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

Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada, M3J 1P3

We aim to show how a neural network based machine learning projective clustering algorithm, Projective Adaptive Resonance Theory (PART), can be effectively used to provide data-informed sports decisions. We illustrate 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, our unsupervised clustering analysis provides evidence-based recommendations about the club team formation, and in particular, the decision to buy and sell players within the same clusters, under different scenarios including financial constraints.
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Keywords PART; subspace clustering; unsupervised learning; sports analytics; football manager; soccer

Citation: Marco Tosato, Jianhong Wu. An application of PART to the Football Manager data for players clusters analyses to inform club team formation. Big Data and Information Analytics, 2018, 3(1): 43-54. doi: 10.3934/bdia.2018002


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