Intelligent team formation and player selection: a data-driven approach for football coaches

Abstract

This article presents a data-driven approach called Intelligent Team Formation and Player Selection (ITFPS), aimed at assisting football coaches in making informed decisions about team formation and player selection. The proposed approach utilizes deep neural networks to evaluate the suitability of each player for different positions on the field. The problem is then formulated as finding the maximum weighted matching in a complete bipartite graph, with the objective of achieving the best possible alignment between team members and the positions designated by the coach. The Hungarian algorithm is employed to facilitate this matching process. Furthermore, the approach allows coaches to select a system of distinct representatives for each position, based on the specific qualities expected from players in a given match. The effectiveness of the approach is demonstrated through tests conducted on four teams from the 2021–2022 English Premier League. The results indicate that ITFPS produces decisions comparable to those made by successful coaches. By optimizing team formations and enabling the utilization of rotating formations, this approach not only enhances team performance but also empowers coaches to make strategic decisions while fully leveraging the potential of their players. ITFPS serves as an intelligent assistant for coaches, complementing their strategic perspectives.