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.

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% HRmax, while the prematch presented a higher distance covered above 18 km · h(-1) and time spent below 75% HRmax. 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% HRmax, 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.

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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) under-17 (U17) players played with each other; and iii) players under the age of 15 and 17 played with each other in two equivalent mixed age teams. The players’ physical performance was measured using the distances covered at different speed categories and tactical behaviour was assessed using several positioning-derived variables. The results showed that, when playing in the mixed age condition, the U15 players increased the distance covered in sprinting intensity (18.1%; ±21.1%) and the U17 players increased the distance covered in jogging zones (6.8%; ±6.5%). The intra-team movement synchronization in longitudinal and lateral displacements was higher when U15 players confronted peers of the same age, in the first half (-13.4%; ±2.0%, -20.3%; ±5.7% respectively), and when U17 players confronting the mixed group, in both halves (-16.9%; ±2.5%, 9.8%; ±4.0% and 7.9%; ±5.7%, 10.6% ±4.4%, respectively). The differences between age groups and the mixed condition may be connected with the level of players’ tactical expertise and adaptive positioning according to the dynamic environmental information. In general, these results suggest that mixing the age groups may be useful to promote a wider range of training session stimuli in these young football players.