A Survey of Large Models in Sports

2026-08-14

AI Summary

This study addresses the impeded development of sports large language models (LLMs) caused by the absence of systematic reviews by presenting the first comprehensive survey in this domain. Employing bibliometric analysis, multimodal evaluation, and benchmark assessments, we systematically synthesize task applications, data resources, and technical challenges. We construct a panoramic landscape of sports LLMs, delineate future research directions, and release an open-source knowledge base comprising multi-source datasets and benchmarks. By filling a critical gap in the literature, this work establishes essential infrastructure and theoretical foundations to advance both academic research and industrial deployment in sports intelligence.

Abstract

Sports have witnessed growing global enthusiasm in recent years, serving as a vital force for physical health, cultural exchange, social connection, and economic growth. The rapid advancement of large models, particularly (multimodal) large language models (M)LLMs, has demonstrated transformative potential to reshape sports understanding, analysis, and interaction across diverse domains. This paper presents a comprehensive survey of large models in sports, including (i) an overview of tasks and applications across different participant groups; (ii) a detailed analysis of sports-related datasets and benchmarks; and (iii) a critical discussion of current challenges and future directions. Our goal is to establish a foundation for advancing research and practical development of large-model-driven sports intelligence. An open-source GitHub repository is maintained at: https://github.com/Road2Redemption/Awesome_Large_Models_In_Sports1.

Problem

• Large Models
• Sports Intelligence
• Multimodal LLMs
• Survey

Innovation

• Large Models in Sports
• Multimodal LLMs
• Sports Benchmarks
• Survey
• Sports Intelligence