Powerlifting at junior level : selection paradigm
Deutscher übersetzter Titel: | Kraftdreikampf auf Junioren-Niveau : Auswahlmuster |
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Autor: | Płóciennik, Łukasz; Rygula, Igor |
Erschienen in: | icSPORTS 2013 : proceedings of the International Congress on Sports Science Research and Technology Support ; September 20-22, 2013, in Vilamoura, Algarve, Portugal |
Veröffentlicht: | Cham: Science and Technology Publications (Verlag), 2013, S. 116-123, Lit. |
Herausgeber: | International Congress on Sports Science Research and Technology Support |
Format: | Literatur (SPOLIT) |
Publikationstyp: | Sammelwerksbeitrag |
Medienart: | Elektronische Ressource (online) Gedruckte Ressource |
Dokumententyp: | Tagungsband |
Sprache: | Englisch |
DOI: | 10.5220/0004605601160123 |
Schlagworte: | |
Online Zugang: | |
Erfassungsnummer: | PU201609006452 |
Quelle: | BISp |
Abstract des Autors
The variability of the sport result obtained in powerlifting (PL) causes a few profound problems within coaching practice. One of them is the issue that concerns assigning individuals to particular training group of fitness level. Simply, this process is called selection. Since PL does not have any scientific-based selection algorithm we reckon, it is necessary to project it, with the idea to rationale the procedure. Thus the aims of the study were to construct discriminant and classification functions. A group of thirty-two powerlifters was selected for the investigation (22,397 yr +/- 0,826). The average sport result was 331,449 +/- 41,959 Wilks Points. Observation method and diagnostic survey were used to collect the data. During the course of the multidimensional statistical analysis, Hellwig’s algorithm, multiple regression, and discriminant analysis were utilised. The distances between stratified subdivisions of athletes were maintained in 99%. The classification matrix of young powerlifting contestants indicates that all the athletes were grouped adequately. Finally, for junior age category in PL, classification functions assign individuals to specified subgroups statistically better than a priori rule.