Athletes must distribute their attention to many relevant cues during a match. Therefore, athletes’ ability to deal with dual-tasks may be different from the non-athlete population, demanding a deeper investigation within the sports domain. This study aimed to systematically review the acute and chronic effects of dual-tasks in motor and cognitive performances in athletes from different modalities. The search for articles followed all the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The keywords used were: “dual-task” OR “double task” OR “multi-task” OR “divided attention” OR “secondary task” OR “second task” AND “working memory” OR “visual” OR “decision making” OR “gaze behavior” OR “attention” AND “sports” OR “athletes” OR “players”. The Scopus, Pubmed, and Web of Science databases were screened for studies comparing single and dual-tasks, in which the participants were athletes competing at any level, and in which at least one of the following variables were investigated: working memory, decision-making, visual search behavior, perception, anticipation, attention, or motor tasks. Articles were screened using pre-defined selection criteria, and methodological quality was assessed by two researchers independently. Following the eligibility criteria, we included 18 articles in the review: 13 on the acute effects, and five on the chronic effects. This review showed that the acute effect of dual-tasks impairs the motor and cognitive performances of athletes (dual-task cost). However, training with dual-tasks (chronic effect) improved working memory skills and attentional control. We conclude that dual-tasks acutely and chronically impacts motor and cognitive performance.

This study on the 2018 Russia FIFA World Cup examined: i) the potential variations of ball possession, match running performance, player prominence, team network properties according to match outcome and playing formation; and ii) the relationships between player prominence and total distance covered according to team ball possession. Sixty-one matches were analysed (n = 988 player observations). Running performance was examined using total distance covered in (TDIP) and out of possession, and that travelled in different speed-range categories. Player prominence (micro) and team network properties (macro) were obtained using social network analysis where completed passes between teammates were counted (n = 28,019 passes). Main findings were: i) with the exception of clustering coefficients which indicate the level of interconnectivity between close teammates (win = draw > loss), match outcome was unaffected by ball possession, running and network measures; iii) teams employing a1‒4‒2‒3‒1 formation reported greater values for ball possession, TDIP, and micro/macro network measures compared to those playing 1‒4‒4‒2 and 1‒4‒3‒3 formations; iv) TDIP tended to be related to most player prominence variables, even though the magnitude of coefficients varied considerably according to network measures and playing positions. This study has provided additional insights into elite soccer match-play performance.

This study aimed to compare the physical and physiological responses of young football players of different categories during small-sided games (SSGs) played on different pitch sizes. Forty-eight (24 U-13 and 24 U-14) athletes played a 3 vs. 3 + 1 SSG in two experimental conditions: regular (36 × 27 m) and large pitch sizes (40 × 29 m). The total distance covered, the distances covered at different speed zones (0 to 6.9 km/h, 6.9 to 14.3, and 14.3 to 21.4), maximum heart rate, and mean heart rate were recorded. The results showed that older athletes covered larger distances during SSGs (p = 0.001; d = 0.937; large effect) and lower distances at the lowest (0–6.9 km/h) speed zone (p = 0.001; d = 0.657; moderate-to-large effect). Neither the physical nor physiological variables (except for distance covered between 14.3 and 21.4 km/h) differed between pitch sizes. This result indicates that pitch size may not impact the physical or physiological responses of U-13 and U-14 players during SSGs, but differences between categories were found. In conclusion, the development of tactical skills may be desirable to better explore the available space in the same age categories.

The analyses of players and teams’ behaviors during the FIFA World Cup may provide a better understanding on how football tactics and strategies have developed in the past few years in elite football. The Social Network Analysis (SNA) has been carried out in the investigations about passing distribution, improving the understanding on how players interact and cooperate during a match. In football official matches, studies have used the SNA as a means of coding players’ cooperation and opposition patterns. However, situational variables such as match status were previously investigated and associated with changes on teams’ dynamics within and/or between matches, but were not considered in studies based on Social Network Analysis. This study aimed to analyze the influence of match status on teams’ cooperation patterns and players’ prominence according to playing positions during 2018 FIFA World Cup. Fourteen matches of the knockout stage were analyzed. Macro and micro network measures were obtained from adjacency matrixes collected for each team, in each match status (winning, drawing, and losing). A one-way ANOVA was used to compare teams’ networks (macro-analysis variables) within each match status, while a two-way ANOVA (match status × playing position) was used to compare the micro-analysis variables. Results showed no differences between match status for macro analysis. Winning situations induced higher prominence in central midfielders (0.107; p = 0.001), wide midfielders (0.093; p = 0.001), and center forward (0.085; p = 0.001), while in losing situations lower prominence levels were observed for goalkeepers (0.044; p = 0.001) and center forward (0.074; p = 0.001). Data revealed that teams do not change macrostructures according to match status. On the other hand, the microstructures showed important adaptations regarding game styles, with changes in players’ behaviors according to playing positions. In general, the levels of centrality and prestige in players of different positions indicated a more direct play style in winning situations and a more build-up style in losing situations. These results allow a better understanding about the influence of match status on players’ and teams’ performance during high-level football competitions and may help coaches to improve athletes’ performance in these situations.

This study aimed to systematically review the influence of the relative area on athletes’ physical and physiological responses (outcomes) during small-sided games of soccer which were not matched to the relative area. It also presents a meta-analysis comparing standard small-sided games protocols with different relative areas. We searched the Web of Science and Scopus electronic databases for literature published up to March 2020 following all PRISMA guidelines for selecting articles for a qualitative and quantitative synthesis. As a result, eleven articles matched the inclusion criteria. For the quantitative synthesis, a pooled random-effects model was used to make pairwise comparisons between protocols with larger or smaller areas in each selected study. Prediction intervals were used to assess the heterogeneity. We found positive effects of larger relative areas per player on their total distance (d = 0.73; 95% CI = 0.12–1.34; p = 0.006; medium to large effect), distance covered at high speed (d = 0.93; 95% CI = 0.22–1.65; p = 0.001; large effect), and mean heart rate (d = 0.52; 95% CI = 0.17–0.88; p = 0.008; medium effect). In other words, larger relative areas were found to induce higher physical and physiological responses in players. Future studies should consider the relative area per player when comparing data for different small-sided games configurations to avoid confounding variables. Also, coaches can use relative area per player calculations to easily predict increases and decreases in expected training loads for different versions of SSGs during training.