Paper Info
Title
Generating Defensive Plays in Basketball Games.
Abstract
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.
Year
DOI
Venue
2018
10.1145/3240508.3240670
MM '18: ACM Multimedia Conference Seoul Republic of Korea October, 2018
Keywords
Field
DocType
Conditional adversarial network, basketball, defensive strategies
High fidelity,Computer vision,Discriminator,Generative adversarial network,Computer science,Basketball games,Acceleration,Artificial intelligence,Basketball,Offensive
Conference
ISBN
Citations 
PageRank 
978-1-4503-5665-7
0
0.34
References 
Authors
7
6
Name
Order
Citations
PageRank
Chieh-Yu Chen111.16
Wenze Lai210.82
Hsin-Ying Hsieh311.16
Wen-Hao Zheng400.34
Yu-Shuen Wang578539.72
Jung-hong Chuang613619.62