  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.
