PlayBest: Professional Basketball Player Behavior Synthesis via Planning with Diffusion

• URL: http://arxiv.org/abs/2306.04090v3
• Date: Tue, 16 Jul 2024 21:01:27 GMT
• Title: PlayBest: Professional Basketball Player Behavior Synthesis via Planning with Diffusion
• Authors: Xiusi Chen, Wei-Yao Wang, Ziniu Hu, David Reynoso, Kun Jin, Mingyan Liu, P. Jeffrey Brantingham, Wei Wang,
• Abstract summary: We introduce PlayBest (PLAYer BEhavior DynamicThesis), a method to improve player decision-making. We extend the diffusion probabilistic model to learn challenging environmental dynamics from historical National Basketball Association (NBA) player motion tracking data. Our results reveal that the model excels at generating reasonable basketball trajectories that produce efficient plays.
• Score: 24.47841117058161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamically planning in complex systems has been explored to improve decision-making in various domains. Professional basketball serves as a compelling example of a dynamic spatio-temporal game, encompassing context-dependent decision-making. However, processing the diverse on-court signals and navigating the vast space of potential actions and outcomes make it difficult for existing approaches to swiftly identify optimal strategies in response to evolving circumstances. In this study, we formulate the sequential decision-making process as a conditional trajectory generation process. Based on the formulation, we introduce PlayBest (PLAYer BEhavior SynThesis), a method to improve player decision-making. We extend the diffusion probabilistic model to learn challenging environmental dynamics from historical National Basketball Association (NBA) player motion tracking data. To incorporate data-driven strategies, an auxiliary value function is trained with corresponding rewards. To accomplish reward-guided trajectory generation, we condition the diffusion model on the value function via classifier-guided sampling. We validate the effectiveness of PlayBest through simulation studies, contrasting the generated trajectories with those employed by professional basketball teams. Our results reveal that the model excels at generating reasonable basketball trajectories that produce efficient plays. Moreover, the synthesized play strategies exhibit an alignment with professional tactics, highlighting the model's capacity to capture the intricate dynamics of basketball games.

Related papers

• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• SportsNGEN: Sustained Generation of Realistic Multi-player Sports Gameplay [19.80390059667457]
  We present a transformer decoder based sports simulation engine, SportsNGEN, trained on sports player and ball tracking sequences. By training on a large database of professional tennis tracking data, we demonstrate that simulations produced by SportsNGEN can be used to predict the outcomes of rallies. We show that the model output sampling parameters are crucial to simulation realism and that SportsNGEN is probabilistically well-calibrated to real data.
  arXiv  Detail & Related papers  (2024-02-10T01:16:21Z)
• ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton [52.21869064818728]
  Deep learning approaches for player tactic forecasting in badminton show promising performance partially attributed to effective reasoning about rally-player interactions. We propose a turn-based feature attribution approach, ShuttleSHAP, for analyzing forecasting models in badminton based on variants of Shapley values.
  arXiv  Detail & Related papers  (2023-12-18T05:37:51Z)
• Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM [18.884300680050316]
  This paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. We suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics.
  arXiv  Detail & Related papers  (2023-06-14T02:19:59Z)
• Lucy-SKG: Learning to Play Rocket League Efficiently Using Deep Reinforcement Learning [0.0]
  We present Lucy-SKG, a Reinforcement Learning-based model that learned how to play Rocket League in a sample-efficient manner. Our contributions include the development of a reward analysis and visualization library, a novel parameterizable reward shape function, and auxiliary neural architectures.
  arXiv  Detail & Related papers  (2023-05-25T07:33:17Z)
• Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling [70.54015529131325]
  Basketball is dictated by many variables, such as playstyle and game dynamics. It is crucial that the performance profiles can reflect the diverse playstyles. We present a tool that can visualize player performance profiles in a timely manner.
  arXiv  Detail & Related papers  (2023-03-17T01:13:18Z)
• Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks [83.28949556413717]
  We study the problem of computing an approximate Nash equilibrium of continuous-action game without access to gradients. We model players' strategies using artificial neural networks. This paper is the first to solve general continuous-action games with unrestricted mixed strategies and without any gradient information.
  arXiv  Detail & Related papers  (2022-11-29T05:16:41Z)
• Planning with Diffusion for Flexible Behavior Synthesis [125.24438991142573]
  We consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories.
  arXiv  Detail & Related papers  (2022-05-20T07:02:03Z)
• Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees [0.9137554315375919]
  We present a preliminary investigation of a novel algorithm called Dyna-T. In reinforcement learning (RL) a planning agent has its own representation of the environment as a model. Experience can be used for learning a better model or improve directly the value function and policy.
  arXiv  Detail & Related papers  (2022-01-12T15:06:30Z)
• Learning to Play Imperfect-Information Games by Imitating an Oracle Planner [77.67437357688316]
  We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Our approach is based on model-based planning. We show that the planner is able to discover efficient playing strategies in the games of Clash Royale and Pommerman.
  arXiv  Detail & Related papers  (2020-12-22T17:29:57Z)
• Multi-Modal Trajectory Prediction of NBA Players [14.735704310108101]
  We propose a method that captures the multi-modal behavior of players, where they might consider multiple trajectories and select the most advantageous one. The method is built on an LSTM-based architecture predicting multiple trajectories and their probabilities, trained by a multi-modal loss function. Experiments on large, fine-grained NBA tracking data show that the proposed method outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2020-08-18T11:35:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.