Discovering a cohesive football team through players’ attributed collaboration networks

Abstract

The process of team composition in multiplayer sports such as football has been a main area of interest within the field of the science of teamwork, which is important for improving competition results and game experience. Recent algorithms for the football team composition problem take into account the skill proficiency of players but not the interactions between players that contribute to winning the championship. To automate the composition of a cohesive team, we consider the internal collaborations among football players. Specifically, we propose a Team Composition based on the Football Players’ Attributed Collaboration Network (TC-FPACN) model, aiming to identify a cohesive football team by maximizing football players’ capabilities and their collaborations via three network metrics, namely, network ability, network density and network heterogeneity&homogeneity. Solving the optimization problem is NP-hard; we develop an approximation method based on greedy algorithms and then improve the method through pruning strategies given a budget limit. We conduct experiments on two popular football simulation platforms. The experimental results show that our proposed approach can form effective teams that dominate others in the majority of simulated competitions.

Recommending Player Signings to Football Teams: A Data-Driven Optimization Approach

“…Cao et al (2022) go even further and consider the spatial regions of player actions and how they interact with opposing players. Yu et al (2023) expand on this idea of player interaction and propose a player selection model based on that. This paper contributes to this literature by combining some previously proposed ideas into a brand-novel framework.…”