# CaddieSet: A Golf Swing Dataset with Human Joint Features and Ball Information

arXiv: https://arxiv.org/abs/2508.20491 · HTML: https://mlsanity.com/paper/2508.20491/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [GolfDB: A Video Database for Golf Swing Sequencing](https://mlsanity.com/paper/1903.06528/) (2019)
- [BayesNAM: Leveraging Inconsistency for Reliable Explanations](https://mlsanity.com/paper/2411.06367/) (2024)

## Abstract (arXiv)

Recent advances in deep learning have led to more studies to enhance golfers' shot precision. However, these existing studies have not quantitatively established the relationship between swing posture and ball trajectory, limiting their ability to provide golfers with the necessary insights for swing improvement. In this paper, we propose a new dataset called CaddieSet, which includes joint information and various ball information from a single shot. CaddieSet extracts joint information from a single swing video by segmenting it into eight swing phases using a computer vision-based approach. Furthermore, based on expert golf domain knowledge, we define 15 key metrics that influence a golf swing, enabling the interpretation of swing outcomes through swing-related features. Through experiments, we demonstrated the feasibility of CaddieSet for predicting ball trajectories using various benchmarks. In particular, we focus on interpretable models among several benchmarks and verify that swing feedback using our joint features is quantitatively consistent with established domain knowledge. This work is expected to offer new insight into golf swing analysis for both academia and the sports industry.