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Speed skating is a competitive form of ice skating in which the athletes race each other at distances from 500 to 10000 m. In speed skating the main components of specific fitness include acceleration, anaerobic sprint ability and explosive power of the lower extremities. A specific starting pattern in speed skating affects the relationships between strength of individual muscle groups. The main aim of this study was to analyse the pattern of internal structure of specific speed skating movements depending on their velocity – % PSE. The study examined six female athletes from the Polish National Speed Skating Team. The results of muscle activity index (4/7 and 4/10 PES mean) revealed statistically significant differences for the GM (Z=2.36; p=0.017), A (Z=2.02; p=0.04) and BF (Z=2.20; p=0.027) muscles respectively. The same analysis was performed for differences between peak activities at intensities of 4/7 PES and 4/10 PES. The results revealed statistically significant differences for the TFL (Z=2.52; p=0.011) and A (Z=2.20; p=0.027) and BF (Z=2.36; p=0.017) muscles. The results obtained in this study show that the more effective use of Gluteus Medius (GM), Adductor (A), Biceps Femoris (BF) and Tensor Fasciae Latae (TFL) muscles substantially improves starting speed in speedskating.
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Predicting competitive swimming performance

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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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