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

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Published in:PLoS ONE 2024, Vol.19 (8), p.e0303960
Main Authors: Knechtle, Beat, Weiss, Katja, Valero, David, Villiger, Elias, Nikolaidis, Pantelis T, Andrade, Marilia Santos, Scheer, Volker, Cuk, Ivan, Gajda, Robert, Thuany, Mabliny
Format: Report
Language:English
Subjects:
Athletes
Citizenship
Demographic aspects
Machine learning
Marathon running
Physiological research
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container_issue 8
container_start_page e0303960
container_title PLoS ONE
container_volume 19
creator Knechtle, Beat
Weiss, Katja
Valero, David
Villiger, Elias
Nikolaidis, Pantelis T
Andrade, Marilia Santos
Scheer, Volker
Cuk, Ivan
Gajda, Robert
Thuany, Mabliny
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doi_str_mv 10.1371/journal.pone.0303960
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1932-6203
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source Publicly Available Content (ProQuest); PubMed
subjects Athletes
Citizenship
Demographic aspects
Machine learning
Marathon running
Physiological research
title Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events
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