Generating defensive plays in basketball games

Chieh Yu Chen, Wen Hao Zheng, Wenze Lai, Yu-Shuen Wang, Hsin Ying Hsieh, Jung-Hong Chuang

研究成果: Conference contribution同行評審

10 引文 斯高帕斯(Scopus)


In this paper, we present a method to generate realistic defensive plays in a basketball game based on the ball and the offensive team's movements. Our system allows players and coaches to simulate how the opposing team will react to a newly developed offensive strategy for evaluating its effectiveness. To achieve the aim, we train on the NBA dataset a conditional generative adversarial network that learns spatio-temporal interactions between players' movements. The network consists of two components: a generator that takes a latent noise vector and the offensive team's trajectories as input to generate defensive team's trajectories; and a discriminator that evaluates the realistic degree of the generated results. Since a basketball game can be easily identified as fake if the ball handler, who is not defended, does not shoot the ball or cut into the restricted area, we add the wide open penalty to the objective function to assist model training. To evaluate the results, we compared the similarity of the real and the generated defensive plays, in terms of the players' movement speed and acceleration, distance to defend ball handlers and non- ball handlers, and the frequency of wide open occurrences. In addition, we conducted a user study with 59 participants for subjective tests. Experimental results show the high fidelity of the generated defensive plays to real data and demonstrate the feasibility of our algorithm.

主出版物標題MM 2018 - Proceedings of the 2018 ACM Multimedia Conference
發行者Association for Computing Machinery, Inc
出版狀態Published - 15 10月 2018
事件26th ACM Multimedia conference, MM 2018 - Seoul, Korea, Republic of
持續時間: 22 10月 201826 10月 2018


名字MM 2018 - Proceedings of the 2018 ACM Multimedia Conference


Conference26th ACM Multimedia conference, MM 2018
國家/地區Korea, Republic of


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