Multi-Agent System for Comprehensive Soccer Understanding

Abstract: 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.

• The paper presents a unified framework integrating SoccerWiki, SoccerBench, and SoccerAgent to enable holistic soccer analysis.
• It employs a modular multi-agent system that orchestrates 18 specialized tools for robust text and video question answering.
• Experimental results demonstrate superior accuracy and context-aware reasoning, surpassing traditional monolithic models in soccer tasks.

Multi-Agent System for Comprehensive Soccer Understanding: An Expert Summary

Introduction

The paper "Multi-Agent System for Comprehensive Soccer Understanding" (2505.03735) presents a unified framework to advance the field of AI-powered soccer analytics. The authors identify critical limitations in current research: a focus on isolated visual tasks with minimal reasoning requirements and the proliferation of fragmented, specialist models unsuited for comprehensive, generalizable soccer understanding. To overcome these limitations, they introduce a set of resources and methods—SoccerWiki (a large-scale multimodal soccer knowledge base), SoccerBench (a soccer-specific multimodal QA benchmark), and SoccerAgent (a multi-agent reasoning system)—which together enable and evaluate holistic, knowledge-driven soccer analysis.

Datasets: SoccerWiki and SoccerBench

SoccerWiki

SoccerWiki is the first multimodal, large-scale knowledge base dedicated to soccer. It aggregates entity-level data for 9,471 players, 266 teams, 202 referees, and 235 venues sourced from Wikipedia, Flashscore, and curated match-level sources. Entities are annotated with images and detailed textual metadata ranging from biographies and career statistics to event annotations, providing dense context for knowledge-intensive reasoning and retrieval. The coverage spans top European leagues and recent international competitions.

SoccerBench

SoccerBench is designed as a holistic, multimodal QA benchmark for soccer understanding. It contains approximately 10,000 standardized multiple-choice QA pairs spanning 13 tasks and distributed across text, image, and video modalities. The tasks include but are not limited to: background/situational knowledge, camera status classification, jersey number and color recognition, score and time recognition, replay-event association, action classification, commentary generation, and multiview foul recognition.

Benchmark curation is accomplished through pipeline-driven integration of SoccerWiki with numerous established soccer datasets (e.g., SoccerNet, SoccerReplay-1988, SoccerNet-XFoul) and employs a hybrid of template-based and LLM-driven question generation. The multi-choice format is created with plausible distractors constructed by LLMs or sampling within annotation categories, ensuring benchmark complexity and robustness.

Methodology: SoccerAgent Multi-Agent System

SoccerAgent is introduced as a modular multi-agent system for soccer reasoning, explicitly targeting the limitations of monolithic MLLMs and specialist models. The framework formalizes the QA process as agentic task decomposition and sequential tool invocation:

• Agent Architecture: SoccerAgent consists of a planning agent ($\mathcal{A}_{plan}$) for decomposing a complex query into a toolchain and an execution agent ($\mathcal{A}_{exec}$) for iterative, history-aware tool execution.
• Toolbox Composition: The system integrates 18 tools: 12 soccer-specialized modules, 4 retrieval and image analysis tools, and 6 general multimodal parsing modules. Tools are drawn from existing open-source models or custom modules (e.g., action classification, match search, textual entity extraction, jersey number recognition).
• Planning and Execution Logic: For each query, the system determines the minimal set of tools required, plans an ordered chain, and executes instructions using structured, explicit markers. The process is interpretable, robust to execution errors, and history/context-aware.

The system is built to enforce a strict separation between chain planning and execution, supporting transparent ablation and extensibility via new soccer tools.

Experimental Evaluation

Benchmarking and Baselines

Evaluations are conducted on SoccerBench, comparing SoccerAgent to 11 strong baselines, including state-of-the-art commercial MLLMs (e.g., GPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash, Gemini 2.5 Pro) and open-source MLLMs (DeepSeek-v3, DeepSeek-R1, Qwen2.5-VL, LLaVA-OneVision, VideoLLaMA3, etc.). All systems are tested in a standardized multi-choice setting.

Main Results

• Performance Superiority: SoccerAgent achieves highest accuracy in text QA (85.0%) and video QA (73.3%), outperforming all baselines, including commercial offerings, even when not provided with multi-choice options during reasoning.
• Strong Soccer-Specific Reasoning: SoccerAgent excels in background knowledge QA and action classification, categories that require explicit soccer domain knowledge and contextual reasoning.
• Challenging Benchmark: Task-level performance of most baselines remains modest (often below 60%), indicating the necessity and complexity of both SoccerBench and the knowledge-driven approach instantiated by SoccerAgent.

Ablation Analysis

• Robustness to Prompts: Ablations demonstrate that SoccerAgent’s accuracy in toolchain planning and execution is relatively invariant to the presence/absence of additional task descriptions and few-shot execution examples, especially in visual domains.
• Role of Descriptions: Access to detailed task definitions benefits the planning stage, while excessive demonstration examples can diminish image/video reasoning (potentially due to overfitting to exemplars rather than generalizing tool logic).

Qualitative Analysis

SoccerAgent exhibits sophisticated decompositional reasoning chains, context-driven tool invocation, and error-correction capabilities (e.g., dynamically revising tool use after initial missteps).

Implications and Future Directions

This work delivers a standardized, extensible framework for soccer understanding tasks, emphasizing:

• The necessity of multimodal, up-to-date, and richly annotated domain knowledge for long-horizon, knowledge-intensive reasoning in specialized domains.
• The clear advantage of integrating multi-agent, tool-centric architectures over monolithic MLLMs for real-world task coverage and generalization.
• The establishment of SoccerBench and SoccerWiki as foundational resources, enabling the quantitative evaluation and continuous development of soccer intelligence systems.

Practical implications include benchmarking, ablation, error analysis, and the potential for transfer to other knowledge-driven sports domains or general video understanding. Future research may focus on expanding SoccerWiki to additional leagues or modalities, integrating online data streams for dynamic updates, and refining agent communication protocols for more advanced collaborative reasoning. The agentic design pattern also anticipates future advances in compositional neural-symbolic or LLM-based planning systems.

Conclusion

The presented framework systematically advances comprehensive AI-driven soccer understanding by unifying knowledge integration, modular agentic reasoning, and robust evaluation. SoccerWiki and SoccerBench supply irreplaceable testbeds for benchmarking both perception and knowledge reasoning. SoccerAgent’s modular multi-agent approach sets a new standard for soccer analysis and by extension for knowledge-intensive, task-compositional multimodal understanding. This paradigm is likely to stimulate further research in agent-based systems, sports analytics, and general multimodal QA.