Bayesian networks in performance of cyclists

Bachelor thesis (2017)

Authors

M.H.M. Christianen Electrical Engineering, Mathematics and Computer Science

Contributors

G.F. Nane (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2017 Mark Christianen
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Published Date

14-07-2017

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This report examines the possibility of modeling the performance of professional cyclists of Team Sunweb using Bayesian Networks. This research has an objective to see how these structures work and how they fit in the complex world of cycling. We want to compare different cyclists of Team Sunweb in the Grand Tours (Giro d'Italia, Tour de France and Vuelta a Espana) of the year 2016 and build a model for the leader - who is supported by a group of helpers - during different stages in a given race and see if we can predict the pedal power in the crucial part of the race, i.e. the sprint or a last difficult climb. We can conclude that the Bayesian network we created with the combination of help from an expert and the data captures the most common relationships between all variables, but that the model doesn’t reveal surprising relationships or good predictions.

Files

Bep_final.pdf
(pdf | 3.11 Mb)