ISSN: 2300-9705
eISSN: 2353-2807
OAI
Issue archive /
Vol. 9, No. 1/2015
Predicting Competitive Swimming Performance
| Authors: |
Olga
Fidos-Czuba
Phd student. The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland Krzysztof Kozłowski Phd student. The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland Adam Maszczyk Department of Statistics and Methodology, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland Robert Roczniok Department of Statistics and Methodology, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland Łukasz Rutkowski Phd student. The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland Arkadiusz Stanula Department of Statistics and Methodology, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland Robert Wilk Department of Water Sports, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland Piotr Wiśniewski Phd student. The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland |
| Keywords: | Kohonen feature map regression sports selection swimming performance |
| Data publikacji całości: | 2015 |
| Page range: | 8 (105-112) |
Abstract
The aim of this study was to present the results of analyses conducted by means of complementary analytic tools in order to verify their efficacy and the hypothesis that Kohonen’s neural models may be applied in the classification process of swimmers. A group of 40 swimmers, aged 23 ±5 years took part in this research. For the purpose of verification of usefulness of Kohonen’s neural models, statistical analyses were carried out on the basis of results of the independent variables (physiological and physical profiles, specific tests in the water). In predicting the value of variables measured with the so called strong scale regression models, numerous variables were used. The construction of such models required strict determination of the endogenous variable (Y – results for swim distances of 200 m crawl), as well as the proper choice of variables in explaining the study’s phenomenon. The optimum choice of explanatory variables for the Kohonen’s networks was made on the grounds of regression analysis. During statistical analysis of the gathered material neural networks were used: Kohonen’s feature maps (data mining analysis). The obtained model has the form of a topological map, where certain areas can be separated, and the map constructed in this way can be used in the assessment of candidates for sports training.
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