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Predicting Cycling Performance Using Machine Learning

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title
Predicting Cycling Performance Using Machine Learning
author
Murillo Burford, Esteban
abstract
For over two decades, professional cyclists have been relying on power meters in their training. These devices provide an accurate and reliable measurement of the effort made while cycling. Such devices, however, are costly for the majority of cyclists and they often come with an overwhelming variety of features, which can be confusing to non-professionals. Several popular online platforms use physics-based equations for estimation of cycling power and they rely on several assumptions about environment. At the same time, these platforms have access to large collections of workout data along with the personal characteristics of riders, which could be used for the data-driven predictions of cycling power.
subject
cycling
machine learning
performance
power
prediction
sports
contributor
Khuri, Natalia (committee chair)
Cañas, Daniel (committee member)
Pauca, Paúl (committee member)
date
2020-05-29T08:35:52Z (accessioned)
2021-05-28T08:30:12Z (available)
2020 (issued)
degree
Computer Science (discipline)
embargo
2021-05-28 (terms)
identifier
http://hdl.handle.net/10339/96810 (uri)
language
en (iso)
publisher
Wake Forest University
type
Thesis

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