Analysis and Modeling of Football Team’s Collaboration Mode and Performance Evaluation Using Network Science and BP Neural Network

Peng Cao,Xueyin Zhao,Yu Niu,Feiting Shi,Xuhao Wang,Jian Zhang,Yushuai Wu

doi:10.1155/2020/7397169

Abstract

With the continuous development of society, the cooperation of different dimensions is urgently needed. Analysis and modeling of team cooperation model and performance evaluation are especially important for competitive sport. In this paper, a football team’s attacking mode and the team performance were assessed using network science methodologies. The match process was analyzed by using the data of Team A (given in the form of attachment due to excessive file size) and the method of complex network science. Each player was regarded as a node in the network, and the interaction among players was considered as the connection to the network. This method directly reflected the favorable formation of the team and the interaction frequency among members. Then, a team performance evaluation model was established using the backpropagation neural network (BPNN) and the uncrossed analytic hierarchy process (U-AHP) method based on the factors including the number of passes and successful pass rate. The team performance was comprehensively rated from two levels: member and team level. Analysis from established models indicated that Team A had a higher probability of winning when using the “4-4-2” offensive strategy and performance evaluation analysis indicated that more passes and higher pass success rates were more beneficial to win the game. Following the model developed in this study, some suggestions were given from the perspectives of team strategy, attack mode, cooperation, and incentive mode.

Highlights

Football is known as the “world’s first game” and the most influential sport in the world [1]. e standard 11-player football match consists of 10 players and 1 goalkeeper from each team, 22 players in total who fight, defend, and attack on the rectangular grass field [2]
Network science is widely used in the classic problems of the social system [6], such as the stimulation of cooperation among individuals, epidemic infection, or the spread of information on social networks [7]. e football team’s playing style and personal contribution can be revealed through indicators from network science [8]
Research on the collaborative results mainly focuses on the team performance evaluation

Summary

Introduction

Football is known as the “world’s first game” and the most influential sport in the world [1]. e standard 11-player football match consists of 10 players and 1 goalkeeper from each team, 22 players in total who fight, defend, and attack on the rectangular grass field [2]. Research on the collaborative results mainly focuses on the team performance evaluation. Is paper mainly uses the CNS method to establish and analyze the football team’s cooperation network and uses the BPNN method to evaluate the team’s performance. E CNS method can analyze the whole process of the football match and the collaborative network established by it is both flexible and dynamic, which can reflect the overall situation of the team and track the dynamics of each player during the game. Is paper mainly uses the CNS method to analyze the cooperation problems in football teams and through the use of a large number of match data to build a matching network and to reveal the mechanism of team cooperation, focusing on the team’s attack and defense strategy. As the team’s performance evaluation is relatively complex, it has a great impact on the accuracy and convergence speed of the network [18,19,20]. e solution proposed in this paper is to enhance the adaptability of the evaluation network by using a large amount of data, greatly improving the accuracy of the interpretation of the network

Methods

Implementation Methods

Result

Results and Discussion