@inproceedings{xu-etal-2026-survey,
    title = "A Survey of Large Models in Sports",
    author = "Xu, Yichen  and
      Ma, Jianzhe  and
      Wang, Chuhan  and
      Cao, Zhonghao  and
      Chen, Liangyu  and
      Wang, Wenxuan  and
      Jin, Qin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1851/",
    pages = "37154--37189",
    ISBN = "979-8-89176-395-1",
    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."
}