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arXiv · 2512.21877

CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Parth Agarwal·Navya Kommuri·Trizal Garg·Prisha Singhal·Dhruv Shah·Vaibhav Devraj·Yash Sinha·Jagat Sesh Challa·Murari Mandal·Dhruv Kumar
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Abstract

Cricket is the second most popular sport worldwide, with billions of fans seeking advanced statistical insights unavailable through standard web searches. Although LLMs have advanced significantly in Text-to-SQL tasks, their capability to handle domain-specific nuances and multilingual requirements in sports analytics remains under-explored. We present CricBench, a benchmark suite evaluating the intrinsic SQL generation abilities of LLMs on cricket data across four formats: Test, ODI, T20I, and IPL. We curate a Gold-Standard dataset of 2,654 evaluation instances across four languages (English, Hindi, Punjabi, and Telugu). We evaluate seven models, GPT-5 Mini, Claude Sonnet 4, DeepSeek R1 and V3, Qwen 235B, Llama 3.1, and Gemma 2, using schema-only prompting. No single model dominates across all formats: GPT-5 Mini leads on Test cricket (12.4% DMA), Qwen 235B leads on IPL (28.7%) and T20I (17.5%), and all models score 0% on hard ODI queries. All models show a stark disconnect between syntactic validity (>98% execution accuracy) and semantic correctness (<29% DMA), with a domain gap of 37-55 percentage points versus BIRD. To our knowledge, CricBench is the first Text-to-SQL benchmark for cricket analytics.

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Parth Agarwal, Navya Kommuri, Trizal Garg, Prisha Singhal, Dhruv Shah, Vaibhav Devraj, Yash Sinha, Jagat Sesh Challa, Murari Mandal, Dhruv Kumar. 2025-12-26. CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics. https://arxiv.org/abs/2512.21877

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