Typical weekly workload of under 15, under 17, and under 19 elite Portuguese football players
Deutscher übersetzter Titel: | Typische, wöchentliche Trainingsbelastung bei unter 15-, unter 17- und unter 19 -jährigen portugiesischen Fußballspielern |
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Autor: | Coutinho, Diogo; Gonçalves, Bruno; Figueira, Bruno; Abade, Eduardo; Marcelino, Rui; Sampaio, Jaime E. |
Erschienen in: | Journal of sports sciences |
Veröffentlicht: | 33 (2015), 12 (Science and Medicine in Football), S. 1229-1237, Lit. |
Format: | Literatur (SPOLIT) |
Publikationstyp: | Zeitschriftenartikel |
Medienart: | Elektronische Ressource (online) Gedruckte Ressource |
Sprache: | Englisch |
ISSN: | 0264-0414, 1466-447X |
DOI: | 10.1080/02640414.2015.1022575 |
Schlagworte: | |
Online Zugang: | |
Erfassungsnummer: | PU201506004242 |
Quelle: | BISp |
Abstract
This study aims to describe the time–motion and physiological performance profiles of footballers whose ages are under 15 (U15), under 17 (U17), and under 19 (U19) during a typical week of a competitive season. A total of 151 elite Portuguese players U15 (age 14.0 ± 0.2; n = 56), U17 (age 15.8 ± 0.4; n = 66), and U19 (age 17.8 ± 0.6; n = 19) were monitored during 33 training sessions (TSs) (U15 n = 12; U17 n = 11; and U19 n = 10 TSs). The TS data were captured at 15 Hz by global positioning systems devices and divided into post-match (session after the match), prematch (session before the match), and middle week (average of remaining sessions). The U15 middle week showed a higher number of sprints, distance covered in intermediate speed zones, and time spent above 90% HRmax, while the prematch presented a higher distance covered above 18 km · h−1 and time spent below 75% HRmax. In U17, both prematch and post-match data presented lower values than middle-week data in most of the variables. The post-match data in U19 presented higher values of distance covered above 13 km · h−1, body impacts above 10 G, and time spent above 85% HRmax, while middle week showed higher values in body impacts in most of the zones. In addition, the prematch data presented 35% to 100% less values than the middle-week data. Understanding the weekly workload variations according to the competition and the developmental ages of the players can contribute to optimising short- and mid-term planning. Verf.-Referat