A Survey of Large Models in Sports

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: this https URL.

36 pages, 4 figures, 6 tables. Accepted to Findings of ACL 2026

Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2608.14377 [cs.CL]

(or arXiv:2608.14377v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.14377 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.18653/v1/2026.findings-acl.1851 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.