title: Multi-Agent System for Comprehensive Soccer Understanding
summary: 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/.
paper_authors._id: 681acb9c285636e830d37c8e
paper_authors.name: Jiayuan Rao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-07T08:08:50.372Z
paper_authors._id: 6625ce8074ae2df4e3effa92
paper_authors.avatarUrl: /avatars/686cd85cf8d3d8f0beb0737811144294.svg
paper_authors.fullname: Jiayuan Rao 饶珈源
paper_authors.type: user
paper_authors.user: Homie0609
paper_authors._id: 681acb9c285636e830d37c8f
paper_authors.name: Zifeng Li
paper_authors._id: 681acb9c285636e830d37c90
paper_authors.name: Haoning Wu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-07T02:56:09.106Z
paper_authors._id: 632c7a0d1d303f5f9acf01b8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
paper_authors.fullname: Haoning Wu
paper_authors.type: user
paper_authors.user: haoningwu
paper_authors._id: 681acb9c285636e830d37c91
paper_authors.name: Ya Zhang
paper_authors._id: 681acb9c285636e830d37c92
paper_authors.name: Yanfeng Wang
paper_authors._id: 681acb9c285636e830d37c93
paper_authors.name: Weidi Xie
paper_title: Multi-Agent System for Comprehensive Soccer Understanding
paper_summary: 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/.
paper_ai_summary: A framework for comprehensive soccer understanding includes a knowledge base, benchmark, and multi-agent system for reasoning and evaluation.
paper_ai_keywords: SoccerWiki SoccerBench multimodal multi-choice QA multi-agent system collaborative reasoning MLLMs