Discovering a cohesive football team through players’ attributed collaboration networks

Zeng, Yifeng, Yu, Shenbao, Pan, Yinghui and Chen, Bilian (2022) Discovering a cohesive football team through players’ attributed collaboration networks. Applied Intelligence, 53 (11). pp. 13506-13526. ISSN 0924-669X

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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.

Item Type: Article
Additional Information: Funding information: This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61836005 and 62176225 and the Youth Innovation Fund of Xiamen under Grant No. 3502Z20206049.
Uncontrolled Keywords: Football team composition, Attributed collaboration networks, Game analysis, Heterogeneity&homogeneity
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 30 Sep 2022 09:08
Last Modified: 12 Oct 2023 03:30

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