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    InsightChaser links tactical insights to soccer visualizations using knowledge graphs and large language models (LLMs). This approach enhances visual reasoning, making complex sports analytics more accessible.

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    Area of Science:

    • Sports Analytics
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Tactical visualizations in sports analytics offer insights but pose challenges in connecting high-level information to visual patterns.
    • Existing visual-text linking studies inadequately support the reasoning process needed to interpret insights within game contexts.

    Purpose of the Study:

    • To propose InsightChaser, a novel approach for bridging tactical insights and soccer visualizations.
    • To enhance visual-text linking through visual reasoning and knowledge graph construction.
    • To improve user comprehension of complex sports tactics by integrating large language models (LLMs).

    Main Methods:

    • Constructing knowledge graphs to represent visual elements and contextual game information.
    • Integrating LLMs to retrieve relevant visual elements and establish explicit links with tactical insights.
    • Utilizing LLMs for reasoning explanations and visual effects to enhance visual-text links.
    • Developing an interactive visualization system for exploring and explaining enhanced links.

    Main Results:

    • Demonstrated the ability to link tactical insights with corresponding visual patterns in soccer.
    • Enhanced the interpretability of visualizations through LLM-powered reasoning and visual effects.
    • Facilitated interactive exploration and understanding of complex tactical information.

    Conclusions:

    • InsightChaser effectively bridges the gap between tactical insights and soccer visualizations.
    • The integration of LLMs and knowledge graphs significantly improves visual reasoning and user comprehension.
    • The developed system offers a promising direction for advanced sports analytics visualization.