# Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv: https://arxiv.org/abs/2401.01505 · HTML: https://mlsanity.com/paper/2401.01505/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 65 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 30 of 47)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering](https://mlsanity.com/paper/1906.02467/) (2019)
- [TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering](https://mlsanity.com/paper/1704.04497/) (2017)
- [Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding](https://mlsanity.com/paper/2311.08046/) (2023)
- [LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment](https://mlsanity.com/paper/2310.01852/) (2023)
- [MERLOT: Multimodal Neural Script Knowledge Models](https://mlsanity.com/paper/2106.02636/) (2021)
- [MovieQA: Understanding Stories in Movies through Question-Answering](https://mlsanity.com/paper/1512.02902/) (2015)
- [Just Ask: Learning to Answer Questions from Millions of Narrated Videos](https://mlsanity.com/paper/2012.00451/) (2020)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling](https://mlsanity.com/paper/2111.12681/) (2021)
- [All in One: Exploring Unified Video-Language Pre-training](https://mlsanity.com/paper/2203.07303/) (2022)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering](https://mlsanity.com/paper/1904.04357/) (2019)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [Motion-Appearance Co-Memory Networks for Video Question Answering](https://mlsanity.com/paper/1803.10906/) (2018)
- [MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions](https://mlsanity.com/paper/2105.07404/) (2021)
- [Uncertainty-aware Score Distribution Learning for Action Quality Assessment](https://mlsanity.com/paper/2006.07665/) (2020)

## Built on by (most cited first, 30 of 41)

- [Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models](https://mlsanity.com/paper/2504.15271/) (2025)
- [MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos](https://mlsanity.com/paper/2406.08407/) (2024)
- [SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2410.08474/) (2024)
- [X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model](https://mlsanity.com/paper/2404.06332/) (2024)
- [Towards Universal Soccer Video Understanding](https://mlsanity.com/paper/2412.01820/) (2024)
- [Multi-Agent System for Comprehensive Soccer Understanding](https://mlsanity.com/paper/2505.03735/) (2025)
- [VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding](https://mlsanity.com/paper/2412.02186/) (2024)
- [SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports](https://mlsanity.com/paper/2511.06499/) (2025)
- [Video Action Differencing](https://mlsanity.com/paper/2503.07860/) (2025)
- [Language and Multimodal Models in Sports: A Survey of Datasets and Applications](https://mlsanity.com/paper/2406.12252/) (2024)
- [SoccerMaster: A Vision Foundation Model for Soccer Understanding](https://mlsanity.com/paper/2512.11016/) (2025)
- [Aurelia: Test-time Reasoning Distillation in Audio-Visual LLMs](https://mlsanity.com/paper/2503.23219/) (2025)
- [Recurrent Attention-based Token Selection for Efficient Streaming Video-LLMs](https://mlsanity.com/paper/2510.17364/) (2025)
- [SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game Understanding](https://mlsanity.com/paper/2505.16630/) (2025)
- [When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives](https://mlsanity.com/paper/2406.12084/) (2024)
- [Domain Adaptation of VLM for Soccer Video Understanding](https://mlsanity.com/paper/2505.13860/) (2025)
- [Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video](https://mlsanity.com/paper/2406.14877/) (2024)
- [FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding](https://mlsanity.com/paper/2504.19514/) (2025)
- [FineQuest: Adaptive Knowledge-Assisted Sports Video Understanding via Agent-of-Thoughts Reasoning](https://mlsanity.com/paper/2509.11796/) (2025)
- [DeepSport: A Multimodal Large Language Model for Comprehensive Sports Video Reasoning via Agentic Reinforcement Learning](https://mlsanity.com/paper/2511.12908/) (2025)
- [The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering](https://mlsanity.com/paper/2501.07109/) (2025)
- [SV3.3B: A Sports Video Understanding Model for Action Recognition](https://mlsanity.com/paper/2507.17844/) (2025)
- [TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies?](https://mlsanity.com/paper/2509.15602/) (2025)
- [Let's Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models' Understanding of Sports](https://mlsanity.com/paper/2510.01247/) (2025)
- [SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding](https://mlsanity.com/paper/2601.21666/) (2026)
- [DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering](https://mlsanity.com/paper/2503.15887/) (2025)
- [Shot2Tactic-Caption: Multi-Scale Captioning of Badminton Videos for Tactical Understanding](https://mlsanity.com/paper/2510.14617/) (2025)
- [CL-VISTA: Benchmarking Continual Learning in Video Large Language Models](https://mlsanity.com/paper/2604.00677/) (2026)
- [WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting](https://mlsanity.com/paper/2607.18084/) (2026)
- [Bridging the Gap Between Multimodal Foundation Models and World Models](https://mlsanity.com/paper/2510.03727/) (2025)

## Abstract (arXiv)

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance.