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.
@misc{rao2025socceragent,
title = {Multi-Agent System for Comprehensive Soccer Understanding},
author = {Rao, Jiayuan and Li, Zifeng and Wu, Haoning and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
journal = {arXiv preprint arXiv:2505.03735},
year = {2025},
}