research-article

Generating Defensive Plays in Basketball Games

Authors:

Hsin-Ying Hsieh,

Jung-Hong ChuangAuthors Info & Claims

MM '18: Proceedings of the 26th ACM international conference on Multimedia

Pages 1580 - 1588

https://doi.org/10.1145/3240508.3240670

Published: 15 October 2018 Publication History

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.

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Cited By

Fujii KTakeishi NKawahara YTakeda K(2024)Decentralized policy learning with partial observation and mechanical constraints for multiperson modelingNeural Networks10.1016/j.neunet.2023.11.068171(40-52)Online publication date: Mar-2024
https://doi.org/10.1016/j.neunet.2023.11.068
Kuo MWang Y(2023)Basketball Flow: Learning to Synthesize Realistic and Diverse Basketball GamePlays based on Strategy SketchesProceedings of the 5th ACM International Conference on Multimedia in Asia Workshops10.1145/3611380.3630167(1-5)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3611380.3630167
Tang QXu GLiang XLuo JShen GZhao M(2023)A Method for Generating Basketball Defensive Runs by Combining VAE-WGAN2023 9th International Conference on Computer and Communications (ICCC)10.1109/ICCC59590.2023.10507495(2538-2543)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ICCC59590.2023.10507495
Show More Cited By

Index Terms

Generating Defensive Plays in Basketball Games
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia content creation

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Published In

cover image ACM Conferences

MM '18: Proceedings of the 26th ACM international conference on Multimedia

October 2018

2167 pages

ISBN:9781450356657

DOI:10.1145/3240508

General Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Kyoung Mu Lee
Seoul National University, Korea
,
Jiebo Luo
University of Rochester, USA
,
Wenwu Zhu
Tsinghua University, China
,
Program Chairs:
Hyeran Byun
Yonsei University, Korea
,
Chang Wen Chen
State Univ. Of New York at Buffalo, USA
,
Rainer Lienhart
University of Augsburg, Germany
,
Tao Mei
JD AI, China

Copyright © 2018 ACM.

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Published: 15 October 2018

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MM '18: ACM Multimedia Conference

October 22 - 26, 2018

Seoul, Republic of Korea

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MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Fujii KTakeishi NKawahara YTakeda K(2024)Decentralized policy learning with partial observation and mechanical constraints for multiperson modelingNeural Networks10.1016/j.neunet.2023.11.068171(40-52)Online publication date: Mar-2024
https://doi.org/10.1016/j.neunet.2023.11.068
Kuo MWang Y(2023)Basketball Flow: Learning to Synthesize Realistic and Diverse Basketball GamePlays based on Strategy SketchesProceedings of the 5th ACM International Conference on Multimedia in Asia Workshops10.1145/3611380.3630167(1-5)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3611380.3630167
Tang QXu GLiang XLuo JShen GZhao M(2023)A Method for Generating Basketball Defensive Runs by Combining VAE-WGAN2023 9th International Conference on Computer and Communications (ICCC)10.1109/ICCC59590.2023.10507495(2538-2543)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ICCC59590.2023.10507495
Wu YDeng DXie XHe MXu JZhang HZhang HWu Y(2022)OBTracker: Visual Analytics of Off-ball Movements in BasketballIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209373(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209373
Chao YChen WPeng JHu M(2022)Learning Robust Latent Space of Basketball Player Trajectories for Tactics Analysis2022 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME52920.2022.9859960(1-6)Online publication date: 18-Jul-2022
https://doi.org/10.1109/ICME52920.2022.9859960
Wang YLiu WLiu X(2022)Explainable AI techniques with application to NBA gameplay predictionNeurocomputing10.1016/j.neucom.2022.01.098483(59-71)Online publication date: Apr-2022
https://doi.org/10.1016/j.neucom.2022.01.098
Latif SDriss MBoulila WHuma ZJamal SIdrees ZAhmad J(2021)Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future DirectionsSensors10.3390/s2122751821:22(7518)Online publication date: 12-Nov-2021
https://doi.org/10.3390/s21227518
Fujii K(2021)Data-Driven Analysis for Understanding Team Sports BehaviorsJournal of Robotics and Mechatronics10.20965/jrm.2021.p050533:3(505-514)Online publication date: 20-Jun-2021
https://doi.org/10.20965/jrm.2021.p0505
Chen WTsai WChang HHu MChu W(2021)Instant Basketball Defensive Trajectory GenerationACM Transactions on Intelligent Systems and Technology10.1145/346061913:1(1-20)Online publication date: 23-Dec-2021
https://dl.acm.org/doi/10.1145/3460619
Chang HChen WTsai WHu MChu WChua TWang JTian QGurrin CJia JZhang HSun Q(2021)An autoregressive generation model for producing instant basketball defensive trajectoryProceedings of the 2nd ACM International Conference on Multimedia in Asia10.1145/3444685.3446300(1-7)Online publication date: 7-Mar-2021
https://dl.acm.org/doi/10.1145/3444685.3446300
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