TY - JOUR
T1 - Discovering a cohesive football team through players’ attributed collaboration networks
AU - Zeng, Yifeng
AU - Yu, Shenbao
AU - Pan, Yinghui
AU - Chen, Bilian
N1 - 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.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - 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.
AB - 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.
KW - Attributed collaboration networks
KW - Football team composition
KW - Game analysis
KW - Heterogeneity&homogeneity
UR - http://www.scopus.com/inward/record.url?scp=85139706616&partnerID=8YFLogxK
U2 - 10.1007/s10489-022-04199-4
DO - 10.1007/s10489-022-04199-4
M3 - Article
SN - 0924-669X
VL - 53
SP - 13506
EP - 13526
JO - Applied Intelligence
JF - Applied Intelligence
IS - 11
ER -