PROFILING OF AGE-RELATED DEVELOPMENT OF METABOLIC PERFORMANCE DETERMINANTS IN ELITE YOUTH SOCCER PLAYERS: CONVENTIONAL STATISTICS AND CONTRIBUTIONS OF MACHINE LEARNING

Publikationen: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitrag - Abstract in KonferenzbandForschungBegutachtung

Standard

PROFILING OF AGE-RELATED DEVELOPMENT OF METABOLIC PERFORMANCE DETERMINANTS IN ELITE YOUTH SOCCER PLAYERS: CONVENTIONAL STATISTICS AND CONTRIBUTIONS OF MACHINE LEARNING. / Ji, Sanghyeon; Broich, Holger; Mester, Joachim et al.

Book of Abstracts of the 27th Annual Congress of the European College of Sport Science: 30 August-2 September 2022. Hrsg. / F. Dela; M.F. Piacentini; J.W. Helge; A. Calvo Lluch; E. Sáez; F. Pareja Blanco; E. Tsolakidis. Sevilla : ECSS, 2022. S. 247-248.

Publikationen: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitrag - Abstract in KonferenzbandForschungBegutachtung

Harvard

Ji, S, Broich, H, Mester, J, Wigger, U & Wahl, P 2022, PROFILING OF AGE-RELATED DEVELOPMENT OF METABOLIC PERFORMANCE DETERMINANTS IN ELITE YOUTH SOCCER PLAYERS: CONVENTIONAL STATISTICS AND CONTRIBUTIONS OF MACHINE LEARNING. in F Dela, MF Piacentini, JW Helge, A Calvo Lluch, E Sáez, F Pareja Blanco & E Tsolakidis (Hrsg.), Book of Abstracts of the 27th Annual Congress of the European College of Sport Science: 30 August-2 September 2022. ECSS, Sevilla , S. 247-248, Annual Congress of the
European College of Sport Science, Sevilla, Spanien, 30.08.22. <https://www.ecss.mobi/DATA/CONGRESSES/SEVILLA_2022/DOCUMENTS/BOA_WEB.pdf>

APA

Ji, S., Broich, H., Mester, J., Wigger, U., & Wahl, P. (2022). PROFILING OF AGE-RELATED DEVELOPMENT OF METABOLIC PERFORMANCE DETERMINANTS IN ELITE YOUTH SOCCER PLAYERS: CONVENTIONAL STATISTICS AND CONTRIBUTIONS OF MACHINE LEARNING. in F. Dela, M. F. Piacentini, J. W. Helge, A. Calvo Lluch, E. Sáez, F. Pareja Blanco, & E. Tsolakidis (Hrsg.), Book of Abstracts of the 27th Annual Congress of the European College of Sport Science: 30 August-2 September 2022 (S. 247-248). ECSS. https://www.ecss.mobi/DATA/CONGRESSES/SEVILLA_2022/DOCUMENTS/BOA_WEB.pdf

Vancouver

Ji S, Broich H, Mester J, Wigger U, Wahl P. PROFILING OF AGE-RELATED DEVELOPMENT OF METABOLIC PERFORMANCE DETERMINANTS IN ELITE YOUTH SOCCER PLAYERS: CONVENTIONAL STATISTICS AND CONTRIBUTIONS OF MACHINE LEARNING. in Dela F, Piacentini MF, Helge JW, Calvo Lluch A, Sáez E, Pareja Blanco F, Tsolakidis E, Hrsg., Book of Abstracts of the 27th Annual Congress of the European College of Sport Science: 30 August-2 September 2022. Sevilla : ECSS. 2022. S. 247-248

Bibtex

@inbook{e118a271175044d19979398cd490e6d4,
title = "PROFILING OF AGE-RELATED DEVELOPMENT OF METABOLIC PERFORMANCE DETERMINANTS IN ELITE YOUTH SOCCER PLAYERS: CONVENTIONAL STATISTICS AND CONTRIBUTIONS OF MACHINE LEARNING",
abstract = "INTRODUCTION:Performance diagnostic is an indispensable process for optimizing individual training programs and monitoring an athlete{\textquoteright}s development in all sports disciplines. Due to the high impact of biological maturation on performance development of young athletes, systematic and continuous physiological monitoring is even more important in this population than in senior athletes (Barker & Armstrong, 2010). However, data on physiological performance determinants and their time-course changes in young elite athletes is limited and rather complemented with data from untrained children and adolescents. Thus, this cross-sectional study aimed to explore physiological characteristics regarding endurance performance and the longitudinal development of elite youth soccer players based on chronological age and age groups.METHODS:221 male elite youth soccer players (17.0±2.9 yrs) performed an incremental step test till exhaustion (start at 2.8 m/s +0.4 m/s every 5 min), to determine physiological determinants of endurance performance such as running velocity at lactate thresholds (vLT1 & vLT2), maximal oxygen uptake (VO2max), oxygen cost of running (CR), and total distance covered (DTot). For each athlete, 1-10 tests were carried out in a period of 4 years (number of tests: U15 = 72, U16 = 136, U17 = 204, U19 = 201, U23 = 188). The data were organized in a “data warehouse” and used for conventional statistics and machine learning, such as clustering and decision trees.RESULTS:There was no significant difference between age groups (mean±SD) regarding VO2max (ml/kg/min; U15 = 55.5±3.8, U16 = 55.1±4.4, U17 = 54.6±4.0, U19 = 55.3±4.5, U23 = 53.7±4.1). In contrast, the key indicator of overall treadmill performance (i.e. DTot; m) increased with age: U15 = 4598±855, U16 = 5089±868, U17 = 5241±911, U19 = 5667±890, U23 = 5836±888. At the same time, improved CR (ml/kg/m) with increasing age was found: U15 = 0.241±0.019, U16 = 0.232±0.022, U17 = 0.224±0.017, U19 = 0.216±0.015, U23 = 0.208±0.014. A similar development pattern in these variables was also observed in the intra-individual analyses.Due to the large number of experimentally available variables an important consideration is the strategy of “dimension reduction” to gain a better understanding of complex key indicators. For this purpose, factor-analyses with varimax-rotation have been used. Two major factors with an explained variance of 68% were extracted, which can be associated with dominant aerobic (52%) and anaerobic energy supply (16%).CONCLUSION:Our results indicated an age-related improvement in CR without a change in VO2max resulting in improved endurance performance. Although the positive impact of growth and maturation cannot be ruled out (Ariens et al., 1997), the decreased metabolic demand of running (CR) per se can be associated with performance development in elite youth soccer players. Metabolic profiling and player grouping using machine-learning can help to better understand and improve an athlete{\textquoteright}s performance development.",
author = "Sanghyeon Ji and Holger Broich and Joachim Mester and Ulrike Wigger and Patrick Wahl",
year = "2022",
language = "Deutsch",
isbn = "978-3-9818414-5-9",
pages = "247--248",
editor = "F. Dela and M.F. Piacentini and Helge, {J.W. } and {Calvo Lluch}, A. and E. S{\'a}ez and {Pareja Blanco}, F. and E. Tsolakidis",
booktitle = "Book of Abstracts of the 27th Annual Congress of the European College of Sport Science",
publisher = "ECSS",
note = "Annual Congress of the<br/>European College of Sport Science, ECSS 2022 Sevilla ; Conference date: 30-08-2022 Through 02-09-2022",
url = "https://sport-science.org/index.php/congress/ecss-sevilla-2022",

}

RIS

TY - CHAP

T1 - PROFILING OF AGE-RELATED DEVELOPMENT OF METABOLIC PERFORMANCE DETERMINANTS IN ELITE YOUTH SOCCER PLAYERS: CONVENTIONAL STATISTICS AND CONTRIBUTIONS OF MACHINE LEARNING

AU - Ji, Sanghyeon

AU - Broich, Holger

AU - Mester, Joachim

AU - Wigger, Ulrike

AU - Wahl, Patrick

N1 - Conference code: 27

PY - 2022

Y1 - 2022

N2 - INTRODUCTION:Performance diagnostic is an indispensable process for optimizing individual training programs and monitoring an athlete’s development in all sports disciplines. Due to the high impact of biological maturation on performance development of young athletes, systematic and continuous physiological monitoring is even more important in this population than in senior athletes (Barker & Armstrong, 2010). However, data on physiological performance determinants and their time-course changes in young elite athletes is limited and rather complemented with data from untrained children and adolescents. Thus, this cross-sectional study aimed to explore physiological characteristics regarding endurance performance and the longitudinal development of elite youth soccer players based on chronological age and age groups.METHODS:221 male elite youth soccer players (17.0±2.9 yrs) performed an incremental step test till exhaustion (start at 2.8 m/s +0.4 m/s every 5 min), to determine physiological determinants of endurance performance such as running velocity at lactate thresholds (vLT1 & vLT2), maximal oxygen uptake (VO2max), oxygen cost of running (CR), and total distance covered (DTot). For each athlete, 1-10 tests were carried out in a period of 4 years (number of tests: U15 = 72, U16 = 136, U17 = 204, U19 = 201, U23 = 188). The data were organized in a “data warehouse” and used for conventional statistics and machine learning, such as clustering and decision trees.RESULTS:There was no significant difference between age groups (mean±SD) regarding VO2max (ml/kg/min; U15 = 55.5±3.8, U16 = 55.1±4.4, U17 = 54.6±4.0, U19 = 55.3±4.5, U23 = 53.7±4.1). In contrast, the key indicator of overall treadmill performance (i.e. DTot; m) increased with age: U15 = 4598±855, U16 = 5089±868, U17 = 5241±911, U19 = 5667±890, U23 = 5836±888. At the same time, improved CR (ml/kg/m) with increasing age was found: U15 = 0.241±0.019, U16 = 0.232±0.022, U17 = 0.224±0.017, U19 = 0.216±0.015, U23 = 0.208±0.014. A similar development pattern in these variables was also observed in the intra-individual analyses.Due to the large number of experimentally available variables an important consideration is the strategy of “dimension reduction” to gain a better understanding of complex key indicators. For this purpose, factor-analyses with varimax-rotation have been used. Two major factors with an explained variance of 68% were extracted, which can be associated with dominant aerobic (52%) and anaerobic energy supply (16%).CONCLUSION:Our results indicated an age-related improvement in CR without a change in VO2max resulting in improved endurance performance. Although the positive impact of growth and maturation cannot be ruled out (Ariens et al., 1997), the decreased metabolic demand of running (CR) per se can be associated with performance development in elite youth soccer players. Metabolic profiling and player grouping using machine-learning can help to better understand and improve an athlete’s performance development.

AB - INTRODUCTION:Performance diagnostic is an indispensable process for optimizing individual training programs and monitoring an athlete’s development in all sports disciplines. Due to the high impact of biological maturation on performance development of young athletes, systematic and continuous physiological monitoring is even more important in this population than in senior athletes (Barker & Armstrong, 2010). However, data on physiological performance determinants and their time-course changes in young elite athletes is limited and rather complemented with data from untrained children and adolescents. Thus, this cross-sectional study aimed to explore physiological characteristics regarding endurance performance and the longitudinal development of elite youth soccer players based on chronological age and age groups.METHODS:221 male elite youth soccer players (17.0±2.9 yrs) performed an incremental step test till exhaustion (start at 2.8 m/s +0.4 m/s every 5 min), to determine physiological determinants of endurance performance such as running velocity at lactate thresholds (vLT1 & vLT2), maximal oxygen uptake (VO2max), oxygen cost of running (CR), and total distance covered (DTot). For each athlete, 1-10 tests were carried out in a period of 4 years (number of tests: U15 = 72, U16 = 136, U17 = 204, U19 = 201, U23 = 188). The data were organized in a “data warehouse” and used for conventional statistics and machine learning, such as clustering and decision trees.RESULTS:There was no significant difference between age groups (mean±SD) regarding VO2max (ml/kg/min; U15 = 55.5±3.8, U16 = 55.1±4.4, U17 = 54.6±4.0, U19 = 55.3±4.5, U23 = 53.7±4.1). In contrast, the key indicator of overall treadmill performance (i.e. DTot; m) increased with age: U15 = 4598±855, U16 = 5089±868, U17 = 5241±911, U19 = 5667±890, U23 = 5836±888. At the same time, improved CR (ml/kg/m) with increasing age was found: U15 = 0.241±0.019, U16 = 0.232±0.022, U17 = 0.224±0.017, U19 = 0.216±0.015, U23 = 0.208±0.014. A similar development pattern in these variables was also observed in the intra-individual analyses.Due to the large number of experimentally available variables an important consideration is the strategy of “dimension reduction” to gain a better understanding of complex key indicators. For this purpose, factor-analyses with varimax-rotation have been used. Two major factors with an explained variance of 68% were extracted, which can be associated with dominant aerobic (52%) and anaerobic energy supply (16%).CONCLUSION:Our results indicated an age-related improvement in CR without a change in VO2max resulting in improved endurance performance. Although the positive impact of growth and maturation cannot be ruled out (Ariens et al., 1997), the decreased metabolic demand of running (CR) per se can be associated with performance development in elite youth soccer players. Metabolic profiling and player grouping using machine-learning can help to better understand and improve an athlete’s performance development.

UR - https://www.mendeley.com/catalogue/42287c1b-3016-37e4-9dd0-452d120c6bcf/

M3 - Konferenzbeitrag - Abstract in Konferenzband

SN - 978-3-9818414-5-9

SP - 247

EP - 248

BT - Book of Abstracts of the 27th Annual Congress of the European College of Sport Science

A2 - Dela, F.

A2 - Piacentini, M.F.

A2 - Helge, J.W.

A2 - Calvo Lluch, A.

A2 - Sáez, E.

A2 - Pareja Blanco, F.

A2 - Tsolakidis, E.

PB - ECSS

CY - Sevilla

T2 - Annual Congress of the<br/>European College of Sport Science

Y2 - 30 August 2022 through 2 September 2022

ER -

ID: 8023000