Efficient Strategy Mining for Football Social Network

Abstract

With the growing popularity of social network in sport, it expresses the social relationships between individuals and facilitates realistic applications, e.g., social event mining and discovery. Sport network as a specific social network has been widely studied in research and commercial fields. However, most of the existing works utilize a simplex strategy to improve certain indicators in the team and do not consider the effect of strategy adjustment based on the current situation. In this paper, we study the problem of efficient strategy mining in football social network. To address this problem, we propose a quantitative way to combine the aspects of coordination, adaptability, flexibility, and tempo into a passing network, which notably improves the timeliness and information content of the existing network. On this basis, we design a suppression function to express the impact of strategy. Then, we propose a novel passing network and group cooperation scheme based on quantified team performance to obtain the efficient strategies. At last, the experimental results show that, based on the performance of the same team, our optimized passing network has a higher winning rate in practice.