The aim of the present study was (a) to describe the anthropometric profile of a large group of soccer players based on different age groups and their playing positions on the field, and (b) to examine the variations of body composition among adult soccer players using diverse equations based on skinfold thickness. A total of 618 Greek soccer players who were grouped by age (i.e., 12–14, 14–16, 16–18, and 18–37 years) and playing position (i.e., goalkeeper, defender, midfielder, and forward) were evaluated for weight, height, and skinfolds. The Pařízková formula was used to estimate the percentage of body fat. Furthermore, for players who were 18 years or older the Reilly and Evans formulas was used to estimate the percentage of body fat. Independent of the age, in this large sample, goalkeepers presented higher values for weight, height and the percentage of body fat estimation as compared with other field positions. An anthropometric pattern was observed in each tactical position, namely, across a specific age of increasing maturation process (14–16 years). With the Pařízková formula, we found a mean (SD) range of variation in the percentage of body fat estimation between 4.87 ± 1.46 and 5.51 ± 1.46 as compared with the Evans formula. The same pattern of differences was found when the Reilly equation was considered. In conclusion, we observed a position specificity of anthropometric characteristics across different age categories. Additionally, the same data supported different validated equations which resulted in large differences in the final outcome estimations.

This study had two main objectives: (1) to determine if differences in Functional Movement Screen (FMS) scores exist between two levels of competition; and (2) to analyze the association between FMS individual and overall scores and physical performance variables of lower-limb power (jumps), repeated sprint ability and shot speed. Twenty-two Under 16 (U16) and twenty-six Under 19 (U19) national competitive soccer players participated in this study. All participants were evaluated according to anthropometrics, FMS, jump performance, instep kick speed and anaerobic performance. There were no significant differences in the individual FMS scores between competitive levels. There were significant negative correlations between hurdle step (right) and Running-based Anaerobic Sprint Test (RAST) power average ( $\mathsf{\rho }$ = −0.293; p = 0.043) and RAST fatigue index (RAST FatIndex) ( $\mathsf{\rho }$ = −0.340; p = 0.018). The hurdle step (left) had a significant negative correlation to squat jump (SJ) ( $\mathsf{\rho }$ = −0.369; p = 0.012). Rotary stability had a significant negative correlation to RAST fatigue index (Right: $\mathsf{\rho }$ = −0.311; p = 0.032. Left: $\mathsf{\rho }$ = −0.400; p = 0.005). The results suggest that individual FMS scores may be better discriminants of performance than FMS total score and established minimal association between FMS scores and physical variables. Based on that, FMS may be suitable for the purposes of determining physical function but not for discriminating physical performance.

Background: No self-report method to measure different types and intensities of physical activity (PA) in adults has been tested in the Portuguese population. We assessed the validity, reproducibility and seasonal bias on past-year PA reporting. Subjects and methods: A sample of 953 Portuguese adults was evaluated between 2001 and 2003. A 4 × 7-day PA diary was used as a reference method to evaluate the validity of the EPIC questionnaire adapted for the Portuguese population (n = 114). Spearman's correlation coefficients were calculated and agreement was tested using Bland–Altman plots. Trigonometric linear models were used to assess the seasonal variation. Results: Correlations between the questionnaire and the diaries were 0.56, 0.50, 0.88 and 0.78 for total, rest, occupational and leisure-time PA, respectively. The coefficients for reproducibility (2–3 months interval) ranged between 0.80 for leisure and 0.91 for occupational. Visualizing Bland–Altman plots, only rest PA revealed a tendency towards an increase in differences with increasing rest reported. Males interviewed in April and August reported the highest and lowest mean of leisure-time PA, respectively. For professional activities, the probability of amplitude being over one-half standard deviation was 33%. Conclusions: The questionnaire is a valid and reproducible instrument for the brief assessment of usual energy expenditure in adults, detailing different types of PA. In males, seasonal bias on reporting leisure-time and professional PA was found.

The Wingate anaerobic test (WAnT) has been used extensively to evaluate performance in soccer, however, a comprehensive sport-specific normative database has not been available so far. Therefore, the main aim of the present study was to develop norms of the main indices of the WAnT with regards to age in soccer. A secondary aim was to examine the relationship of WAnT with two common field tests, 20 m sprint and vertical jump, and study the variation of this relationship by age and playing position. Hundred and ninety five male soccer players (age 18.1 ± 4.9 years) performed the WAnT, and a sub-sample of 190 soccer players (age 19.4 ± 5.1 years) performed 20 m sprint, squat (SJ) and countermovement jump (CMJ). Age was related very largely with peak power (R² = 0.57) and mean power of the WAnT (R² = 0.60) when they were expressed in W, and largely (R² = 0.41 and R² = 0.33, respectively) when they were expressed in W.kg⁻¹, whereas it did not relate with fatigue index. After being adjusted for age, a relationship of SJ (B = 3.91, 90% CI: 2.49, 5.32; R² = 0.26), CMJ (B = 3.59, 90% CI: 2.22, 4.95; R² = 0.24) and 20 m sprint (B = −0.06, 90% CI: −0.10; −0.01; R² = 0.19) with peak power of the WanT was observed. In summary, P_peak and P_mean were related very largely to age, especially during adolescence, and percentile norms of these indices were developed for 1-year age groups from 11 to 21 years old and for a single adult age group (22–39 years old). These findings on the largest dataset of soccer players ever studied would be expected to offer a practical tool to the members of the sports medicine team (e.g., exercise physiologists, fitness trainers, and coaches) working with them.