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Human running performance from real-world big data

Author

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  • Thorsten Emig

    (Université Paris-Saclay, CNRS, Laboratoire de Physique Théorique et Modèles Statistiques)

  • Jussi Peltonen

    (Polar Electro Oy, Professorintie 5)

Abstract

Wearable exercise trackers provide data that encode information on individual running performance. These data hold great potential for enhancing our understanding of the complex interplay between training and performance. Here we demonstrate feasibility of this idea by applying a previously validated mathematical model to real-world running activities of ≈ 14,000 individuals with ≈ 1.6 million exercise sessions containing duration and distance, with a total distance of ≈ 20 million km. Our model depends on two performance parameters: an aerobic power index and an endurance index. Inclusion of endurance, which describes the decline in sustainable power over duration, offers novel insights into performance: a highly accurate race time prediction and the identification of key parameters such as the lactate threshold, commonly used in exercise physiology. Correlations between performance indices and training volume and intensity are quantified, pointing to an optimal training. Our findings hint at new ways to quantify and predict athletic performance under real-world conditions.

Suggested Citation

  • Thorsten Emig & Jussi Peltonen, 2020. "Human running performance from real-world big data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18737-6
    DOI: 10.1038/s41467-020-18737-6
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    Cited by:

    1. Mabliny Thuany & Sara Pereira & Lee Hill & Jean Carlos Santos & Thomas Rosemann & Beat Knechtle & Thayse Natacha Gomes, 2021. "Where Are the Best European Road Runners and What Are the Country Variables Related to It?," Sustainability, MDPI, vol. 13(14), pages 1-9, July.
    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. Šuštaršič Ana & Videmšek Mateja & Karpljuk Damir & Miloloža Ivan & Meško Maja, 2022. "Big Data in Sports: A Bibliometric and Topic Study," Business Systems Research, Sciendo, vol. 13(1), pages 19-34, June.
    4. Kengo Ishihara & Naho Inamura & Asuka Tani & Daisuke Shima & Ai Kuramochi & Tsutomu Nonaka & Hiroshi Oneda & Yasuyuki Nakamura, 2021. "Contribution of Solid Food to Achieve Individual Nutritional Requirement during a Continuous 438 km Mountain Ultramarathon in Female Athlete," IJERPH, MDPI, vol. 18(10), pages 1-16, May.

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