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Introduction: There has been an abundance of dietary analysis research conducted on adult male soccer players, while studies on youth players are lacking. Furthermore, the daily distribution of energy and macronutrient intake throughout the day has been reported to influence training adaptations, but this is often not considered in the literature. This study aims to quantify daily energy and macronutrient intake and assess their distribution over 5 days, and compare daily energy intakes and predicted daily energy expenditure in under-16 male soccer players. Methods: The sample included 25 soccer participants aged 14.8–15.7 years. Five-day self-reported food diaries were used to record the food/drink consumption. Intake was analyzed for total daily energy, macronutrient intakes, and distribution among meals (breakfast, lunch, dinner, and snacks). Daily energy expenditure was predicted by resting energy expenditure and physical activity levels developed for youth sports participants. Results: The mean total energy intake was 1,928 ± 388 kcal∙day−1, whereas the estimated daily energy expenditure was 3,568 kcal∙day−1. Relative daily protein intakes were lower at breakfast, morning snack, afternoon snack, and night snack compared to lunch and dinner. Discussion: Youth soccer players do not appear to meet energy requirements and daily CHO guidelines. Fluctuations in protein intake throughout the day were noted and may influence training adaptations (i.e., muscle protein synthesis and recovery).

Purpose: The purpose of this study was to analyze variations of selection for competition between late and early mature players and test the relationships between anthropometry, body composition, maturation, and selection for competition. Methods: Seventy-nine youth soccer players from under-11 to under-14 participated in this study, over 6 months. Body composition and maturity offset were estimated based on anthropometric data collected. Participants were also monitored for their number of matches as starters and time of play accrued in minutes. Results: Minutes played had large correlation coefficients with maturity offset (r = 0.58), and leg length and sitting height interaction (r = 0.56). Multiple linear regression explained 35% of the variation in minutes played (p < 0.001, R2 = 0.41, R2adjusted = 0.35, RMSE = 334.8), but only 12% of the variation in matches as starter (p = 0.04, R2 = 0.21, R2adjusted = 0.12, RMSE = 5.47) between above and below the median of the maturity offset was accounted for, respectively. Conclusions: Although maturation may play a role in the minutes of play accrued and matches as starters in young, it is not necessarily determining. A significant amount of the variation in the minutes of play accrued of players can be accounted for when considering body composition and anthropometric data.

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.