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Detecting the marathon asymmetry with a statistical signature

Author

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  • Billat, Véronique
  • Carbillet, Thomas
  • Correa, Matthieu
  • Pycke, Jean-Renaud

Abstract

Lately, the sub two-hour marathon attempt in Monza was still based on the belief that constant speed is the best way of running. This idea is relayed by marathon organizers who offer pace-group leaders to help the runners to maintain a target race speed. The purposes of this study are to verify the hypotheses that 1. The mass runners try to maintain a constant speed without succeeding. 2. Marathoners run in an asymmetric way and this turns out to be visible in the speed time series. Those two points are independent of the gender, the level of performance (2h30–3h40) and the profile of the race (Paris vs Berlin). Before considering a predictive running strategy for optimizing personal marathon running performance, here we shed light on some significant statistical features by analyzing speed time series data recorded by 273 runners’ GPS. We started with looking for a trend in the speed time series. By means of Kendall’s non-parametric rank correlation coefficient we exhibited a decreasing trend in speed data, whichever the level of performance, gender (Male and Female) and race profile (Berlin and Paris marathons). Going deeper in the study we applied a systematic analysis of the asymmetry of speed via classical statistical measures of skewness. Among them the quantiles of the average speed, i.e. the proportion of the race run above or below the final average The combination of the trend and the asymmetry lead to building up a statistical signature for the speed time series which is identical regardless the level of performance, gender and race profile.

Suggested Citation

  • Billat, Véronique & Carbillet, Thomas & Correa, Matthieu & Pycke, Jean-Renaud, 2019. "Detecting the marathon asymmetry with a statistical signature," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 240-247.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:240-247
    DOI: 10.1016/j.physa.2018.09.159
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    References listed on IDEAS

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    1. Billat, Véronique L. & Mille-Hamard, Laurence & Meyer, Yves & Wesfreid, Eva, 2009. "Detection of changes in the fractal scaling of heart rate and speed in a marathon race," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3798-3808.
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    Citations

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    Cited by:

    1. Véronique Billat & Luc Poinsard & Florent Palacin & Jean Renaud Pycke & Michael Maron, 2022. "Oxygen Uptake Measurements and Rate of Perceived Exertion during a Marathon," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    2. Jean-Renaud Pycke & Véronique Billat, 2022. "Marathon Performance Depends on Pacing Oscillations between Non Symmetric Extreme Values," IJERPH, MDPI, vol. 19(4), pages 1-19, February.
    3. Claire A. Molinari & Pierre Bresson & Florent Palacin & Véronique Billat, 2021. "Pace Controlled by a Steady-State Physiological Variable Is Associated with Better Performance in a 3000 M Run," IJERPH, MDPI, vol. 18(15), pages 1-11, July.
    4. Claire A. Molinari & Johnathan Edwards & Véronique Billat, 2020. "Maximal Time Spent at VO 2max from Sprint to the Marathon," IJERPH, MDPI, vol. 17(24), pages 1-11, December.
    5. Véronique Billat & Damien Vitiello & Florent Palacin & Matthieu Correa & Jean Renaud Pycke, 2020. "Race Analysis of the World’s Best Female and Male Marathon Runners," IJERPH, MDPI, vol. 17(4), pages 1-6, February.
    6. Guo, Junke & Mohebbi, Amin & Zhang, Tian C., 2022. "Application of general unit hydrograph model for marathon finish time distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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