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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.17157 |
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| _version_ | 1866916914051153920 |
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| author | Martinez, Sebastian Ahuja, Naman Bardoliya, Fenil Bryan, Chris Gupta, Vivek |
| author_facet | Martinez, Sebastian Ahuja, Naman Bardoliya, Fenil Bryan, Chris Gupta, Vivek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPORTSQL: An Interactive System for Real-Time Sports Reasoning and Visualization Martinez, Sebastian Ahuja, Naman Bardoliya, Fenil Bryan, Chris Gupta, Vivek Computation and Language 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. |
| title | SPORTSQL: An Interactive System for Real-Time Sports Reasoning and Visualization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.17157 |