research-article

Basketball Flow: Learning to Synthesize Realistic and Diverse Basketball GamePlays based on Strategy Sketches

Authors:

Yu-Shuen WangAuthors Info & Claims

MMAsia '23 Workshops: Proceedings of the 5th ACM International Conference on Multimedia in Asia Workshops

Article No.: 15, Pages 1 - 5

https://doi.org/10.1145/3611380.3630167

Published: 30 December 2023 Publication History

Abstract

In this study, we present BasketballFlow, a system designed to generate diverse basketball gameplays based on a pre-determined strategy sketch. A strategy sketch is a graphical representation that coaches use to outline their planned tactics, encompassing the projected routes of the ball and the offensive players. Despite the visual depiction of the offensive strategy, less experienced players might find it challenging to fully understand these tactics and often falter in their implementation due to interference from defensive players. Our system aims to remedy this by simulating different game scenarios that illustrate potential defensive maneuvers, thereby aiding these less experienced players in improving their success rate of tactical execution. BasketballFlow is composed of a variational generative adversarial network (VAEGAN) and a normalizing flow. The VAEGAN is tasked with producing highly accurate game scenarios, while the normalizing flow ensures a wide diversity in the simulated outcomes. Compared to other existing methods, BasketballFlow demonstrates superior proficiency in simulating a broad spectrum of gameplays while maintaining a lower Frèchet distance to real gameplays. The effectiveness of our BasketballFlow system is validated through our experimental results.

References

[1]

Rameen Abdal, Peihao Zhu, Niloy J Mitra, and Peter Wonka. 2021. Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics 40, 3 (2021), 1–21.

Digital Library

[2]

Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017).

[3]

Peter Beshai. 2014. Buckets: Basketball Shot Visualization. University of British Columbia, published Dec (2014), 547–14.

[4]

Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G Willcocks. 2021. Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE transactions on pattern analysis and machine intelligence (2021).

[5]

Chieh-Yu Chen, Wenze Lai, Hsin-Ying Hsieh, Yu-Shuen Wang, Wen-Hsiao Peng, and Jung-Hong Chuang. 2018. Adversarial generation of defensive trajectories in basketball games. In 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 1–1.

[6]

Chieh-Yu Chen, Wenze Lai, Hsin-Ying Hsieh, Wen-Hao Zheng, Yu-Shuen Wang, and Jung-Hong Chuang. 2018. Generating defensive plays in basketball games. In Proceedings of the 26th ACM international conference on Multimedia. 1580–1588.

Digital Library

[7]

Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2018. Neural ordinary differential equations. arXiv preprint arXiv:1806.07366 (2018).

[8]

Laurent Dinh, David Krueger, and Yoshua Bengio. 2014. Nice: Non-linear independent components estimation. arXiv preprint arXiv:1410.8516 (2014).

[9]

Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2016. Density estimation using real nvp. arXiv preprint arXiv:1605.08803 (2016).

[10]

Carl Doersch. 2016. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908 (2016).

[11]

Alexander Franks, Andrew Miller, Luke Bornn, and Kirk Goldsberry. 2015. Counterpoints: Advanced defensive metrics for nba basketball. In 9th Annual MIT Sloan Sports Analytics Conference, Boston, MA.

[12]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems 27 (2014).

[13]

Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. 2018. Ffjord: Free-form continuous dynamics for scalable reversible generative models. arXiv preprint arXiv:1810.01367 (2018).

[14]

Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, and Daan Wierstra. 2014. Deep autoregressive networks. In International Conference on Machine Learning. PMLR, 1242–1250.

[15]

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. 2017. Improved training of wasserstein gans. In Advances in Neural Information Processing Systems. 5767–5777.

[16]

Hsin-Ying Hsieh, Chieh-Yu Chen, Yu-Shuen Wang, and Jung-Hong Chuang. 2019. BasketballGAN: Generating Basketball Play Simulation Through Sketching. arXiv preprint arXiv:1909.07088 (2019).

[17]

Diederik P Kingma and Prafulla Dhariwal. 2018. Glow: Generative flow with invertible 1x1 convolutions. arXiv preprint arXiv:1807.03039 (2018).

[18]

Justin Kubatko, Dean Oliver, Kevin Pelton, and Dan T Rosenbaum. 2007. A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports 3, 3 (2007).

[19]

Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, and Ce Liu. 2021. Vitgan: Training gans with vision transformers. arXiv preprint arXiv:2107.04589 (2021).

[20]

Dean Oliver. 2004. Basketball on paper: rules and tools for performance analysis. Potomac Books, Inc.

[21]

George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. 2019. Normalizing flows for probabilistic modeling and inference. arXiv preprint arXiv:1912.02762 (2019).

[22]

Jaime Sampaio, Eric J Drinkwater, and Nuno M Leite. 2010. Effects of season period, team quality, and playing time on basketball players’ game-related statistics. European Journal of Sport Science 10, 2 (2010), 141–149.

[23]

Jaime Sampaio, Manuel Janeira, Sergio Ibáñez, and Alberto Lorenzo. 2006. Discriminant analysis of game-related statistics between basketball guards, forwards and centres in three professional leagues. European journal of sport science 6, 3 (2006), 173–178.

[24]

Thomas Seidl, Aditya Cherukumudi, Andrew Hartnett, Peter Carr, and Patrick Lucey. 2018. Bhostgusters: Realtime Interactive Play Sketching with Synthesized NBA Defenses.(2018). (2018).

[25]

Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan. 2019. Pointflow: 3d point cloud generation with continuous normalizing flows. In IEEE/CVF International Conference on Computer Vision. 4541–4550.

[26]

Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2022. Diffusion models: A comprehensive survey of methods and applications. Comput. Surveys (2022).

[27]

Xianwen Yu, Xiaoning Zhang, Yang Cao, and Min Xia. 2019. VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders. In IJCAI. 4206–4212.

Cited By

Liu ZXie XHe MZhao WWu YCheng LZhang HWu Y(2024)Smartboard: Visual Exploration of Team Tactics with LLM AgentIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345620031:1(23-33)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456200

Index Terms

Basketball Flow: Learning to Synthesize Realistic and Diverse Basketball GamePlays based on Strategy Sketches
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
2. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia content creation

Recommendations

Generating Defensive Plays in Basketball Games
MM '18: Proceedings of the 26th ACM international conference on Multimedia

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 ...
Mastering Basketball With Deep Reinforcement Learning: An Integrated Curriculum Training Approach
AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems

Despite the success of deep reinforcement learning in a variety type of games such as Board games, RTS, FPS, and MOBA games, sports games (SPG) like basketball have been seldom studied. Basketball is one of the most popular and challenging sports games ...
Basketball predictions in the NCAAB and NBA: Similarities and differences

Most work on predicting the outcome of basketball matches so far has focused on National College Athletics Association Basketball NCAAB games. Since NCAAB and professional National Basketball Association, NBA basketball have a number of differences, it ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MMAsia '23 Workshops: Proceedings of the 5th ACM International Conference on Multimedia in Asia Workshops

December 2023

97 pages

ISBN:9798400703263

DOI:10.1145/3611380

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 December 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Industrial Technology Research Institute, Taiwan
National Science and Technology Council, Taiwan

Conference

MMAsia '23

Sponsor:

SIGMM

MMAsia '23: ACM Multimedia Asia

December 6 - 8, 2023

Tainan, Taiwan

Acceptance Rates

Overall Acceptance Rate 59 of 204 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
52
Total Downloads

Downloads (Last 12 months)52
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu ZXie XHe MZhao WWu YCheng LZhang HWu Y(2024)Smartboard: Visual Exploration of Team Tactics with LLM AgentIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345620031:1(23-33)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456200

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents