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Computational intelligence in sports: Challenges and opportunities within a new research domain

Author

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  • Fister, Iztok
  • Ljubič, Karin
  • Suganthan, Ponnuthurai Nagaratnam
  • Perc, Matjaž
  • Fister, Iztok

Abstract

Computational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain.

Suggested Citation

  • Fister, Iztok & Ljubič, Karin & Suganthan, Ponnuthurai Nagaratnam & Perc, Matjaž & Fister, Iztok, 2015. "Computational intelligence in sports: Challenges and opportunities within a new research domain," Applied Mathematics and Computation, Elsevier, vol. 262(C), pages 178-186.
    • Handle: RePEc:eee:apmaco:v:262:y:2015:i:c:p:178-186
    DOI: 10.1016/j.amc.2015.04.004
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    References listed on IDEAS

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    2. Aino Ahtinen & Minna Isomursu & Shruti Ramiah & Jan Blom, 2013. "Advise, Acknowledge, Grow and Engage: Design Principles for a Mobile Wellness Application to Support Physical Activity," International Journal of Mobile Human Computer Interaction (IJMHCI), IGI Global, vol. 5(4), pages 20-55, October.
    3. Trenchard, Hugh, 2013. "Peloton phase oscillations," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 194-201.
    4. Arnold Baca, 2014. "Adaptive Systems in Sports," Springer Books, in: Panos M. Pardalos & Victor Zamaraev (ed.), Social Networks and the Economics of Sports, edition 127, pages 115-124, Springer.
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    Cited by:

    1. Erkaymaz, Okan & Ozer, Mahmut & Perc, Matjaž, 2017. "Performance of small-world feedforward neural networks for the diagnosis of diabetes," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 22-28.
    2. Abdullah Erdal Tümer & Sabri Koçer, 2017. "Prediction of team league’s rankings in volleyball by artificial neural network method," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(3), pages 202-211, May.
    3. Alessandro Innocenti & Tommaso Nannicini & Roberto Ricciuti, 2021. "The Importance of Betting Early," Risks, MDPI, vol. 9(4), pages 1-15, April.
    4. Kostić, Srđan & Stojković, Milan & Prohaska, Stevan, 2016. "Hydrological flow rate estimation using artificial neural networks: Model development and potential applications," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 373-385.
    5. Lee, Tae H. & Park, Ju H. & Jung, Hoyoul, 2018. "Network-based H∞ state estimation for neural networks using imperfect measurement," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 205-214.
    6. Devansh Patel & Dhwanil Shah & Manan Shah, 2020. "The Intertwine of Brain and Body: A Quantitative Analysis on How Big Data Influences the System of Sports," Annals of Data Science, Springer, vol. 7(1), pages 1-16, March.

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