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Computational Intelligence and Neuroscience
Volume 2015, Article ID 735060, 9 pages
http://dx.doi.org/10.1155/2015/735060
Research Article

Predictive Modeling in Race Walking

1Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland
2Faculty of Physical Education, University of Rzeszów, 35-959 Rzeszów, Poland

Received 15 December 2014; Accepted 18 June 2015

Academic Editor: Okyay Kaynak

Copyright © 2015 Krzysztof Wiktorowicz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. O. Bompa and G. Haff, Periodization: Theory and Methodology of Training, Human Kinetics, Champaign, Ill, USA, 1999.
  2. A. Maszczyk, A. Zając, and I. Ryguła, “A neural network model approach to athlete selection,” Sports Engineering, vol. 13, no. 2, pp. 83–93, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Przednowek and K. Wiktorowicz, “Prediction of the result in race walking using regularized regression models,” Journal of Theoretical and Applied Computer Science, vol. 7, no. 2, pp. 45–58, 2013. View at Google Scholar
  4. V. Papić, N. Rogulj, and V. Pleština, “Identification of sport talents using a web-oriented expert system with a fuzzy module,” Expert Systems with Applications, vol. 36, no. 5, pp. 8830–8838, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Roczniok, A. Maszczyk, A. Stanula et al., “Physiological and physical profiles and on-ice performance approach to predict talent in male youth ice hockey players during draft to hockey team,” Isokinetics and Exercise Science, vol. 21, no. 2, pp. 121–127, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Haghighat, H. Rastegari, N. Nourafza, N. Branch, and I. Esfahan, “A review of data mining techniques for result prediction in sports,” Advances in Computer Science, vol. 2, no. 5, pp. 7–12, 2013. View at Google Scholar
  7. K. Przednowek and K. Wiktorowicz, “Neural system of sport result optimization of athletes doing race walking,” Metody Informatyki Stosowanej, vol. 29, no. 4, pp. 189–200, 2011 (Polish). View at Google Scholar
  8. A. Drake and R. James, “Prediction of race walking performance via laboratory and field tests,” New Studies in Athletics, vol. 23, no. 4, pp. 35–41, 2009. View at Google Scholar
  9. P. Chatterjee, A. K. Banerjee, P. Dasb, and P. Debnath, “A regression equation to predict VO2 max of young football players of Nepal,” International Journal of Applied Sports Sciences, vol. 21, no. 2, pp. 113–121, 2009. View at Google Scholar
  10. B. Ofoghi, J. Zeleznikow, C. MacMahon, and M. Raab, “Data mining in elite sports: a review and a framework,” Measurement in Physical Education and Exercise Science, vol. 17, no. 3, pp. 171–186, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. E. Mężyk and O. Unold, “Machine learning approach to model sport training,” Computers in Human Behavior, vol. 27, no. 5, pp. 1499–1506, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Pfeiffer and A. Hohmann, “Applications of neural networks in training science,” Human Movement Science, vol. 31, no. 2, pp. 344–359, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. I. Ryguła, “Artificial neural networks as a tool of modeling of training loads,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 2985–2988, IEEE, September 2005. View at Scopus
  14. A. J. Silva, A. M. Costa, P. M. Oliveira et al., “The use of neural network technology to model swimming performance,” Journal of Sports Science and Medicine, vol. 6, no. 1, pp. 117–125, 2007. View at Google Scholar · View at Scopus
  15. A. Maszczyk, R. Roczniok, Z. Waśkiewicz et al., “Application of regression and neural models to predict competitive swimming performance,” Perceptual and Motor Skills, vol. 114, no. 2, pp. 610–626, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, NY, USA, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  17. G. S. Maddala, Introduction to Econometrics, Wiley, Chichester, UK, 2001.
  18. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, NY, USA, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  19. A. E. Hoerl and R. W. Kennard, “Ridge regression: biased estimation for nonorthogonal problems,” Technometrics, vol. 12, no. 1, pp. 55–67, 1970. View at Google Scholar
  20. R. Tibshirani, “Regression shrinkage and selection via the Lasso,” Journal of the Royal Statistical Society, Series B: Methodological, vol. 58, no. 1, pp. 267–288, 1996. View at Google Scholar · View at MathSciNet
  21. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” The Annals of Statistics, vol. 32, no. 2, pp. 407–499, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society—Series B: Statistical Methodology, vol. 67, no. 2, pp. 301–320, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. S. Arlot and A. Celisse, “A survey of cross-validation procedures for model selection,” Statistics Surveys, vol. 4, pp. 40–79, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  24. R Development Core Team, R: A Language and Environment for Statistical Computing, R Development Core Team, Vienna, Austria, 2011.
  25. B. Ripley, B. Venables, K. Hornik, A. Gebhardt, and D. Firth, Package “MASS”, Version 7.3–20, CRAN, 2012.
  26. H. Zou and T. Hastie, Package “elasticnet”, version 1.1, CRAN, 2012.
  27. R Development Core Team and Contributors Worldwide, The R “Stats” Package, R Development Core Team, Vienna, Austria, 2011.
  28. StatSoft, Statistica (Data Analysis Software System), Version 10, StatSoft, 2011.