This research addresses whether the ball's direction after a soccer free-kick can be accurately predicted solely by observing the shooter's kicking technique. To investigate this, we meticulously curated a dataset of soccer players executing free kicks and conducted manual temporal segmentation to identify the moment of the kick precisely. Our approach involves utilizing neural networks to develop a model that integrates Human Action Recognition (HAR) embeddings with contextual information, predicting the ball-on-goal position (BoGP) based on two temporal states: the kicker's run-up and the instant of the kick. The study encompasses a performance evaluation for eleven distinct HAR backbones, shedding light on their effectiveness in BoGP estimation during free-kick situations. An extra tabular metadata input is introduced, leading to an interesting model enhancement without introducing bias. The promising results reveal 69.1% accuracy when considering two primary BoGP classes: right and left. This underscores the model's proficiency in predicting the ball's destination towards the goal with high accuracy, offering promising implications for understanding free-kick dynamics in soccer.

通过利用神经网络，本研究提出了一种模型，结合人体动作识别(HAR)嵌入与上下文信息，通过观察运动员的踢球技术准确预测足球射门后的运动方向，取得了很高的准确率，为了解足球罚球动力学提供了有意义的启示。

通过踢球动作对足球进球位置进行分类