TL;DR
MambaKick is a learning-based framework that predicts penalty shot directions in soccer using pretrained HAR embeddings and lightweight temporal models, enabling low-latency decision support.
Contribution
It introduces a novel combination of pretrained human action recognition embeddings with efficient temporal modeling for sports intention prediction.
Findings
Achieves up to 53.1% accuracy for three-class prediction
Improves or matches baseline performance across HAR backbones
Demonstrates practical low-latency prediction in real-world sports footage
Abstract
Penalty kicks in soccer are decided under extreme time constraints, where goalkeepers benefit from anticipating shot direction from the kickers motion before or around ball contact. In this paper, MambaKick is presented as a learning-based framework for penalty direction prediction that leverages pretrained human action recognition (HAR) embeddings extracted from contact-centered short video segments and combines them with a lightweight temporal predictor. Rather than relying on explicit kinematic reconstruction or handcrafted biomechanical features, the approach reuses transferable spatiotemporal representations and utilizes selective state-spare models (Mamba) for efficient sequence aggregation. Simple contextual metadata (e.g., field side and footedness) are also considered as complementary cues that may reduce ambiguity in real-world footage. Across a range of HAR backbones,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
