An analysis of rock climbing sport regarding performance, sponsorship, and health
data mining, data analytics, sport data analysis, logistic regression, neural network
The 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.
H. 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.
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