Classifying young soccer players by training performances.

Players within the same age group may present different physical and physiological profiles. This study classified young soccer players according to their physical and physiological profiles obtained during the training sessions and compared classification by age and playing position criteria. 151 male elite Portuguese soccer players (under 15, under 17, and under 19 years old) participated. Time-motion and body acceleration and deceleration data were collected using GPS technology with heart rate monitored continuously across the selected training sessions. The data were grouped using two-step cluster analysis to classify athletes. A repeated-measures factorial ANOVA was performed to identify differences in the variables. Three clusters comprised 15.2%, 37.1%, and 47.7% of the total sample, respectively. Players of the same ages and playing experience had different performance profiles. Grouping players with similar physiological profiles during training sessions may allow coaches to balance oppositions and reduce the variability of the physiological outcomes.

Time-motion and physiological profile of football training sessions performed by under-15, under-17 and under-19 elite Portuguese players.

PURPOSE: To provide the time-motion and physiological profile of regular training sessions (TS) performed during the competitive season by under-15 (U15), under-17 (U17), and under-19 (U19) elite-level Portuguese soccer players. METHODS: One hundred fifty-one elite players of U15 (age 14.0 ± 0.2 y, n = 56), U17 (age 15.8 ± 0.4 y, n = 66), and U19 (age 17.8 ± 0.6 y, n = 29) participated in the study during a 9-wk period. Time-motion and body-impact data were collected using GPS technology (15 Hz) across 38 randomly selected TS that resulted in a total of 612 samples. In addition, heart rate (HR) was continuously monitored (1 Hz) in the selected TS. RESULTS: The total distances covered (m) were higher in U17 (4648.3 ± 831.9), followed by U19 (4212.5 ± 935.4) and U15 (3964.5 ± 725.4) players (F = 45.84, P < .001). Total body impacts and relative impacts were lower in U15 (total: 490.8 ± 309.5, F = 7.3, P < .01), but no differences were identified between U17 (total: 584.0 ± 363.5) and U19 (total: 613.1 ± 329.4). U19 players had less high- and very-high-intensity activity (above 16 km/h; F = 11.8, P < .001) and moderate-intensity activity (10.0-15.9 km/h; F = 15.07, P < .001). HR values showed significant effects of zone (F = 575.7, P < .001) and interaction with age group (F = 9.7, P < .001), with pairwise differences between all zones (zone 1, <75%; zone 2, 75-84.9%; zone 3, 85-89.9%; zone 4, ≥90%). All players spent most of their time below 75% HRmax (U15, ~50%; U17, ~42%; U19, ~50%). CONCLUSION: Results showed high variability between TS, refraining from identifying meaningful trends when measuring performance, although different demands were identified according to age group. The U15 TS were less physiologically demanding, probably because of increased focus on small-sided games to develop basic tactical principles and technical skills. The focus on game-like situations imposed higher external and internal workloads on U17 and U19 players.

Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game.

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.