Multi-Agent System for Comprehensive Soccer Understanding

• URL: http://arxiv.org/abs/2505.03735v1
• Date: Tue, 06 May 2025 17:59:31 GMT
• Title: Multi-Agent System for Comprehensive Soccer Understanding
• Authors: Jiayuan Rao, Zifeng Li, Haoning Wu, Ya Zhang, Yanfeng Wang, Weidi Xie,
• Abstract summary: We construct SoccerWiki, the first large-scale multimodal soccer knowledge base.<n>We present SoccerBench, the largest and most comprehensive soccer-specific benchmark.<n>We introduce SoccerAgent, a novel multi-agent system that decomposes complex soccer questions.
• Score: 56.28536879015841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in AI-driven soccer understanding have demonstrated rapid progress, yet existing research predominantly focuses on isolated or narrow tasks. To bridge this gap, we propose a comprehensive framework for holistic soccer understanding. Specifically, we make the following contributions in this paper: (i) we construct SoccerWiki, the first large-scale multimodal soccer knowledge base, integrating rich domain knowledge about players, teams, referees, and venues to enable knowledge-driven reasoning; (ii) we present SoccerBench, the largest and most comprehensive soccer-specific benchmark, featuring around 10K standardized multimodal (text, image, video) multi-choice QA pairs across 13 distinct understanding tasks, curated through automated pipelines and manual verification; (iii) we introduce SoccerAgent, a novel multi-agent system that decomposes complex soccer questions via collaborative reasoning, leveraging domain expertise from SoccerWiki and achieving robust performance; (iv) extensive evaluations and ablations that benchmark state-of-the-art MLLMs on SoccerBench, highlighting the superiority of our proposed agentic system. All data and code are publicly available at: https://jyrao.github.io/SoccerAgent/.

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