Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events

• Beat Knechtle ,

  Roles Conceptualization

  * E-mail: beat.knechtle@hispeed.ch

  Affiliations Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland, Institute of Primary Care, University of Zurich, Zurich, Switzerland

  https://orcid.org/0000-0002-2412-9103

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• Katja Weiss,

  Roles Writing – review & editing

  Affiliation Institute of Primary Care, University of Zurich, Zurich, Switzerland

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• David Valero,

  Roles Formal analysis

  Affiliation Ultra Sports Science Foundation, Pierre-Benite, France

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• Elias Villiger,

  Roles Data curation

  Affiliation Institute of Primary Care, University of Zurich, Zurich, Switzerland

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• Pantelis T. Nikolaidis,

  Roles Writing – review & editing

  Affiliation School of Health and Caring Sciences, University of West Attica, Athens, Greece

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• Marilia Santos Andrade,

  Roles Writing – review & editing

  Affiliation Department of Physiology, Federal University of São Paulo, São Paulo, Brazil

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• Volker Scheer,

  Roles Writing – review & editing

  Affiliation Ultra Sports Science Foundation, Pierre-Benite, France

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• Ivan Cuk,

  Roles Writing – review & editing

  Affiliation Faculty of Sport and Physical Education, University of Belgrade, Belgrade, Serbia

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• Robert Gajda,

  Roles Writing – review & editing

  Affiliation Center for Sports Cardiology at the Gajda-Med Medical Center in Pułtusk, Pułtusk, Poland

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• Mabliny Thuany

  Roles Writing – review & editing

  Affiliation Faculty of Sports, University of Porto, Porto, Portugal

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