MambaKick AI Framework Predicts Soccer Penalty Kick Direction Using Human Action Recognition
MambaKick, a novel AI framework, forecasts the direction of penalty kicks in soccer by examining brief video clips taken at the moment of ball contact. Rather than relying on biomechanical features or kinematic reconstruction, it utilizes pretrained human action recognition (HAR) embeddings. The framework integrates selective state-space models for effective sequence aggregation and includes contextual information such as field side and footedness to minimize confusion. MambaKick matches or enhances the performance of strong embeddings across various HAR backbones, catering to goalkeepers' limited response time during penalty kicks. This approach focuses on contact-centered video segments and a streamlined temporal predictor. This research, cataloged as arXiv:2604.16588v1, propels AI usage in sports analytics, highlighting real-time prediction and efficiency.
Key facts
- MambaKick is an AI framework for predicting soccer penalty kick direction
- Uses pretrained human action recognition (HAR) embeddings from contact-centered video segments
- Employs selective state-space models (Mamba) for efficient sequence aggregation
- Incorporates contextual metadata like field side and footedness to reduce ambiguity
- Consistently improves or matches strong embedding performance across HAR backbones
- Addresses extreme time constraints goalkeepers face during penalty kicks
- Paper identified as arXiv:2604.16588v1 with cross-disciplinary announcement type
- Avoids explicit kinematic reconstruction and handcrafted biomechanical features
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- arXiv