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We use European football performance data to select teams to form theproposed European football Super League, using only unsupervised techniques. Wefirst used random forest regression to select important variables predictinggoal difference, which we used to calculate the Euclidian distances betweenteams. Creating a Laplacian eigenmap, we bisected the Fielder vector toidentify the five major European football leagues' natural clusters. Ourresults showed how an unsupervised approach could successfully identify fourclusters based on five basic performance metrics: shots, shots on target, shotsconceded, possession, and pass success. The top two clusters identify thoseteams who dominate their respective leagues and are the best candidates tocreate the most competitive elite super league.

Bisecting for selecting: using a Laplacian eigenmaps clustering approach to create the new European football Super League