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Bayesian approaches for critical velocity models

Resource type
Thesis type
(Project) M.Sc.
Date created
2021-11-17
Authors/Contributors
Abstract
In sports science, critical power and related critical velocity models have been widely investigated, and are being increasingly applied to field-based team sports. A challenge associated with these models is that laboratory experiments which yield accurate measurements of maximal sustainable velocity are expensive. Alternatively, inexpensive field data (from training and matches) are being used to fit such models. However, with field data, the dependent variable concerning maximum sustainable velocity is reliably calibrated only for short time durations. This paper develops methods where field data based on short time durations is combined with prior knowledge to fit the three-parameter critical velocity model. This is accomplished in a Bayesian framework where Markov chain methods are required for model fitting and inference
Document
Extent
24 pages.
Identifier
etd21735
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Swartz, Tim
Thesis advisor: Parker, Gary
Language
English
Download file Size
etd21735.pdf 550.88 KB

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