# SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries

arXiv: https://arxiv.org/abs/2406.01273 · HTML: https://mlsanity.com/paper/2406.01273/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2024.

## Built on by (most cited first, 6 of 6)

- [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.08826/) (2025)
- [Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.11063/) (2025)
- [Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case Study](https://mlsanity.com/paper/2409.17580/) (2024)
- [Towards Automatic Soccer Commentary Generation with Knowledge-Enhanced Visual Reasoning](https://mlsanity.com/paper/2604.00057/) (2026)
- [SoccerLens: Grounded Soccer Video Understanding Beyond Accuracy](https://mlsanity.com/paper/2605.09598/) (2026)
- [Agentic generative AI for media content discovery at the national football league](https://mlsanity.com/paper/2510.07297/) (2025)

## Abstract (arXiv)

The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. This paper introduces SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to sports archives. Our evaluations indicate that SoccerRAG effectively handles complex queries, offering significant improvements over traditional retrieval systems in terms of accuracy and user engagement. The results underscore the potential of using RAG and LLMs in sports analytics, paving the way for future advancements in the accessibility and real-time processing of sports data.