Clustering Functional Data Streams: Unsupervised Classification of Soccer Top Players based on Google Trends

Nowadays, football teams have become large companies, which produce very high incomes, and the induced gain that may arise from purchasing notorious players is become a crucial aspect for their commercial strategies. The interest for a player is not exclusively tied to his technical skills but also to other factors, which may attract people not only interested in football. Because soccer has become a fact of life for many supporters, the attention paid to football players is also reflected in the internet traffic. For this reason, the analysis of web-data nowadays is a central issue for monitoring players notoriety and catching information about marketing strategies. This paper aims to provide an original methodological approach for the analysis of web-data using the functional data analysis approach. The main advantages of the proposal are the drastic dimensionality reduction of the data and the use of functional tools for obtaining additional information about data. Specifically, in this study, we focus on the problem of clustering data streams using the k-means algorithm and three different semi-metrics based on the functional principal components decomposition, first, and second derivatives, respectively. An application to a real data set regarding Google queries of 24 football players is proposed for illustrating the method. The final aim of this research is provide scholars, practitioners, football clubs, and agencies interested in planning marketing strategies with additional tools for analyzing web-data.