Gait-Based Prediction of Penalty Kick Direction in Soccer

Abstract

Understanding and predicting penalty kick outcomes is critical in performance analysis and strategic decision-making in soccer. This study investigates the potential of gait-based biometrics to classify the intended shoot zone of penalty takers using temporal gait embeddings extracted from multiple state-of-the-art gait recognition backbones. We compile a comprehensive evaluation across several models and datasets, including baseline models and other models such as GaitPart, GLN, GaitSet, and GaitGL trained on OUMVLP, CASIA-B, and GREW. A standardized LSTM-based classifier is trained to predict the shooting zone from video-level gait sequences, using consistent train-test splits to ensure fair comparisons. While performance varies across model-dataset pairs, we observe that certain combinations yield better predictive accuracy, suggesting that the gait representation and the training data influence downstream task performance to some degree. This work demonstrates the feasibility of using gait as a predictive cue in sports analytics. It offers a structured benchmark for evaluating gait embeddings in the context of penalty shoot zone prediction.