# FACTS: Fine-Grained Action Classification for Tactical Sports

arXiv: https://arxiv.org/abs/2412.16454 · HTML: https://mlsanity.com/paper/2412.16454/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 37 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Built on by (most cited first, 4 of 4)

- [DeepSport: A Multimodal Large Language Model for Comprehensive Sports Video Reasoning via Agentic Reinforcement Learning](https://mlsanity.com/paper/2511.12908/) (2025)
- [Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges](https://mlsanity.com/paper/2505.03991/) (2025)
- [BoxMind: Closed-loop AI strategy optimization for elite boxing validated in the 2024 Olympics](https://mlsanity.com/paper/2601.11492/) (2026)
- [Video-Based Sexual Harassment Detection Using Fine-Tuned Video Transformers](https://mlsanity.com/paper/2610.15354/) (2026)

## Abstract (arXiv)

Classifying fine-grained actions in fast-paced, close-combat sports such as fencing and boxing presents unique challenges due to the complexity, speed, and nuance of movements. Traditional methods reliant on pose estimation or fancy sensor data often struggle to capture these dynamics accurately. We introduce FACTS, a novel transformer-based approach for fine-grained action recognition that processes raw video data directly, eliminating the need for pose estimation and the use of cumbersome body markers and sensors. FACTS achieves state-of-the-art performance, with 90% accuracy on fencing actions and 83.25% on boxing actions. Additionally, we present a new publicly available dataset featuring 8 detailed fencing actions, addressing critical gaps in sports analytics resources. Our findings enhance training, performance analysis, and spectator engagement, setting a new benchmark for action classification in tactical sports.