InsightChaser: Enhancing Visual Reasoning of Sports Tactical Visualization with Visual-Text Linking

In sports analytics, tactical visualization is widely used to convey valuable insights. However, due to the complex domain knowledge and contextual information involved in tactical visualizations, it is challenging for users to connect high-level tactical insights to corresponding visual patterns. This requires users to engage in a reasoning process to interpret insights within game contexts, which remains insufficiently supported in existing visual-text linking studies. In this work, we propose InsightChaser, a novel approach to bridge tactical insights and soccer visualizations through visual-text linking and visual reasoning enhancement. InsightChaser constructs knowledge graphs to represent both visual elements and contextual game information. Integrating large language models (LLMs), our approach retrieves relevant visual elements and establishes explicit links with insights. Moreover, InsightChaser utilizes LLMs to enhance these visual-text links by providing reasoning explanations and visual effects. We further develop an interactive visualization system that supports navigation and explanation of enhanced visual-text links. Users can explore linked tactical insights interactively and reason through enhanced visual explanations. We conduct two case studies using real-world soccer data and a user study to demonstrate the effectiveness of our approach.