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246. Effects of four weeks of repeated sprint training on physiological indices in futsal players.

Author

do Nascimento, Paulo Cesar, De Lucas, Ricardo Dantas, Dal Pupo, Juliano, Arins, Francimara Budal, Castagna, Carlo, and Antonacci Guglielmo, Luiz Guilherme

Subjects
PHYSICAL training & conditioning, ANALYSIS of variance, FOOTBALL, MULTIVARIATE analysis, RUNNING, PRE-tests & post-tests, EDUCATIONAL outcomes, DATA analysis software, STATISTICAL models, DESCRIPTIVE statistics
Abstract

Copyright of Brazilian Journal of Kineanthropometry & Human Performance is the property of Brazilian Journal of Kineanthropometry & Human Performance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2015
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247. CLASSIFYING YOUNG SOCCER PLAYERS BY TRAINING PERFORMANCES.

Author

ABADE, EDUARDO A., GONÇALVES, BRUNO V., SILVA, ALEXANDRA M., LEITE, NUNO M., CASTAGNA, CARLO, and SAMPAIO, JAIME E.

Subjects
ACCELEROMETERS, ANALYSIS of variance, CLUSTER analysis (Statistics), GEOGRAPHIC information systems, HEART beat, SOCCER, ELITE athletes, REPEATED measures design, DATA analysis software, DESCRIPTIVE statistics, ADOLESCENCE
Abstract

Players within the same age group may present different physical and physiological profiles. This study classified young soccer players according to their physical and physiological profiles obtained during the training sessions and compared classification by age and playing position criteria. 151 male elite Portuguese soccer players (under 15, under 17, and under 19 years old) participated. Time-motion and body acceleration and deceleration data were collected using GPS technology with heart rate monitored continuously across the selected training sessions. The data were grouped using two-step cluster analysis to classify athletes. A repeated-measures factorial ANOVA was performed to identify differences in the variables. Three clusters comprised 15.2%, 37.1%, and 47.7% of the total sample, respectively. Players of the same ages and playing experience had different performance profiles. Grouping players with similar physiological profiles during training sessions may allow coaches to balance oppositions and reduce the variability of the physiological outcomes. [ABSTRACT FROM AUTHOR]

Published
2014
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