# Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage

arXiv: https://arxiv.org/abs/2202.12259 · HTML: https://mlsanity.com/paper/2202.12259/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image](https://mlsanity.com/paper/1701.00295/) (2017)
- [Game Plan: What AI can do for Football, and What Football can do for AI](https://mlsanity.com/paper/2011.09192/) (2020)
- [Feature Boosting Network For 3D Pose Estimation](https://mlsanity.com/paper/1901.04877/) (2019)
- [3D Human Pose Estimation with 2D Marginal Heatmaps](https://mlsanity.com/paper/1806.01484/) (2018)
- [Optimising Game Tactics for Football](https://mlsanity.com/paper/2003.10294/) (2020)
- [Learning Football Body-Orientation as a Matter of Classification](https://mlsanity.com/paper/2106.00359/) (2021)

## Built on by (most cited first, 1 of 1)

- [AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements](https://mlsanity.com/paper/2405.12070/) (2024)

## Abstract (arXiv)

As an amateur goalkeeper playing grassroots soccer, who better to learn from than top professional goalkeepers? In this paper, we harness computer vision and machine learning models to appraise the save technique of professionals in a way those at lower levels can learn from. We train an unsupervised machine learning model using 3D body pose data extracted from broadcast footage to learn professional goalkeeper technique. Then, an "expected saves" model is developed, from which we can identify the optimal goalkeeper technique in different match contexts.