Bisecting for selecting: using a Laplacian eigenmaps clustering approach to create the new European football Super League
This work addresses team selection for sports leagues, but it is incremental as it applies existing unsupervised methods to a new dataset.
The authors tackled the problem of selecting teams for a European football Super League using unsupervised clustering on performance data, successfully identifying four clusters with the top two representing dominant teams suitable for an elite league.
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.