  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.
