A Fuzzy-based Software Tool Used to Predict 110m Hurdles Results During the Annual Training Cycle

Krzysztof Przednowek, Krzysztof Wiktorowicz, Tomasz Krzeszowski, Janusz Iskra

Abstract

This paper describes a fuzzy-based software tool for predicting results in the 110m hurdles. The predictive models were built on using 40 annual training cycles completed by 18 athletes. These models include: ordinary least squares regression, ridge regression, LASSO regression, elastic net regression and nonlinear fuzzy correction of least squares regression. In order to compare them, and choose the best model, leave-one-out cross-validation was used. This showed that the fuzzy corrector proposed in this paper has the lowest prediction error. The developed software can support a coach in planning an athlete's annual training cycle. It allows the athlete's results to be predicted, and in this way, for the best training loads to be selected. The tool is a web-based interactive application that can be run from a computer or a mobile device. The whole system was implemented using the R programming language with additional packages.

References

  1. Arlot, S. and Celisse, A. (2010). A survey of crossvalidation procedures for model selection. Statistics Surveys, 4:40-79.
  2. Edelmann-Nusser, J., Hohmann, A., and Henneberg, B. (2002). Modeling and prediction of competitive performance in swimming upon neural networks. European Journal of Sport Science, 2(2):1-10.
  3. Gabin, B., Camerino, O., Anguera, M. T., and Castañer, M. (2012). Lince: Multiplatform sport analysis software. Procedia - Social and Behavioral Sciences, 46:4692 - 4694.
  4. Gavojdea, A.-M. (2015). The impact of using the specialized software on studing the landings at uneven bars. In Conference proceedings of eLearning and Software for Education (eLSE 2015), number 3, pages 346- 352.
  5. Haghighat, M., Rastegari, H., and Nourafza, N. (2013). A review of data mining techniques for result prediction in sports. Advances in Computer Science: an International Journal, 2(5):7-12.
  6. Iskra, J. and Rygula, I. (2001). The optimization of training loads in high class hurdlers. Journal of Human Kinetics, 6:59-72.
  7. Krzeszowski, T., Przednowek, K., Wiktorowicz, K., and Iskra, J. (2016). Estimation of hurdle clearance parameters using a monocular human motion tracking method. Computer Methods in Biomechanics and Biomedical Engineering, 19(12):1319-1329. PMID: 26838547.
  8. Louzada, F., Maiorano, A. C., and Ara, A. (2016). iSports: A web-oriented expert system for talent identification in soccer. Expert Systems with Applications, 44:400 - 412.
  9. Maszczyk, A., Zaja?c, A., and Rygula, I. (2011). A neural network model approach to athlete selection. Sports Engineering, 13(2):83-93.
  10. Omorczyk, J., Nosiadek, L., Nosiadek, A., and Chwala, W. (2014). Use of biomechanical analysis for technical training in artistic gymnastics using the example of a back handspring. In Urbanik, C., Mastalerz, A., and IwaÁska, D., editors, Selected problems of biomechanics of sport and rehabilitation, volume II, pages 104- 115. Józef Pilsudski University of Physical Education in Warsaw.
  11. Papic, V., Rogulj, N., and Pleština, V. (2009). Identification of sport talents using a web-oriented expert system with a fuzzy module. Expert Systems with Applications, 36(5):8830 - 8838.
  12. Przednowek, K., Iskra, J., and Przednowek, K. H. (2014). Predictive modeling in 400-metres hurdles races. In 2nd Int. Congress on Sport Sciences Research and Technology Support - icSPORTS 2014, pages 137- 144. SCITEPRESS, Rome, Italy.
  13. R Core Team (2016). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  14. Randers, M. B., Mujika, I., Hewitt, A., Santisteban, J., Bischoff, R., Solano, R., Zubillaga, A., Peltola, E., Krustrup, P., and Mohr, M. (2010). Application of four different football match analysis systems: A comparative study. Journal of Sports Sciences, 28(2):171-182.
  15. Riza, L. S., Bergmeir, C., Herrera, F., and Benítez, J. M. (2015). frbs: Fuzzy rule-based systems for classification and regression in R. Journal of Statistical Software, 65(6):1-30.
  16. Roczniok, R., Maszczyk, A., Stanula, A., Czuba, M., Pietraszewski, P., Kantyka, J., and StarzyÁski, M. (2013). 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, 21(2):121-127.
  17. Sañudo, B., Rueda, D., Pozo-Cruz, B. D., de Hoyo, M., and Carrasco, L. (2014). Validation of a video analysis software package for quantifying movement velocity in resistance exercises. Journal of Strength and Conditioning Research.
  18. Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Springer, New York.
  19. Wang, L. X. and Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6):1414-1427.
  20. Wiktorowicz, K., Przednowek, K., Lassota, L., and Krzeszowski, T. (2015). Predictive modeling in race walking. Computational Intelligence and Neuroscience, 2015:9. Article ID 735060.
  21. Zou, H. and Hastie, T. (2016). Package "elasticnet". CRAN.
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Paper Citation


in Harvard Style

Przednowek K., Wiktorowicz K., Krzeszowski T. and Iskra J. (2016). A Fuzzy-based Software Tool Used to Predict 110m Hurdles Results During the Annual Training Cycle . In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS, ISBN 978-989-758-205-9, pages 176-181. DOI: 10.5220/0006043701760181


in Bibtex Style

@conference{icsports16,
author={Krzysztof Przednowek and Krzysztof Wiktorowicz and Tomasz Krzeszowski and Janusz Iskra},
title={A Fuzzy-based Software Tool Used to Predict 110m Hurdles Results During the Annual Training Cycle},
booktitle={Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,},
year={2016},
pages={176-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006043701760181},
isbn={978-989-758-205-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,
TI - A Fuzzy-based Software Tool Used to Predict 110m Hurdles Results During the Annual Training Cycle
SN - 978-989-758-205-9
AU - Przednowek K.
AU - Wiktorowicz K.
AU - Krzeszowski T.
AU - Iskra J.
PY - 2016
SP - 176
EP - 181
DO - 10.5220/0006043701760181