# Multi-Agent System for Comprehensive Soccer Understanding

arXiv: https://arxiv.org/abs/2505.03735 · HTML: https://mlsanity.com/paper/2505.03735/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 32 of its 68 references that are in the map.
- First posted on arXiv 6 May 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 30 of 32)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [LLaVA-Video: Video Instruction Tuning With Synthetic Data](https://mlsanity.com/paper/2410.02713/) (2024)
- [VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding](https://mlsanity.com/paper/2501.13106/) (2025)
- [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://mlsanity.com/paper/2409.02813/) (2024)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling](https://mlsanity.com/paper/2501.00574/) (2024)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment](https://mlsanity.com/paper/2204.03646/) (2022)
- [AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator](https://mlsanity.com/paper/2402.09742/) (2024)
- [Embodied LLM Agents Learn to Cooperate in Organized Teams](https://mlsanity.com/paper/2403.12482/) (2024)
- [GTA: A Benchmark for General Tool Agents](https://mlsanity.com/paper/2407.08713/) (2024)
- [MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models](https://mlsanity.com/paper/2408.02718/) (2024)
- [TacticAI: an AI assistant for football tactics](https://mlsanity.com/paper/2310.10553/) (2023)
- [MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos](https://mlsanity.com/paper/2406.08407/) (2024)
- [Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports](https://mlsanity.com/paper/2401.01505/) (2024)
- [MatchTime: Towards Automatic Soccer Game Commentary Generation](https://mlsanity.com/paper/2406.18530/) (2024)
- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation](https://mlsanity.com/paper/2303.14655/) (2023)
- [Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review](https://mlsanity.com/paper/2502.11518/) (2025)

## Built on by (most cited first, 18 of 18)

- [SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence](https://mlsanity.com/paper/2505.17012/) (2025)
- [SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports](https://mlsanity.com/paper/2511.06499/) (2025)
- [SoccerMaster: A Vision Foundation Model for Soccer Understanding](https://mlsanity.com/paper/2512.11016/) (2025)
- [DeepSport: A Multimodal Large Language Model for Comprehensive Sports Video Reasoning via Agentic Reinforcement Learning](https://mlsanity.com/paper/2511.12908/) (2025)
- [SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System](https://mlsanity.com/paper/2511.17943/) (2025)
- [GenTac: Generative Modeling and Forecasting of Soccer Tactics](https://mlsanity.com/paper/2604.11786/) (2026)
- [Stepping VLMs onto the Court: Benchmarking Spatial Intelligence in Sports](https://mlsanity.com/paper/2603.09896/) (2026)
- [Multi-Modal Soccer Scene Analysis with Masked Pre-Training](https://mlsanity.com/paper/2512.19528/) (2025)
- [Learning Consistent Temporal Grounding between Related Tasks in Sports Coaching](https://mlsanity.com/paper/2603.18453/) (2026)
- [Towards Automatic Soccer Commentary Generation with Knowledge-Enhanced Visual Reasoning](https://mlsanity.com/paper/2604.00057/) (2026)
- [SoccerNet 2026 Challenges Results](https://mlsanity.com/paper/2607.07320/) (2026)
- [Towards Comprehensive Basketball Understanding](https://mlsanity.com/paper/2608.23435/) (2026)
- [SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing](https://mlsanity.com/paper/2604.23392/) (2026)
- [MSUE: Multi-Modal Soccer Understanding Expert](https://mlsanity.com/paper/2606.12106/) (2026)
- [TreeSoc: Tree-Structured Dynamic Reasoning and Tool Synergy for Soccer Video Understanding](https://mlsanity.com/paper/2607.10990/) (2026)
- [BasketEvent: Understanding Who Did What and When in Basketball Videos](https://mlsanity.com/paper/2607.21267/) (2026)
- [SportsGrounder: Proposal-Aided Interleaved Grounding for Dense Sports Video Reasoning](https://mlsanity.com/paper/2608.07932/) (2026)
- [Semantic Action Graph: A Shared Representation for Agent Grounding and Human Interpretation of Sports Highlights](https://mlsanity.com/paper/2609.20768/) (2026)

## Abstract (arXiv)

Recent advances in 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. Concretely, 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 multimodal (text, image, video) multi-choice QA pairs across 13 distinct tasks; (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 comparisons with representative MLLMs on SoccerBench highlight the superiority of our agentic system.