NEURO-MUSCULAR DEVELOPMENT AND AGE-RELATED PROFILING IN ELITE YOUTH SOCCER PLAYERS: CONVENTIONAL STATISTICS AND CONTRIBUTIONS OF MACHINE LEARNING

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INTRODUCTION:
From a neuromuscular perspective, soccer is characterized by the need to move ones own body mass at high speed. In addition, rapid changes of direction and quick responses to visual stimuli are needed. For these reasons, performance diagnostics often include jumping, tapping, balance and sprint tests and are supplemented by tests on changes of direction and visual information processing. However, little is known about the development of these skills in youth elite athletes. Therefore, this cross-sectional study investigated neuromuscular performance tests of elite youth soccer players across different age groups.

METHODS:
232 male elite youth soccer players (17.3±2.8 yrs) performed neuro-muscular tests including counter-movement jump (CMJ), squat jump (SJ), drop jump (DJ), star run (StR), chase next (CN), tapping (Tap), balance (Ba) and sprinting (Spr). For each athlete, 1-11 tests were carried out in a period of 4 years (number of tests: U15 = 88, U16 = 168, U17 = 263, U19 = 229, U23 = 192). The data were organized in a “data warehouse” and used for conventional statistics and machine learning, such as clustering and decision trees.

RESULTS:
Conventional Statistics
There were significant differences between certain age groups (mean±SD) e.g. regarding CMJ, SJ, DJ, StR and Reactive Strength Index (RSI) as a key indicator for neuro-muscular reactivity. These differences, however, cannot statistically be related to biological age or age groups as the intraindividual or groupwise within variation is very high. So is the individual development in the context of soccer specific training. Thus an individual profiling is required.
Examples for groupwise results are: CMJ [cm]: U15 = 33.8±4.2, U16 = 36.6.1±4,6, U17 = 38.0±3.9, U19 = 40.6±4.5, U23 = 40.5±4.1. StR [s]: U15 = 18.4±1.2, U16 = 18.0.±1.1, U17 = 17.5±1.1, U19 = 17.3±1.2, U23 = 17.2±1.1). RSI [cm/ms]: U15 = 0.141±0.030, U16 = 0.148±0.033, U17 = 0.163±0.031, U19 = 0.182±0.035, U23 = 0.185±0.033).

Machine Learning
In order to obtain to gain a better understanding of complex key indicators a “dimension reduction” by means of factor-analyses with varimax-rotation was carried out. Two major factors with an explained variance of 39% were extracted, which can be associated with dominant reactive components (24%) and more coordinative features (15%).

CONCLUSION:
The data show that neuromuscular performance differs between age groups, but that this difference is not simply a function of age. It is conceivable that the difference between and high variance within age groups is the result of individual training stimuli, causing some individuals to stand out from the rest over time. It turns out that the interindividual variance is primarily based on two factors that can be attributed to reactive or coordinative abilities. These results will help athletic trainers to understand the development of the neuromuscular performance in youth athletes, set appropriate priorities in training, and identify talent.
OriginalspracheDeutsch
TitelBook of Abstracts of the 27th Annual Congress of the European College of Sport Science : 30 August-2 September 2022
Redakteure/-innenF. Dela, M.F. Piacentini, J.W. Helge, A. Calvo Lluch, E. Sáez, F. Pareja Blanco, E. Tsolakidis
Seitenumfang1
ErscheinungsortSevilla
Herausgeber (Verlag)ECSS
Erscheinungsdatum2022
Seiten77
ISBN (elektronisch) 978-3-9818414-5-9
PublikationsstatusVeröffentlicht - 2022
VeranstaltungAnnual Congress of the
European College of Sport Science
- Sevilla, Sevilla, Spanien
Dauer: 30.08.202202.09.2022
Konferenznummer: 27
https://sport-science.org/index.php/congress/ecss-sevilla-2022

ID: 8022880

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