ECSS Paris 2023: OP-AP12
INTRODUCTION: Adolescence is marked by significant variability in biological maturation, leading to differences in physical performance among youth athletes despite sharing the same chronological age (CA). These differences influence the timing and magnitude of peak height velocity (PHV), which varies between males (M) and females (F) but also within sexes (1), resulting in different development rates in physical performance qualities (2). Frequent assessment and long-term physical performance monitoring are crucial along the talent pathway. However, there is a lack of normative data on high-level youth athletes, especially considering the varying stages of maturation in both sexes. The study aimed to examine the impact of maturation and gender on physical performance. METHODS: A mixed-longitudinal study design was used, including 6171 data points from 1478 elite junior tennis players (3633 M: 14.0±2.1yrs; 2538 F: 13.8±1.9yrs) over the last 10 years. Athletes were categorized based on their biological maturity using the maturity offset method into pre-, mid-, and post-PHV. Physical performance was assessed via standardized tests, including anthropometrics, jumping, sprinting, handgrip strength, and endurance. A subsample (n=36) underwent repeated assessments for longitudinal force-velocity (Fv) profile tracking. Correlation and generalized additive model analyses were applied to examine performance differences across maturity stages. RESULTS: Early matured athletes had superior results relative to their peers at similar CA. Strength and speed-related tasks exhibited the strongest correlations to age, followed by endurance, with M showing higher correlations than F (e.g., CMJ: M: r=0.74; F: r=0.32). Improvements in 20m sprint times nearly doubled from pre- to post-PHV in M (M:11%; F:6%). F demonstrate greater relative changes during pre-PHV, while M exhibit larger relative increases during mid-PHV (e.g., Yo-Yo IR1; F est. 134m, 37%; M est. 228m, 21%). Longitudinal analysis revealed no significant changes in F0 and v0 over three consecutive times, although individual variations were present. CONCLUSION: The results indicate that performance improves with age and maturity, though the progression differs between sexes in both rate and magnitude. M exhibit a steep performance incline in mid- and partly post-PHV, especially in strength- and speed-based metrics, likely due to testosterone, increasing muscle mass, and neuromuscular efficiency. F enhance performance the most during pre-PHV before estrogen might restrict the minimal but gradual progression. Fv analysis generally aligns with M increasing values post-PHV, and F do not, although lacking significance. Especially F should start maturation-independent individualized training with the onset of PHV, focusing on plyometrics, speed, and endurance to maximize performance in adulthood. Males should prioritize neuromuscular performance post-PHV and its transfer to sports performance, e.g., sprinting. 1 - Cole et al, 2014 2 - Lesinski et al, 2016
Read CV Nicola Reiner VolkECSS Paris 2023: OP-AP12
INTRODUCTION: Young female soccer players are typically grouped by chronological age, but this method ignores the substantial differences in biological maturity that can impact critical performance factors like strength, speed, and coordination. Although studies have examined the influence of age and maturity on male youth athletes, research focusing on female players is still limited. As a result, there is insufficient knowledge about how chronological age and biological maturity interact to influence physical performance and development in young female soccer players. This study aims to address this gap by analyzing the effects of age groups and biological maturity on physical performance, including speed, jumping ability, change-of-direction (COD) ability, and endurance in young female soccer players. METHODS: Eighty-two young female soccer players from the same club academy squad participated in this study and were classified into four age groups based on their birth years: U11, U13, U15, and U18. Maturity offset (YPHV) was estimated using anthropometric measurements [Mirwald et al. 2002]. Each subject was categorized into one of three maturity group (i.e, PRE (YPHV <-1); MI (-1≤YPHV≤1) and POST (YPHV > 1)).The assessment of physical performance took place over four separate sessions, during which players underwent a series of standardized tests. These included the 30-15 intermittent fitness test, a 40-meter sprint, the COD 505 test, a countermovement jump (CMJ), and a repeated sprint test consisting of six 20-meter sprints. RESULTS: Players in the U11 age group consistently demonstrated lower performance across all tests compared to their older counterparts. Similarly, the PRE maturity group, predominantly composed of U11 players along with two U13 players, also exhibited reduced performance levels. U13 players performed worse than those in the U18 category, while U15 players achieved lower results in CMJ, sprint, and COD tests compared to the U18 group. Furthermore, the MI maturity group underperformed in all tests relative to the POST maturity group, which included half of the U15 players and all U18 players. The correlation between chronological age (AGE) and years from peak height velocity (YPHV) with performance was significant, ranging from 0.59 (YPHV) and 0.58 (AGE) for CMJ to 0.78 (YPHV) and 0.75 (AGE) for the 40-meter sprint time. Correlation coefficients for height and body mass were lower compared to those for AGE and YPHV, further highlighting the stronger influence of biological maturity and age on performance outcomes. CONCLUSION: This study shows that biological maturity (YPHV) influences performance slightly more than chronological age, particularly in speed, jumping, and change of direction. Grouping players solely by age may overlook key developmental differences. Integrating maturity status into training and talent identification could better optimize player development.
Read CV Pauline GerusECSS Paris 2023: OP-AP12
INTRODUCTION: Longitudinal monitoring of performance development could help improve talent identification accuracy in sport, including soccer (1). However, during adolescence, the capability to evaluate performance is confounded by sex-specific inter-individual maturational growth differences which do not necessarily align with chronological age-based evaluation methods. To better consider the influence of maturational growth in performance development data and reduce selection bias, the purpose of the study was to illustrate differences performance percentile curves when evaluated according to maturational age v chronological age. Using this information, a predictive model was generated to develop realistic performance benchmarks and support athlete development. METHODS: 10m-sprint and anthropometric data were extracted from the Swiss Football Association’s online database, ranging from 2014 - 2024. Initially, data from 5115 soccer players (f = 223, m = 4892), aged 9-19 years, with corresponding maturity status (Mirwald) estimates were examined to generate percentile curves, using Lambda-Mu-Sigma (LMS). Then, following screening for longitudinal (3+ measures) only, data on 1881 (f = 63, m = 1818) were examined to identify performance trajectories, generate predictions, and illustrate individualized trajectories, applying Linear Mixed-effects Models (LMMs). RESULTS: LMS percentile curves based on chronological and biological age were compared and showed significant differences (t(1013) = 1.21, p < 0.001, d = 0.39) in percentile rank improvement for late (v earlier) maturing male players; illustrating differences in performance percentile curves according to maturational age. Similar results were not observed in females, likely due to sample limitations. LMMs identified individualized longitudinal performance trajectories and generated future performance estimates according to chronological and biological age. Finally, a web-based Shiny-App development facilitated evaluation of current performance and predicted future performance relative to others. CONCLUSION: Percentile ranking improvement of later developing males based on biological v chronological age provides added value in 10m-sprint time assessment. LMMs deployed on longitudinal 10m-sprint data, facilitated the establishment of performance benchmarks and future performance trajectories . Incorporating biological age into modelling allowed individual maturational differences to be better considered, potentially improving predictive accuracy during adolescence. Shiny-App development based on data could provide coaches with maturational considered training objectives, identify likely performance trajectories, and offering a more individualized and maturation-sensitive approach to athlete development and talent identification. 1. Newans et al. (2022)
Read CV Julia HernandezECSS Paris 2023: OP-AP12