The aim of this study was to identify differences in time–motion, modified training impulse, body load and movement behaviour between defenders, midfielders and forwards, during an 11-a-side simulated football game. Twenty elite youth male footballers from the same squad participated in this study (age: 18.1 ± 0.7 years old, body mass: 70.5 ± 4.3 kg, height: 1.8 ± 0.3 m and playing experience: 9.4 ± 1.3 years). All data were collected using GPS units (SPI-Pro, GPSports, Canberra, Australia). The movement behaviour was measured with kinematic data, used to calculate position-specific centroids (defenders, midfielders and forwards), and processed with non-linear statistical procedures (approximate entropy normalised and relative phase). There were significant effects and interactions in all variables across the players’ positions. The results showed that displacements of all players (defenders, midfielders and forwards) were nearer and more coordinated with their own position-specific centroids than with the other centroids. However, this coupling effect was stronger in midfield players and weaker in forwards. All players’ dynamical positioning showed more irregularity when related to the forwards’ centroid, as a consequence of their need to be less predictable when playing. The time–motion and physiological variables showed lower activity in forward players. Adding together, the results may contribute to a better understanding of players’ specific performances and football complexity.

The present study aimed to identify the collective tactical decisions from a high-level football team, by analysing team length, width and surface area in offensive and defensive phase. The sample was composed by six competitive matches, played against stronger and weaker level opponents. Multidimensional qualitative data was obtained using a computerized match analysis system (AMISCO®). The data was analysed using non-linear signal processing tools, such as relative phase to access coordination modes between teams and approximate entropy (ApEn) to measure randomness in each time-series. The results allowed identifying higher offensive length, width and surface area frequencies when playing against weaker teams. On the other hand, higher defensive length, width and surface area frequencies were found against stronger teams. In addition, the ApEn results of surface area shown that tactical behaviour tends to be repeated during the match, both in offensive and defensive phase. The present results contributed to better understand the use of pitch space and the team strategic framework, helping coaches to improve the accuracy of strategic planning.

This study aims to describe the time–motion and physiological performance profiles of footballers whose ages are under 15 (U15), under 17 (U17), and under 19 (U19) during a typical week of a competitive season. A total of 151 elite Portuguese players U15 (age 14.0 ± 0.2; n = 56), U17 (age 15.8 ± 0.4; n = 66), and U19 (age 17.8 ± 0.6; n = 19) were monitored during 33 training sessions (TSs) (U15 n = 12; U17 n = 11; and U19 n = 10 TSs). The TS data were captured at 15 Hz by global positioning systems devices and divided into post-match (session after the match), prematch (session before the match), and middle week (average of remaining sessions). The U15 middle week showed a higher number of sprints, distance covered in intermediate speed zones, and time spent above 90% HR_max, while the prematch presented a higher distance covered above 18 km · h⁻¹ and time spent below 75% HR_max. In U17, both prematch and post-match data presented lower values than middle-week data in most of the variables. The post-match data in U19 presented higher values of distance covered above 13 km · h⁻¹, body impacts above 10 G, and time spent above 85% HR_max, while middle week showed higher values in body impacts in most of the zones. In addition, the prematch data presented 35% to 100% less values than the middle-week data. Understanding the weekly workload variations according to the competition and the developmental ages of the players can contribute to optimising short- and mid-term planning.

This study aims to examine youth players’ physiological responses and technical-tactical performance when playing simulated 3x3 and 5x5 basketball games. Fifteen well-trained male basketball players (16.6 ± 0.2 years old) participated in scrimmage basketball games under two different conditions: 3x3 (half-court) and 5x5 (full-court). The players’ heart rate, muscle oxygen saturation and total hemoglobin data were collected and computed to describe physiological responses, while video analysis was used to characterize their technical-tactical performance. A Bayesian one-way analysis of variance (ANOVA) was used to quantify the predictive influence of both game conditions on the physiological and the technical-tactical variables. The results indicated that different game conditions influenced the players’ physiological responses slightly, as only hemoglobin sample entropy increased between the 3x3 and 5x5 game scenarios. Conversely, statistical differences in most of the technical-tactical variables were moderate and decisive in favour of the game condition model. Overall, this study emphasizes that playing 3x3 and 5x5 basketball games lead to relatively negligible differences in the players’ physiological response but pronounced variations in their technical-tactical performance. Therefore, important implications may be drawn to the applied field as the specificity of technical-tactical adaptations when playing 3x3 or 5x5 formats should be considered by basketball coaches to better design the training sessions for players that fall within our sample age category.

This study aimed to identify the game-related statistics that discriminated between Euroleague basketball players and European basketball players playing in the NBA, when competing in the same event (EuroBasket 2015). There was a total of 78 matches played by 24 teams in two groups of analysis: NBA, participants in the European Championship who played in the NBA season of 2014-2015 (n = 26); Euroleague, participants in the European Championship who played in the Euroleague season of 2014-2015 (n = 82). The players’ performance variables were normalized to the time they spent on the court. To identify which variables best discriminated between the NBA and the Euroleague performance profiles, a descriptive discriminant analysis was conducted. Structure coefficients (SC) from the matrix greater than |0.30| were interpreted as meaningful contributors to discriminating between the groups. The results revealed a significant function (p = 0.008, canonical correlation of 0.51, Λ = 0.74, reclassification = 84.2%) and substantial performance differences in game-related statistics much related to the influence of body size (body height and mass), such as two-point field goals made (SC = 0.42) and missed (SC = 0.40), free-throws made (SC = 0.55), defensive rebounds (SC = 0.62), blocks (SC = 0.48) and suffered fouls (SC = 0.34). No differences were found at the level of game-related statistics indirectly related to perception, such as assists, turnovers or steals. Also, the greater body size in NBA players was likely related to higher variability in performance, thus, being an important topic for coaches and recruiters to analyse.

The study aimed to compare footballers’ performances when playing with teammates and opponents from the same age group with performances when playing with teammates and opponents of different age groups. Three football matches were played: i) under-15 (U15) players played with each other; ii)...