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Abstract Background Bioelectrical impedance analysis (BIA) is a rapid and user-friendly technique for assessing body composition in sports. Currently, no sport-specific predictive equations are available, and the utilization of generalized formulas can introduce systematic bias. The objectives of this study were as follows: (i) to develop and validate new predictive models for estimating fat-free mass (FFM) components in male elite soccer players; (ii) to evaluate the accuracy of existing predictive equations. Methods A total of 102 male elite soccer players (mean age 24.7 ± 5.7 years), participating in the Italian first league, underwent assessments during the first half of the in-season period and were randomly divided into development and validation groups. Bioelectrical resistance (R) and reactance (Xc), representing the bioimpedance components, were measured using a foot-to-hand BIA device at a single frequency of 50 kHz. Dual-energy X-ray absorptiometry was employed to acquire reference data for FFM, lean soft tissue (LST), and appendicular lean soft tissue (ALST). The validation of the newly developed predictive equations was conducted through regression analysis, Bland–Altman tests, and the area under the curves (AUC) of regression receiver operating characteristic (RROC) curves. Results Developed models were: FFM = − 7.729 + (body mass × 0.686) + (stature2/R × 0.227) + (Xc × 0.086) + (age × 0.058), R2 = 0.97, Standard error of estimation (SEE) = 1.0 kg; LST = − 8.929 + (body mass × 0.635) + (stature2/R × 0.244) + (Xc × 0.093) + (age × 0.048), R2 = 0.96, SEE = 0.9 kg; ALST = − 24.068 + (body mass × 0.347) + (stature2/R × 0.308) + (Xc × 0.152), R2 = 0.88, SEE = 1.4 kg. Train-test validation, performed on the validation group, revealed that generalized formulas for athletes underestimated all the predicted FFM components (p 0.05), with R2 values ranging from 0.83 to 0.91, and no trend (p > 0.05). The AUC scores of the RROC curves indicated an accuracy of 0.92, 0.92, and 0.74 for FFM, LST, and ALST, respectively. Conclusions The utilization of generalized predictive equations leads to an underestimation of FFM and ALST in elite soccer players. The newly developed soccer-specific formulas enable valid estimations of body composition while preserving the portability of a field-based method.

The assessment of body composition over a competitive season provides valuable information that can help sports professionals to evaluate the efficacy of training and nutritional strategies, as well as monitoring athletes’ health status. The purpose of this study was to examine the association of changes in body composition and hydration status with changes in lower-body neuromuscular performance in soccer. Twenty-two male professional soccer players (mean ± SD; age: 26.4 ± 4.8 years; height: 184.3 ± 5.7 cm; body mass: 81.1 ± 6.5 kg; body fat: 11.6 ± 1.5%) took part in the study, for which they were tested at the initial and final stage of the competitive season. Total (whole body) and regional (arms and legs) lean soft tissue (LST) were estimated to obtain the body composition profile. Total body water (TBW) content, including extracellular (ECW) and intracellular (ICW) water, was obtained to monitor players’ hydration status. Countermovement jump (CMJ) height, power, and strength were used to derive players’ lower-body neuromuscular performance. The results showed that changes in legs LST and ICW significantly (p < 0.01) explained (r2 = 0.39) the improvements in CMJ height, power, and strength from the initial to the final stage of the season. Given the high demand imposed on the lower limbs during a soccer season, being more susceptible to change compared to whole-body LST, assessing regional LST and ICW would be more appropriate to provide extended information on players’ readiness.

The present study aimed to investigate how playing positions differ in specific body composition variables in professional soccer players with respect to specific field zones and tactical lines. Five hundred and six Serie A and B professional soccer players were included in the study and analyzed according to their playing positions: goalkeepers (GKs), central backs (CBs), fullbacks (FBs), central midfielders (MIDs), wide midfielders (WMs), attacking midfielders (AMs), second strikers (SSs), external strikers (ESs), and central forwards (CFs), as well as their field zones (central and external) and tactical lines (defensive, middle, and offensive). Anthropometrics (stature and body mass) of each player were recorded. Then, body composition was obtained by means of bioelectric impedance analysis (BIA). GKs and CFs were the tallest and heaviest players, with no differences from each other. Likewise, GKs and CFs, along with CBs, were apparently more muscular (for both upper and lower limbs) and fatter at the same time compared with the other roles. Overall, players of the defensive line (CBs and FBs), along with those playing in central field zones (CBs, MIDs, AMs, SSs, and CFs), were significantly (p < 0.05) superior in almost all anthropometric and body composition variables than those of middle and offensive line and external zones, respectively.

Metabolomics is a promising tool for studying exercise physiology and exercise-associated metabolism. It has recently been defined with the term “sportomics” due to metabolomics’ capability to characterize several metabolites in several biological samples simultaneously. This narrative review on exercise metabolomics provides an initial and brief overview of the different metabolomics technologies, sample collection, and further processing steps employed for sport. It also discusses the data analysis and its biological interpretation. Thus, we do not cover sample collection, preparation, and analysis paragraphs in detail here but outline a general outlook to help the reader to understand the metabolomics studies conducted in team-sports athletes, alongside endeavoring to recognize existing or emergent trends and deal with upcoming directions in the field of exercise metabolomics in a team-sports setting.

Background: The present study aimed to determine the association of anthropometry-based characteristics with an aggregate score (AS) of physical performance in young elite soccer players. Methods: Sixteen under 15 elite players were enrolled. Among numerous anthropometrics variables, upper arm contracted (UACC) and relaxed circumference (UARC), corrected arm muscle area (AMAcorr), arm muscle circumference (AMC), thigh muscle circumference (TMC) and suprapatellar girths were also employed in this study. Players’ physical performance was assessed by the change of direction (COD), 10 m and 20 m sprint, countermovement jump (CMJ) test, sprint with 90∘ turns (with ball), and yo-yo intermittent recovery test level 1 (Yo-Yo IRT1). The AS was computed by Principal Components Analysis technique with one component on normalized performance results. A stepwise regression analysis was conducted to assess potential association between anthropometry-based variables and AS. Results: Large negative correlations (r < –0.68) of AS with UACC, UARC, AMAcorr, and AMC were detected. UACC and TMC permits to accurately estimate AS explaining 60% of the total variance (p < 0.001). Conclusions: These findings demonstrated the importance of including anthropometry-based measures of both upper and lower body to the physical performance potential expressed by AS in elite youth soccer.

Background: The objective of this study was to investigate the association between regional and total phase angle (PhA) with lower-body neuromuscular performance in young elite soccer players. Methods: Sixteen elite male soccer players (14.3 ± 1.0 years) participated in this study. Lower (LPhA)- and upper (UPhA)-hemisome PhA together with whole-body PhA (WBPhA) were measured by a bioelectrical-impedance analysis (BIA), while appendicular arm and leg lean soft tissue (ALST and LLST, respectively) were estimated. Urine osmolarity (UOsm) and urine-specific gravity (USG) were also considered. Sprints over 10 m and 20 m and countermovement jump (CMJ) tests were employed to evaluate neuromuscular performance. Results: LPhA (p = 0.003) and UOsm (p = 0.012) explained 62% of the variance in the 10 m sprint. UOsm (p = 0.001) and both LPhA (p < 0.001) and WBPhA (p = 0.024) explained 81% of the total variance in the 20 m sprint. The CMJ height was affected by LPhA (p < 0.001) and UOsm (p = 0.024), which overall explained 68% of its variance (p < 0.05), while 93% of the CMJ power variance was explained by LPhA (p < 0.001), ALST (p < 0.001), and WBPhA (p = 0.011). Conclusions: Regional PhA is a relevant and non-invasive tool to monitor lower-body neuromuscular performance in elite youth soccer. Specifically, LPhA may be favored over WBPhA as more informative.