performance were obtained. The protocol was approved by the local university ethics committee, and all subjects gave written informed consent before participating. The subjects could withdraw from the study at any time and were informed about the protocol details without being informed about the aim of the study.
Multiple-Camera Match System Analysis System
A computerized player tracking system (AMISCO Pro©, Sport-Universal Process, Nice, France) was used to characterize activity profiles in the team. This multiple-camera system tracked the movements of every player over the course of matches. It provided information on running speeds, distances covered, time spent in different categories of movement, and the frequency of occurrence for each activity. Player movements were tracked at a sampling rate of 25.0 Hz providing approximately 2.5 million data points per match (8). Simultaneously, a trained operator coded each technical action involving the ball. The workings of the AMISCO Pro system have been described in more detail elsewhere (6,8,9). Zubillaga et al. (34) and Randers et al. (27) 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. An intra and interobserver reliability analysis was conducted using 5 elite soccer players. Two trained observers tracked each player on 2 occasions, interspersed by 1 week, and the coefficient of variation (CV) was determined to assess reliability (1). The intra and interobserver CV for total distance, walking, running, high-speed running, high-intensity running, and very high-intensity running was <2% with the exception of sprinting that was <3%.
Activity Profile Measurements
From the stored data, the distance covered, the time spent in 5 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). In line with other studies (9,24,34), match analyses were performed distinguishing between the following 5 intensity categories (7,8): 0−11 km·h−1 (standing, walking, jogging); 11.1−14.0 km·h−1 (low-speed running); 14 0.1−19.0 km h−1 (moderate speed running); 19.1−23.0 km·h−1 (high-speed running); >23 km·h−1 (sprinting).
All statistical analyses were performed using SPSS for Windows, version 17.0 (SPSS Inc., Chicago, IL, USA). For all analyses, statistical significance was set at p ≤ 0.05. All results are reported as mean ± SD. Before using parametric statistical test procedures, the normality of the data was verified. An independent-samples t-test was performed to test for differences in the distance covered at various speeds by the players of the 2 groups considered. Cohen's effect sizes were also calculated to describe any trends apparent in the data. An effect of size of 0.3 or less was considered small, 0.3-07 moderate and 0.7 or more, large (1). A standard multiple regression was used to examine how much the distances covered at various speeds by the players was explained by the contextual variables (match location, match status, and quality of opposition), the number of matches played per week and the individual playing position of the players. When estimating the regression models, we found no evidence of heteroscedasticity in residuals or multicollinearity among regressors. Moreover, the RESET test (26) did not reveal specification problems. When interpreting the statistical results, positive or negative coefficients indicate a greater or lower propensity to increase or decrease distance covered by players.
The independent-samples t-test showed no significant differences in the distance covered at different work intensities between the 2 groups considered in the study (see Table 1). Those players who played 1 match a week covered a nonsignificantly greater distance at maximal (>23 km·h−1), submaximal (19.1-23 km·h−1), and medium (14.1-19 km·h−1) intensities than those players who participated in a midweek match. Nevertheless, the above mentioned covered a greater distance by walking and jogging (0-11 km·h−1). Small to moderate effect sizes were recorded, ranging from 0.01 to 0.28.
The effects of the number of matches played during a week, playing positions, match location, quality of the opposition, and match status on distance covered at various speeds in elite soccer are displayed in Table 2.
Total Distance Covered
The number of matches played during a week was not significant for explaining the total distance covered by players in a game. The total distance covered was explained by match location (p < 0.01) and quality of the opponent (p < 0.05). In essence, playing away decreased the total distance covered by 282 m compared with playing at home. Players covered more distance when playing against better ranked teams. Each position of difference in the end-of-season-ranking between confronting teams increased the total distance covered by 16 m. There were differences among playing positions. The ED, EM, F, and CM covered a significantly greater distance than CD. When all the independent variables were zero, that is, the team was losing during the whole match played at home, the distance covered by players was 10,234 m.
Distance Covered at Submaximal or Maximal Intensities (>19.1 km·h−1)
The number of matches played during a week was not significant for explaining the total distance covered by players at submaximal or maximal intensities in a game. The total distance covered at submaximal or maximal intensities was explained by match status. For each minute winning, the distance covered at maximal intensity decreased by 0.81 m (p < 0.05) compared with each minute losing. For example, if the team was losing for all 90 minutes, the predicted distance covered at maximal intensity would be 73 m higher than if winning during the whole match. At submaximal intensity, for each minute winning the distance covered decreased by 0.84 m (p < 0.05) compared with each minute losing. There were differences between playing positions. The ED, EM, and F covered a significantly greater distance at maximal intensity than CD. However, no statistical differences were found when comparing distances covered by CD and CM. At submaximal intensity, ED, CM, and F covered a significantly greater distance than CD. When all the independent variables were equal to zero, the distance covered by players was 227 m (maximal intensity) and 499 m (submaximal intensity).
Distance Covered at Medium Intensities (14.1-19 km·h−1)
The total distance covered at medium intensity was not explained by the number of matches played during a week and the situation variables. When all independent variables were equal to zero, the distance covered by players was 1,360 m. There were differences between playing positions. The ED, CM and EM covered a significantly greater distance than CD. However, no statistical differences were found when comparing distances covered by CD and F.
Distance Covered at Low Intensities (<14.1 km·h−1)
The number of matches played during a week was not significant for explaining the total distance covered by players at low intensities in a game. The total distance covered at low intensities was explained by match status, match location and quality of the opponent. For each minute winning, the distance covered by walking and jogging (0-11 km h−1) increased by 2.2 m (p < 0.05) compared with each minute losing. Accordingly, each minute winning increased by 1.8 m (p < 0.01) the distance covered at low-speed running (11.1-14 km·h−1) compared with each minute losing. Playing away decreased the total distance covered walking and jogging and at low-speed running, respectively, by 144 m (p < 0.01) and 67 m (p < 0.05). Players covered more distance when playing against better ranked teams. Each difference of position in the end-of-season-ranking between confronting teams increased the total distance covered by walking and jogging in 17 m (p < 0.01). No statistical differences were found between playing positions when comparing the distance covered by walking and jogging. CM and F covered a significantly greater distance than CD at low-speed running, but no statistical differences were found when comparing distances covered by CD, ED, and EM. When all the independent variables were equal to zero, the distance covered by players by walking and jogging was 6,632 m and at low-speed running was 1,511 m.
The aim of this study was to investigate recovery via analysis of activity profiles in a professional soccer team over an intense period of matches. Existing research investigating fatigue during soccer match play has generally been focused to date on the influence of fatigue within matches and over different phases of the season (6,13). However, the effects of a congested calendar on the physical performance of professional soccer players have been generally overlooked.
The main finding of this study suggests that the activity profiles of professional soccer players were not influenced by short recovery between matches. Although those players who played 2 matches a week covered lower distance at maximal (>23 km·h−1), submaximal (19.1-23 km·h−1), and medium (14.1-19 km·h−1) intensities than those players who played 1 match a week, no significant differences were found. The present results are in line with the findings of Odetoyinbo et al. (22). The walking profile demonstrates an inverse relationship. Players covered greater distance by walking and jogging when 2 matches were played in the same week. These findings are different than those provided by Odetoyinbo et al. (22). Two reasons may explain the discordance in the results between the 2 studies (a) the fact that Odetoyinbo et al. (22) did not consider the potential impact of the contextual factors on physical performance of players; (b) 2 different competitions were analyzed (Premier League vs. Spanish Soccer League).
Many coaches maintain that an athlete's level of preparation is also elevated by participation in competitions. Participation in competitions does assist athletes to reach a high state of readiness for the next competition. During such competitions, athletes have the opportunity of testing all training factors in the most specific way. Perhaps, to a certain extent, a top team can cope with a busy match schedule. However, it should not be expected that a degree of training and correct peaking can be achieved through competition only. To consider competition as the only means of improvement weakens the whole philosophy of training and, consequently, disturbs the main cycle of activity:training, competition, and regeneration. Future studies should determine the ideal duration of the competitive phase in professional soccer. As far as frequency and number of matches played are concerned, the time required for recovery between matches should be considered.
Moreover, results from this study seem to confirm that the elite soccer players' distance covered at various speeds is dependent on match contextual factors. Elite soccer players performed less high-intensity activity when winning than when they were losing. These results suggest that players are not always using their maximal physical capacity for the 90 minutes. These results are similar to the findings of Bloomfield et al. (2), O'Donoghue and Tenga (21), and Shaw and O'Donoghue (30).
The home teams covered a greater distance than visitors at low intensity (<14.1 km·h−1), but no differences were found at medium, submaximal, or maximal intensities. Despite the fact that home advantage in soccer is well known and well documented (4,20,23), the precise causes and their simple or interactive effects on performance are still not clear.
The distance covered with the lowest intensity (0-11 km·h−1) 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 et al. (17) and Rampinini et al. (25).
The results emphasize the importance of accounting for match location, quality of opposition and match status during the assessment of the physical performance of soccer players (8,31).
Taking into account playing positions, CD and EM covered a greater total distance than F, CD, and ED. No statistical differences were found between playing position when comparing the distance covered by walking and jogging. Furthermore, it was observed that EM, F, and ED covered a greater distance at maximal intensity (>23 km·h−1) than CD and CM. Similar results were found by Di Salvo et al. (9) and Zubillaga et al. (34). They analyzed the Spanish Soccer League using the same data collection (tracking system), classifications of playing positions and ranges of velocities. This research again demonstrates the need for a criterion model in soccer to tailor training programs and strategies to suit the particular needs of individual playing positions.
The major limitations of this study were the low number of matches and players examined and that players played for only 1 club. Therefore, the patterns observed might only be a reflection of this particular team.
In summary, in this study the activity profiles of Spain-based professional soccer players were considered when two matches were played in 3 days. Overall, results suggest that the activity profiles of professional soccer players were not influenced by the short recovery between matches. However, further research is warranted to address others factors that may influence activity profiles over an intense period of matches. Work could be extended to examine the effects of match type (domestic cup competition vs. league games), and the influence of specific team line-ups (systems of play).
This study shows that elite soccer players did not perform below their normal standard on the weekend when they played a midweek match. This indicates that during a certain period a top team can cope with a busy match schedule. Playing 2 matches in a week does not seem to be an excessive workload to justify the rotations of the players in the line-up. Coaches should plan the competitive microcycles according to this finding.
However, future studies should determine how long high-level performance can be maintained playing 2 matches a week. Once this period has been determined, coaches should take measures to lessen loss of performance, for example, rotation of players in the starting line-up, reducing training loads or introducing additional recovery methods. Next, a training period will need to be planned to assure an improved level of performance working more on speed and strength and increasing training intensity.
The detailed evaluation of the influence of match location, quality of opposition, and match status (winning, drawing, or losing) on soccer performance within this study presents a number of implications for analysts and coaches. It is recognized that strategies in soccer are influenced by situational variables and teams alter their style of play during the game accordingly. As a consequence, the scouting of upcoming opposition should be carried out under circumstances that reflect the conditions under which the future match will occur: playing at home or away (match location), when the opponent is ahead, behind and level (match status) and against different quality of opponents (strong or weak). In a practical way, coaches should prepare their teams to apply different strategies and styles of play (i.e. counterattack, direct play or indirect play) depending on the previously mentioned factors.
Similarly, postmatch assessments of the technical, tactical, and physical aspects of performance can be made more objective by in the effects of situational variables. Finally, if an 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.
The authors gratefully acknowledge R.C.D. Espanyol for its help in determining this protocol and for its valuable assistance. The authors have no conflicts of interest that are directly relevant to the content of this article. This study was not supported by any financial aid. Results of this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.
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