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Development of a framework for modeling preference times in triathlon

  • S.I.: India Intl. Congress on Computational Intelligence 2017
  • Published:
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Abstract

Preference time in a triathlon denotes the time that is planned to be achieved by an athlete in a particular competition. Usually, the preference time is calculated some days, weeks, or even months before the competition. Mostly, trainers calculate the proposed preference time according to the current form, body performances of athletes, psychological abilities and their health state. They also take course specifications into account in order to make their proposal as exact as possible. However, until recently, this prediction was performed manually. This paper presents an automatic framework for modeling preference times based on previous results of athletes on a particular racecourse and particle swarm optimization. Indeed, the framework observed the problem as optimization, where the goal is to find such preference time that is as much as possible correlated with past data. Practical experiments with different scenarios reveal that the proposed solution is promising.

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Notes

  1. www.ironman.com.

  2. Data from 2017 editions are not included in dataset edition of 2016.

  3. Swarm plots were created by seaborn python package: https://github.com/mwaskom/seaborn.

References

  1. Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2018) An improved lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust Comput. https://doi.org/10.1007/s10586-018-1769-z

  2. Abdel-Basset M, Manogaran G, Abdel-Fatah L, Mirjalili S (2018) An improved nature inspired meta-heuristic algorithm for 1-d bin packing problems. Pers Ubiquitous Comput. https://doi.org/10.1007/s00779-018-1132-7

  3. Abdel-Basset M, Shawky LA (2018) Flower pollination algorithm: a comprehensive review. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9624-4

  4. Alatas B, Akin E, Bedri Ozer A (2009) Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals 40(4):1715–1734

    Article  MathSciNet  Google Scholar 

  5. Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez J-C, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195

    Article  Google Scholar 

  6. Fister I, Iglesias A, Deb S, Fister D, Fister I Jr (2017) Modeling preference time in middle distance triathlons. arXiv preprint arXiv:1707.00718

  7. Fister I Jr, Fister D (2016) A collection of ironman, ironman 70.3 and ultra-triathlon race results, version 0.1, technical report 0110

  8. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. IEEE, vol 4, pp 1942–1948

  9. Knechtle B, de Sousa CV, Sales MM, Nikolaidis PT (2017) Pacing in deca and double deca iron ultra-triathlon. Adapt Med 9(2):78–84

    Article  Google Scholar 

  10. Liao C-J, Tseng C-T, Luarn P (2007) A discrete version of particle swarm optimization for flowshop scheduling problems. Comput Oper Res 34(10):3099–3111

    Article  Google Scholar 

  11. Mnadla S, Bragazzi NL, Rouissi M, Chaalali A, Siri A, Padulo J, Ardigò LP, Brigo F, Chamari K, Knechtle B (2016) Infodemiological data of ironman triathlon in the study period 2004–2013. Data Brief 9:123–127

    Article  Google Scholar 

  12. Ofoghi B, Zeleznikow J, MacMahon C, Raab M (2013) Data mining in elite sports: a review and a framework. Meas Phys Educ Exerc Sci 17(3):171–186

    Article  Google Scholar 

  13. Parsopoulos KE, Vrahatis MN et al (2002) Particle swarm optimization method for constrained optimization problems. Intell Technol Theory Appl N Trends Intell Technol 76(1):214–220

    MATH  Google Scholar 

  14. Pearson K (1895) Note on regression and inheritance in the case of two parents. Proc R Soc Lond 58:240–242

    Article  Google Scholar 

  15. Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the 2003 IEEE swarm intelligence symposium. SIS’03. IEEE, pp 174–181

  16. Rana S, Jasola S, Kumar R (2011) A review on particle swarm optimization algorithms and their applications to data clustering. Artif Intell Rev 35(3):211–222

    Article  Google Scholar 

  17. Rüst CA, Knechtle B, Knechtle P, Rosemann T, Lepers R (2012) Participation and performance trends in triple iron ultra-triathlon—a cross-sectional and longitudinal data analysis. Asian J Sports Med 3(3):145

    Article  Google Scholar 

  18. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence. IEEE, pp 69–73

  19. Stiefel M, Knechtle B, Alexander Rüst C, Rosemann T, Lepers R (2013) The age of peak performance in ironman triathlon: a cross-sectional and longitudinal data analysis. Extreme Physiol Med 2(1):27

    Article  Google Scholar 

  20. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol

    Google Scholar 

  21. Zhan Z-H, Zhang J, Li Y, Shi Y-H (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847

    Article  Google Scholar 

Download references

Acknowledgements

Iztok Fister Jr. acknowledges the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057). Iztok Fister acknowledges the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0041).

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 34, 2000, Maribor, Slovenia

    Iztok Fister Jr. & Iztok Fister

  2. University of Cantabria, Avenida de los Castros s/n, 39005, Santander, Spain

    Andres Iglesias

  3. Faculty of Economics and Business, University of Maribor, Razlagova 14, 2000, Maribor, Slovenia

    Dušan Fister

  4. Decision Sciences and Modeling Program, Victoria University, Melbourne, Australia

    Suash Deb

  5. IT and Educational Consultant, Ranchi, India

    Suash Deb

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  1. Iztok Fister Jr.
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  2. Andres Iglesias
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  3. Suash Deb
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  4. Dušan Fister
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Correspondence to Iztok Fister Jr..

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Fister, I., Iglesias, A., Deb, S. et al. Development of a framework for modeling preference times in triathlon. Neural Comput & Applic 32, 10833–10846 (2020). https://doi.org/10.1007/s00521-018-3632-9

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