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An analysis of rock climbing sport regarding performance, sponsorship, and health

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dc.contributor.authorHuynh, Huy
dc.contributor.authorSobek, Elliott
dc.contributor.authorEl-Hajj, Mohamad
dc.contributor.authorAtwal, Sunny
dc.date.accessioned2020-10-16
dc.date.accessioned2022-05-31T01:15:56Z
dc.date.available2022-05-31T01:15:56Z
dc.date.issued2018
dc.description.abstractThe basis of this work was to extract knowledge using data mining techniques over 2 million records from rock climbing competitions all over the world. The first phase of the project involved heavy data cleaning and preprocessing procedures to prepare the data for the mining models. After the first phase was completed, we explored three main questions: which factors can predict the performance of a competitor, which factors will likely lead to a sponsorship of a competitor and is it possible to predict healthiness of a competitor. This research will not only help rock climbers gain significant insights about their performance, it will also help sports sponsors choose the best candidates to assign their brand to.
dc.description.urihttps://library.macewan.ca/full-record/edseee/edseee.8301676
dc.identifier.citationH. Huynh, E. Sobek, M. El-Hajj and S. Atwal, "An analysis of rock climbing sport regarding performance, sponsorship, and health," 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, 2018, pp. 248-254.
dc.identifier.doihttps://doi.org/10.1109/CCWC.2018.8301676
dc.identifier.urihttps://hdl.handle.net/20.500.14078/1948
dc.languageEnglish
dc.language.isoen
dc.rightsAll Rights Reserved
dc.subjectdata mining
dc.subjectdata analytics
dc.subjectsport data analysis
dc.subjectlogistic regression
dc.subjectneural network
dc.titleAn analysis of rock climbing sport regarding performance, sponsorship, and healthen
dc.typePresentation

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