Dynamic Descriptive Analytics in Football: A Case Study with Retrieval-Augmented Generation for Structured Data

The rapid evolution of football (soccer) analytics has been driven by advances in structured data analysis and recently also by Large Language Models (LLMs). However, existing methods often fail to adapt dynamically to evolving queries and lack contextual richness. This paper presents a novel retrieval augmented generation (RAG) approach tailored to descriptive football analytics, which leverages spatio-temporal and opponent-related data to transform structured event and player data into actionable insights. Our approach was evaluated using a subset from the 2023/24 season of the first German division (1. Bundesliga) over multiple game weeks, achieving an average accuracy of 63.3% in generating responses, setting a first benchmark. In particular, our approach demonstrated strong performance in answering temporal and spatial queries with an accuracy of 70%, while challenges in player-specific queries highlight opportunities for further refinement. These results underscore the potential of RAG to improve decision making for analysts, sports journalists, and potentially coaches by providing dynamic and query-specific insights, paving the way for advanced applications in descriptive sports analytics and interdisciplinary approaches in digital transformation.