Within-Subject Principal Component Analysis of External Training Load and Intensity Measures in Youth Soccer Training.
Journal
Journal of strength and conditioning research
ISSN: 1533-4287
Titre abrégé: J Strength Cond Res
Pays: United States
ID NLM: 9415084
Informations de publication
Date de publication:
01 Dec 2023
01 Dec 2023
Historique:
medline:
30
11
2023
pubmed:
28
11
2023
entrez:
28
11
2023
Statut:
ppublish
Résumé
Marynowicz, J, Lango, M, Horna, D, Kikut, K, Konefał, M, Chmura, P, and Andrzejewski, M. Within-participant principal component analysis of external training load and intensity measures in youth soccer training. J Strength Cond Res 37(12): 2411-2416, 2023-The aim of this study was to identify which combination of external training load (EL) and external intensity (EI) metrics during youth soccer training sessions captured similar or unique information. Data were collected from 18 youth soccer players during an 18-week in-season competition period using a 10-Hz global positioning system, rating of perceived exertion (RPE), and session-RPE (sRPE). External training load measures included total distance (TD, in meters), PlayerLoad (PL, in arbitrary units), high-speed running distance (HSR, in meters), and number of accelerations (ACC, n). All EL metrics were also divided by session duration (minutes) to obtain EI values. A total of 804 training observations were undertaken (43 ± 17 sessions per player). The analysis was performed by use of the principal component analysis technique. The first principal component (PC) captured 49-70% and 68-89% of the total variance in EI and EL, respectively. The findings show that from the 5 EI metrics, most of the information can be explained by either TD per minute or PL per minute, with a loading from 0.87 to 0.98 and from 0.76 to 0.95, respectively. The majority of EL information can be explained by PL (loading: 0.93-0.98), TD (loading: 0.95-0.99), ACC (loading: 0.71-0.91), or sRPE (loading: 0.70-0.93). The second PC for EL metrics is most strongly correlated with HSR, with loadings from 0.53 to 0.84. The results suggest that the majority of the information contained in the EL variables can be captured in 1 PC without losing much information. The findings suggest that stakeholders who intend to provide a fast and holistic view of EL information in a daily training environment should report TD, PL, ACC, or sRPE plus HSR to coaching staff as a metrics that provides additional unique information.
Identifiants
pubmed: 38015730
doi: 10.1519/JSC.0000000000004545
pii: 00124278-202312000-00013
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2411-2416Informations de copyright
Copyright © 2023 National Strength and Conditioning Association.
Références
Akenhead R, Nassis GP. Training load and player monitoring in high-level football: Current practice and perceptions. Int J Sports Physiol Perform 11: 587–593, 2016.
Bourdon PC, Cardinale M, Murray A, et al. Monitoring athlete training loads: Consensus statement. Int J Sports Physiol Perform 12: S2–S161, 2017.
Buchheit M, Simpson BM. Player-tracking technology: Half-full or half-empty glass? Int J Sports Physiol Perform 12: S2–S35, 2017.
Callanan D, Rankin P, Fitzpatrick P. An analysis of the game movement demands of women's interprovincial rugby union. J Strength Cond Res 35: S20–S25, 2021.
Casamichana D, Castellano J, Calleja-Gonzalez J, San Román J, Castagna C. Relationship between indicators of training load in soccer players. J Strength Cond Res 27: 369–374, 2013.
Casamichana D, Castellano J, Gómez Díaz A, Martín-García A. Looking for complementary intensity variables in different training games in football. J Strength Cond Res 2019. Online ahead of print.
Coppus TA, Anderson T, Hurley E, Gill DL, Brown PK. The practical utility of objective training load indices in Division I college soccer players. J Strength Cond Res 36: 1026–1030, 2022.
Coutts A, Kempton T, Crowcroft S. Developing athlete monitoring systems: Theoretical basis and practical applications. In: Sport, Recovery and Performance: Interdisciplinary Insights. Kellmann M, Beckmann J, eds. London, United Kingdom: Routledge, 2018. pp. 19–32.
Coutts AJ. Working fast and working slow: The benefits of embedding research in high-performance sport. Int J Sports Physiol Perform 11: 1–2, 2016.
Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res 15: 109–115, 2001.
Gaudino P, Iaia FM, Strudwick AJ, et al. Factors influencing perception of effort (session rating of perceived exertion) during elite soccer training. Int J Sports Physiol Perform 10: 860–864, 2015.
Hader K, Rumpf MC, Hertzog M, et al. Monitoring the athlete match response: Can external load variables predict post-match acute and residual fatigue in soccer? A systematic review with meta-analysis. Sports Med Open 5: 48, 2019.
Hair JF. Multivariate Data Analysis: A Global Perspective (7th ed.). Upper Saddle River, NJ: Pearson Education, 2010.
Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med 44: 139–147, 2014.
Herman L, Foster C, Maher M, Mikat R, Porcari J. Validity and reliability of the session RPE method for monitoring exercise training intensity. S Afr J Sports Med 18: 14, 2006.
Houtmeyers KC, Jaspers A, Figueiredo P. Managing the training process in elite sports: From descriptive to prescriptive data analytics. Int J Sports Physiol Perform 16: 1719–1723, 2021.
Houtmeyers KC, Vanrenterghem J, Jaspers A, et al. Load monitoring practice in European elite football and the impact of club culture and financial resources. Front Sports Act Living 3: 679824, 2021.
Impellizzeri FM, Marcora SM, Coutts AJ. Internal and external training load: 15 years on. Int J Sports Physiol Perform 14: 270–273, 2019.
Kassambara A, Mundt F. Factoextra: Extract and visualize the results of multivariate data analyses. R package version. 1: 337–354, 2017. Available at: http://ftp.udc.es/CRAN/web/packages/factoextra/index.html. Accessed February 8, 2022.
Lorenzo-Martínez M, de Dios-Álvarez VM, Padrón-Cabo A, Costa PB, Rey E. Effects of score-line on internal and external load in soccer small-sided games. Int J Perform Anal Sport 20: 231–239, 2020.
Malone JJ, Lovell R, Varley MC, Coutts AJ. Unpacking the black box: Applications and considerations for using GPS devices in sport. Int J Sports Physiol Perform 12: S218–S226, 2017.
Marynowicz J, Kikut K, Lango M, Horna D, Andrzejewski M. Relationship between the session-RPE and external measures of training load in youth soccer training. J Strength Cond Res 34: 2800–2804, 2020.
Marynowicz J, Lango M, Horna D, Kikut K, Andrzejewski M. Predicting ratings of perceived exertion in youth soccer using decision tree models. Biol Sport 39: 245–252, 2022.
Maughan P, Swinton P, MacFarlane N. Relationships between training load variables in professional youth football players. Int J Sports Med 42: 624–629, 2021.
Maughan PC, MacFarlane NG, Swinton PA. Relationship between subjective and external training load variables in youth soccer players. Int J Sports Physiol Perform 16: 1127–1133, 2021.
McLaren SJ, Macpherson TW, Coutts AJ, et al. The relationships between internal and external measures of training load and intensity in team sports: A meta-analysis. Sports Med 48: 641–658, 2018.
O'Donoghue P. Principal components analysis in the selection of key performance indicators in sport. Int J Perform Anal Sport 8: 145–155, 2008.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2021. Available at: https://www.R-project.org/. Accessed February 8, 2022.
Robertson S, Bartlett JD, Gastin PB. Red, amber, or green? Athlete monitoring in team sport: The need for decision-support systems. Int J Sports Physiol Perform 12: S2–S73, 2017.
Rojas-Valverde D, Gómez-Carmona CD, Gutiérrez-Vargas R, Pino-Ortega J. From big data mining to technical sport reports: The case of inertial measurement units. BMJ Open Sport Exerc Med 5: e000565, 2019.
Rojas-Valverde D, Pino-Ortega J, Gómez-Carmona CD, Rico-González M. A systematic review of methods and criteria standard proposal for the use of principal component analysis in team's sports science. Int J Environ Res Public Health 17: 8712, 2020.
Scantlebury S, Till K, Beggs C, et al. Achieving a desired training intensity through the prescription of external training load variables in youth sport: More pieces to the puzzle required. J Sports Sci 38: 1124–1131, 2020.
Thornton HR, Delaney JA, Duthie GM, Dascombe BJ. Developing athlete monitoring systems in team sports: Data analysis and visualization. Int J Sports Physiol Perform 14: 698–705, 2019.
Tierney P, Malone S, Delahunt E. High-speed running density: A new concept. RTS 33: 1–4, 2018.
Weaving D, Beggs C, Dalton-Barron N, Jones B, Abt G. Visualizing the complexity of the athlete-monitoring cycle through principal-component analysis. Int J Sports Physiol Perform 14: 1304–1310, 2019.
Weaving D, Dalton NE, Black C, et al. The same story or a unique novel? Within-participant principal-component analysis of measures of training load in professional rugby union skills training. Int J Sports Physiol Perform 13: 1175–1181, 2018.
Weston M. Training load monitoring in elite English soccer: A comparison of practices and perceptions between coaches and practitioners. Sci Med Footb 2: 216–224, 2018.
Zurutuza U, Castellano J, Echeazarra I, Guridi I, Casamichana D. Selecting training-load measures to explain variability in football training games. Front Psychol 10: 2897, 2019.