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Restoring the real world records in Men’s swimming without high-tech swimsuits

Author

Listed:
  • Gao Zhenyu

    (Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA)

  • Li Yixing

    (Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA)

  • Wang Zhengxin

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA)

Abstract

The recently concluded 2019 World Swimming Championships was another major swimming competition that witnessed some great progresses achieved by human athletes in many events. However, some world records created 10 years ago back in the era of high-tech swimsuits remained untouched. With the advancements in technical skills and training methods in the past decade, the inability to break those world records is a strong indication that records with the swimsuit bonus cannot reflect the real progressions achieved by human athletes in history. Many swimming professionals and enthusiasts are eager to know a measure of the real world records had the high-tech swimsuits never been allowed. This paper attempts to restore the real world records in Men’s swimming without high-tech swimsuits by integrating various advanced methods in probabilistic modeling and optimization. Through the modeling and separation of swimsuit bias, natural improvement, and athletes’ intrinsic performance, the result of this paper provides the optimal estimates and the 95% confidence intervals for the real world records. The proposed methodology can also be applied to a variety of similar studies with multi-factor considerations.

Suggested Citation

  • Gao Zhenyu & Li Yixing & Wang Zhengxin, 2020. "Restoring the real world records in Men’s swimming without high-tech swimsuits," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(4), pages 291-300, December.
    • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:4:p:291-300:n:1
    DOI: 10.1515/jqas-2019-0087
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    Cited by:

    1. Griffin Jim E. & Hinoveanu Laurenţiu C. & Hopker James G., 2022. "Bayesian modelling of elite sporting performance with large databases," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 18(4), pages 253-268, December.

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