SPORTSQL: An Interactive System for Real-Time Sports Reasoning and Visualization

TL;DR

SPORTSQL is an interactive system that enables real-time sports data querying and visualization using natural language, leveraging LLMs for understanding and generating SQL queries over live EPL data.

Contribution

The paper introduces SPORTSQL, a novel modular system integrating LLMs for natural language sports data querying and visualization, along with a new benchmark for evaluating such systems.

Findings

Effective real-time querying of sports data using natural language.

High accuracy in translating questions into SQL over live data.

Demonstrated user-friendly exploration of sports statistics.

Abstract

We present a modular, interactive system, SPORTSQL, for natural language querying and visualization of dynamic sports data, with a focus on the English Premier League (EPL). The system translates user questions into executable SQL over a live, temporally indexed database constructed from real-time Fantasy Premier League (FPL) data. It supports both tabular and visual outputs, leveraging the symbolic reasoning capabilities of Large Language Models (LLMs) for query parsing, schema linking, and visualization selection. To evaluate system performance, we introduce the Dynamic Sport Question Answering benchmark (DSQABENCH), comprising 1,700+ queries annotated with SQL programs, gold answers, and database snapshots. Our demo highlights how non-expert users can seamlessly explore evolving sports statistics through a natural, conversational interface.