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

TL;DR

This paper proposes an unsupervised clustering method using Laplacian eigenmaps and football performance data to identify top teams for forming a competitive European Super League.

Contribution

It introduces a novel unsupervised approach combining variable selection and spectral clustering to identify elite teams based on performance metrics.

Findings

Successfully identified four natural clusters of teams

Top clusters include dominant teams suitable for a super league

Demonstrated effectiveness of unsupervised methods in sports analytics

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.