An application of PART to the Football Manager data for players clusters analyses to inform club team formation

Abstract

We aim to show how a neural network based machine learning pro-jective clustering algorithm, Projective Adaptive Resonance Theory (PART), can be effectively used to provide data-informed sports decisions. We illus-trate this data-driven decision recommendation for AS Roma player market in the Summer 2018 season, using the two separate databases of fourty-seven attributes taken from Football Manager 2018 for each of the twenty-four soccer player, with the first including players of the AS Roma squad 2017-18, and the second consisting of all players linked with transfer moves to AS Roma. This is high dimensional data as players should be grouped only in terms of their performance with respect to a small subset of attributes. Projective clustering analyses provide a purely data-driven analysis to identify critical attributes and attribute characteristics for a group of players to form a natural cluster (in lower dimensional data space) in an unsupervised way. By merging the two databases