Bisecting for selecting: using a Laplacian eigenmaps clustering approach to create the new European football Super League A. J. Bond1 & C. B. Beggs1 1Institute for Sport, Physical Activity and Leisure, Carngergie School of Sport, Leeds Beckett University, Leeds, West Yorkshire, United Kingdom. Alexander John Bond (Corresponding Author) 215 Cavendish Hall, Headingley Campus, Leeds Beckett University, LS6 3QS.
[email protected] Clive Beggs, Faifax Hall, Headingley Campus, Leeds Beckett University, LS6 3QS.
[email protected] Bisecting for selecting: using a Laplacian eigenmaps clustering approach to create the new European football Super League Abstract We use European football performance data to select teams to form the proposed European football Super League, using only unsupervised techniQues. We first used random forest regression to select important variables predicting goal difference, which we used to calculate the Euclidian distances between teams. Creating a Laplacian eigenmap, we bisected the Fielder vector to identify the five major European football leagues' natural clusters. Our results showed how an unsupervised approach could successfully identify four clusters based on five basic performance metrics: shots, shots on target, shots conceded, possession, and pass success. The top two clusters identify those teams who dominate their respective leagues and are the best candidates to create the most competitive elite super league. Keywords: OR in sports; Selection; Unsupervised; Spectral clustering; Laplacian