README.md

metadata

license: cc-by-nc-4.0
language:
  - zh
  - en
pretty_name: SportsTime
task_categories:
  - visual-question-answering
  - question-answering
size_categories:
  - 10K<n<100K
tags:
  - sports
  - video-question-answering
  - long-video-understanding
  - temporal-reasoning
  - temporal-compositional-reasoning
  - eccv-2026
configs:
  - config_name: zh
    data_files:
      - split: train
        path: data/zh/train.jsonl
      - split: test
        path: data/zh/test.jsonl
  - config_name: en
    data_files:
      - split: train
        path: data/en/train.jsonl
      - split: test
        path: data/en/test.jsonl
extra_gated_heading: Request Access to SportsTime
extra_gated_description: >-
  Access requests are reviewed manually, usually within one week. Please provide
  your affiliation and intended non-commercial research or educational use.
extra_gated_button_content: Submit access request
extra_gated_fields:
  Name:
    type: text
    required: true
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    required: true
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    required: false
    description: Optional for student applicants.
  Intended use:
    type: select
    required: true
    options:
      - Research
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      - Other
  I agree to use SportsTime only for non-commercial research or educational purposes and not redistribute the files:
    type: checkbox
    required: true

SportsTime

SportsTime is a long-form sports video question answering benchmark for temporal compositional reasoning, accepted to ECCV 2026.

It contains 14,326 open-ended QA pairs with 50,000+ step-wise temporal evidence annotations across 1,575 videos and five team sports: basketball, American football, ice hockey, soccer, and volleyball.

Dataset

This Hugging Face dataset repository provides the annotation files, official train/test split, and video files. Download access is gated and reviewed manually, usually within one week.

Configuration	Train	Test
Chinese (zh)	11,540	2,786
English (en)	11,540	2,786

Video files are stored under videos/{sport}/{full|highlight}/{video_id}.mp4. See metadata/video_manifest.json for the complete file list and sizes.

Usage

from datasets import load_dataset

zh = load_dataset("Ustiniansy/SportsTime", "zh")
en = load_dataset("Ustiniansy/SportsTime", "en")

Each sample contains the question, answer, Chain-of-Time reasoning, temporal references, sport, video type, language, and the associated video_id.

Links

• GitHub: https://github.com/ustiniansy/SportsTime
• Paper: http://arxiv.org/abs/2604.22226

Citation

@article{cao2026sportstime,
  title   = {Towards Temporal Compositional Reasoning in Long-Form Sports Videos},
  author  = {Cao, Siyu and Zhang, Lu and Zeng, Ruizhe and Liu, Zhi-yong},
  journal = {arXiv preprint arXiv:2604.22226},
  year    = {2026},
  eprint  = {2604.22226},
  archivePrefix = {arXiv},
  url     = {http://arxiv.org/abs/2604.22226}
}

License

SportsTime is released under the Creative Commons Attribution-NonCommercial 4.0 International license.