The purpose of this study was twofold: 1) to characterize a common microcycle considering both internal and external training loads; and 2) to identify the effects of small-sided games (SSGs) and of power and strength training on the fitness status of football players. Fifteen male football players (age: 18.55±0.39 years) participated in this study. Ninety-two consecutive training sessions were monitored and analyzed over a period of nineteen weeks. Rating of perceived exertion (RPE ) was used as an internal load marker, and the distances covered at different speed thresholds and accelerations/decelerations were used as external load markers to characterize the common microcycle. Participants' body composition, vertical jumping ability, maximal strength, speed, and agility were assessed twice before and after the training monitoring process. The results revealed that match day -5 (MD-5) and MD-1 were associated with the lowest RPE scores (4.2 and 3.8 A.U., respectively). MD-4 and MD-3 were associated with the highest RPE values (9.2 and 8.8 A.U., respectively). Meaningful changes in RPE were found between training days. External load monitoring revealed that MD-4 had the highest values of accelerations and decelerations >2 m/s2/min (4.22 and 3.17, respectively) and MD-3 had the highest values of distance covered at high intensity (6.11 m/s2/min). Meaningful moderate improvements in jumping performance (d=0.90) and maximal strength parameters (d=0.83) were also found between assessments. It was identified that the concurrent approach had meaningful impacts on the fitness development of players and should be considered by coaches for future training interventions.

Background: The main purpose of the current study was to compare the within-season variations of workload, training duration, acute/chronic workload ratio (ACWR), training monotony ™, and training strain (TS) through session rating perceived exertion (s-RPE) between starters and non-starters. Methods: Seventeen under-17 European male soccer players (age, 16.2 ± 0.3 y, height, 1.8 ± 0.1 m; body mass, 66.5 ± 4.0 kg) divided in two groups: nine starters and eight non-starters, were evaluated over 50 weeks throughout the season. Results: In general, there were load variations for all players during the full-season. RPE tended to decrease during in-season and RPE, training duration and s-RPE did not present significant differences between starters and non-starters. TM and TS presented lower values for starters in mesocycle (M) 4 and M11 compared to non-starters. TS presented lower values for starters in M4 and M11 compared to non-starters, while in M10 a higher value was found for starters when compared to non-starters. ACWR showed differences between starters and non-starters in two of the mesocycles. Conclusions: This study showed that some mesocycles provided higher load for non-starters. This information can alert coaches that non-starter players are likely to try too hard in training to demonstrate their abilities, leading to non-functional overreaching, overtraining syndrome, and then poor performance.

Introduction: Network analysis has gained increasing attention, as it provides a framework for identifying both collective and individual behaviours within the football teams. Objective: This study aimed to analyse the offensive actions that resulted in shots using network analysis in a Portuguese First Division football team during the 2020-2021 season. Methodology: All 34 matches were coded using Angles® software. Offensive actions were defined as sequences starting with a ball recovery and ending with a shot. Adjacency matrices were constructed for each match, and both macro and micro analytical approaches were employed to examine differences between the two halves of the season. Results: Findings indicated 914 intra-team interactions, with player 14 (midfielder) and player 2 (forward) as key contributors, particularly in micro network metrics such as degree prestige (passes received) and degree centrality (passes made). Statistical analysis revealed no significant differences in network metrics, including density (W = 95, p = 0.0912) and clustering coefficient (W = 112, p = 0.2689), between the season halves. Discussion: These findings offer valuable insights for practitioners seeking in recognizing play patterns and optimizing team dynamics. Identifying key players allows coaches to design targeted training exercises, enhance player roles, and better assess opposition threats and vulnerabilities. Conclusions: Network metrics provides a comprehensive understanding of team dynamics, particularly in identifying key contributors to offensive actions.

This study analysed how tactical systems and match outcomes influence micro-level passing network metrics across playing positions in a professional football team competing in the Portuguese First Division during the 2020–2021 season. It examined how structural variation affects Degree Centrality, Degree Prestige, and Proximity Prestige across tactical systems (1-4-1-4-1, 1-4-3-3, 1-3-4-3) and outcomes (win, loss, draw) in different positions. Data from 28 league matches were used, with adjacency matrices constructed from teammate interactions. Players were grouped into six positions: goalkeepers, fullbacks, central defenders, central midfielders, wingers, and strikers. One-way ANOVA revealed significant differences (p < 0.05) across positions, tactical systems, and match outcomes. Central defenders consistently showed higher values of Degree Centrality and Degree Prestige across most systems and outcomes, highlighting their structural importance. In contrast, strikers and wingers displayed greater Proximity Prestige in the 1-4-3-3 and 1-3-4-3, reflecting their offensive positioning. Match outcome analysis indicated that wingers had significantly higher Degree Prestige in won matches compared to losses. Overall, results show that micro-level network metrics vary meaningfully by position and context, underscoring the importance of interpreting them cautiously. Despite the novelty of this study, focusing on the initial tactical systems without capturing within-match adjustments may condition the generality of the results. Coaches and practitioners should account for tactical and outcome-related variations when applying network analysis to optimise team dynamics.