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A Survey of Large Models in Sports

Paper · 2026 · Yichen Xu, Jianzhe Ma, Chuhan Wang et al.

Summary

This survey maps how large models, especially LLMs and MLLMs such as GPT-4 and Gemini, are being applied to sports. Earlier reviews looked at narrow slices, such as exercise recommendation or sports medicine. The authors instead give a broad picture of tasks, data and open problems. They used systematic snowballing, with forward and backward citation searches following PRISMA, to collect 241 core papers published between January 2020 and July 2025.

The work is organised into three parts:

  • Taxonomy. It spans 19 tasks across six kinds of stakeholders: athletes together with their trainers, coaching and teaching staff, referees, the media and fans, researchers, and the sports industry. For each task the authors describe the technical paradigms and the usual metrics.
  • Datasets and benchmarks. The authors review the data resources available for these tasks.
  • Challenges. They name four: hallucination, long and high-frame-rate video, interpretability, and real-world deployment.

The overall picture is that pre-trained, fine-tuned models generally beat systems built from scratch. Text-only shortcuts, however, lag behind models with proper visual alignment.

The survey and its maintained GitHub list (Awesome_Large_Models_In_Sports) give newcomers a structured entry point to the field.

Key points

  • The volume of research grew quickly: papers on large models in sports rose from one in 2020 to 78 in 2024, and kept growing in 2025.
  • On YourSkatingCoach, a fine-tuned T5 scores 0.27 BLEU-4 for training-plan generation. A vanilla Transformer trained from scratch reaches only 0.04.
  • On SoccerNet-v2 action spotting, the results (t-AmAP) are:
    • fine-tuned Soccer-CLIP: 75.7%
    • COMEDIAN: 73.1%
    • Llama 3.1-8B prompted with textual commentary: 60.8%
  • On SoccerNet-Caption commentary generation, MatchVoice reaches 38.42 CIDEr, while zero-shot Video-LLaMA manages only 3.44.
  • The authors propose four future directions:
    • trustworthy sports AI
    • streaming and long-video mechanisms
    • knowledge grounding with tool use
    • in-the-wild evaluation with edge deployment

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Renmin University of ChinaBeijing · China139 works · 17,752 citations🎓Beijing University of Posts and TelecommunicationsBeijing · China78 works · 33,365 citations🎓Sichuan UniversityChengdu · China20 works · 1,293 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 worksEconomics, Econometrics and Finance · 23 worksEconomics and Econometrics · 16 works

Cite

Cite as Yichen Xu, Jianzhe Ma, Chuhan Wang, Zhonghao Cao, Liangyu Chen, Wenxuan Wang and Qin Jin. 2026. A Survey of Large Models in Sports. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1851

@inproceedings{xu2026survey,
  title = {{A Survey of Large Models in Sports}},
  author = {Yichen Xu, Jianzhe Ma, Chuhan Wang, Zhonghao Cao, Liangyu Chen, Wenxuan Wang and Qin Jin},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1851},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1851}
}

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