IEEE VIS 2024 Content: Smartboard: Visual Exploration of Team Tactics with LLM Agent

Smartboard: Visual Exploration of Team Tactics with LLM Agent

Ziao Liu - Zhejiang University, Hangzhou, China

Xiao Xie - Zhejiang University, Hangzhou, China

Moqi He - Zhejiang University, Hangzhou, China

Wenshuo Zhao - Zhejiang University, Hangzhou, China

Yihong Wu - Zhejiang University, Hangzhou, China

Liqi Cheng - Zhejiang University, Hangzhou, China

Hui Zhang - Zhejiang University, Hangzhou, China

Yingcai Wu - Zhejiang University, Hangzhou, China

Room: Bayshore V

2024-10-17T14:39:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T14:39:00Z
Exemplar figure, described by caption below
The system interface of Smartboard. (A) The chat view provides system feedback and enhances communication between users and the system through tag selections and open-question answering. (B) The setup view provides interactions during tactical setup with tactics sketching, matchup analysis, and situation retrieval. (C) The simulation view presents the coach agent's recommended tactics, along with explanations and evaluations in both overview and detail. (D) The history view records users' tactics and provides the classic tactics for starting exploration.
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Keywords

Sports visualization, tactic board, tactical analysis

Abstract

Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs. Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.