Central European Journal of Sport Sciences and Medicine
Predicting Competitive Swimming Performance
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| ISSN: | 2300-9705 |
| eISSN: | 2353-2807 |
| Autorzy: |
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 |
| Słowa kluczowe: | Kohonen feature map regression sports selection swimming performance |
| Data wydania numeru / monografii: | 2015 |
| Liczba stron: | 8 (105-112) |
| OAI-PMH: | Metadane |
 
 
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Abstrakt
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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Bibliografia (26)
- Abbot A., Collins D. A theoretical and empirical analysis of a ‘State of the Art’ talent identification model. High Ability Studies. 2012; 13 (2): 157–178.
- Abbot A., Collins D. Eliminating the dichotomy between theory and practice in talent identification and development: considering the role of psychology. Journal of Sport Sciences. 2004; 22 (5): 395–408.
- Bartlett R. Artificial intelligence in sports biomechanics. New dawn or false hope? Journal of Sports Science and Medicine. 2006; 5: 474–479.
- Beek P.J., Beek W.J. Tools for constructing dynamical models of rhythmic movement. Hum Mov Sci. 1998; 7: 301–342
- Dutt-Mazumder A., Button C., Robins A., Bartlett R. Neural Network Modelling and Dynamical System Theory. Sports Med. 2011; 41 (12): 1003–1017.
- Gagne F. Giftedness and talent: Reexamining a reexamination of the definitions. Gifted Child Quarterly. 1985; 19: 103–112
- Glazier P.S. Game, set and match? Substantive issues and future directions in performance analysis. Sports Med. 2010; 40 (8): 625–34.
- Haykin S. Neuralnetworks, a comprehensive foundation. Macmillan College Publishing Company. New York 1994
- Haykin S. Neural networks: a comprehensive foundation. 2nd ed. Prentice-Hall Inc. Upper Saddle River (NJ) 1999
- Hohmann A., Seidel I. Scientific Aspects of Talent Development. International Journal of Physical Education. 2003; 40 (1): 9–20
- Kohonen T. Self-organizing maps. New York: Springer 1997
- Konen W., Maurer T., Von der Malsburg C. A fast dynamic link matching algorithm for invariant pattern recognition. Neural Networks. 1994; 7: 1019–1030.
- Lees A. Technique analysis in sports: a critical review. Journal of Sports Sciences. 2002; 20: 813–828
- Leondes C.T. Algorithm and architectures. San Diego (CA); Academic Press 1998
- Lippmann RP. An introduction to computing with neural nets. IEEE ASSP Magazine 1987; 4 (3): 422, h t t p: // h a w k .c s .c s u c i .e d u / W i l l i a m .Wo l f e / U C D /e n g i n e e r i n g /c s e / G r a d u a t e /c o u r s e s / C S C 5 5 42 / L
- Maszczyk A., Zając A., Ryguła I. A neural network model approach to athlete selection. Sports Engineering. 2011; 13 (2): 83–93.
- Maszczyk A., Roczniok R., Waśkiewicz Z., Czuba M., Mikołajec K., Zając A., Stanula A. Application of Regression and Neural Models to Predict competitive swimming performance. Perceptual and Motor Skills. 2012; 114 (2): 610–626.
- Mester J., Perl J. Unconventional simulation and empirical evaluation of biological response to complex high training loads. Sports Science, Rome 1999.
- Murakami M., Tanabe S., Ishikawa M., Isolehto J., Komi V., Ito A. Biomechanical analysis of the javelin throwing at 11th World Championships in Athletics in Helsinki. International Association of Athletics Federation. New Studies in Athletics. 4. 2005.
- Perl J., Lames M., Glitsh U. Modellbildung in der sport-wissenschaft. Beitrage zr Lehr und Forchung im Sport, Bd 132, Schorndorf, Hofmann. 2002.
- Perl J., Dauscher P. Dynamic pattern recognition in sport by means of artificial neural networks. In: Begg R. Palaniswami M. editors. Computational intelligence for movement science. Hershey (PA): Idea Group Publishing. 2006: 299–318.
- Philippaerts R.M, Coutts A., Vaeyens R. Physiological Perspectives on the Identification and Development of Talented Performers in Sport. In: Talent Identification and Development. The Search for Sporting Excellence, eds. R. Fisher, R. Bailey. Berlin: ICS
- Roczniok R., Ryguła I., Kwaśniewska A. The use Kohonen’s neural networks in the recruitment process for sport swimming. Journal of Human Kinetics. 2007; 17: 75–88.
- Stergiou N., Buzzi L., Kurz MJ. Nonlinear tools in human movement. In: Innovative analysis of human movement, ed. N. Stergiou. Champaign (IL): Human Kinetics. 2004: 66–77
- Tidow G. Challenge decathlon – barriers on the way to becoming the “king of athletes”. Part I. International Association of Athletics Federation. New Studies in Athletics. 2000; 15
- Zadeh L. From computing with numbers to computing with words. International Journal of Applied Math and Computer Science. 2002; 12 (3): 307–324.