# Predicting Soccer Penalty Kick Direction Using Human Action Recognition

arXiv: https://arxiv.org/abs/2507.12617 · HTML: https://mlsanity.com/paper/2507.12617/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning](https://mlsanity.com/paper/2104.14558/) (2021)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment](https://mlsanity.com/paper/1904.04346/) (2019)
- [RED: Reinforced Encoder-Decoder Networks for Action Anticipation](https://mlsanity.com/paper/1707.04818/) (2017)
- [Learning to score the figure skating sports videos](https://mlsanity.com/paper/1802.02774/) (2018)
- [A Survey on Content-Aware Video Analysis for Sports](https://mlsanity.com/paper/1703.01170/) (2017)
- [Towards cumulative race time regression in sports: I3D ConvNet transfer learning in ultra-distance running events](https://mlsanity.com/paper/2208.11191/) (2022)
- [Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action](https://mlsanity.com/paper/2312.15236/) (2023)
- [Design and Implementation of A Soccer Ball Detection System with Multiple Cameras](https://mlsanity.com/paper/2302.00123/) (2023)

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

- [MambaKick: Early Penalty Direction Prediction from HAR Embeddings](https://mlsanity.com/paper/2604.16588/) (2026)

## Abstract (arXiv)

Action anticipation has become a prominent topic in Human Action Recognition (HAR). However, its application to real-world sports scenarios remains limited by the availability of suitable annotated datasets. This work presents a novel dataset of manually annotated soccer penalty kicks to predict shot direction based on pre-kick player movements. We propose a deep learning classifier to benchmark this dataset that integrates HAR-based feature embeddings with contextual metadata. We evaluate twenty-two backbone models across seven architecture families (MViTv2, MViTv1, SlowFast, Slow, X3D, I3D, C2D), achieving up to 63.9% accuracy in predicting shot direction (left or right), outperforming the real goalkeepers' decisions. These results demonstrate the dataset's value for anticipatory action recognition and validate our model's potential as a generalizable approach for sports-based predictive tasks.