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/.
