It’s not all in your feet: Improving penalty kick performance with human-avatar interaction and Machine Learning

Abstract

Penalty kicks are increasingly decisive in major international football competitions. Yet, over thirty percent of shootout kicks are missed. The outcome of the kick often relies on the ability of the penalty taker to exploit anticipatory movements of the goalkeeper to redirect the kick towards the open side of the goal. Unfortunately, this ability is difficult to train using classical methods. We used an Augmented-Reality simulator displaying an holographic goalkeeper to test and train penalty kick performance with thirteen young elite players. Machine Learning algorithms were used to optimize the learning rate by maintaining an optimal level of training difficulty. Ten training sessions of twenty kicks reduced the redirection threshold by 120 ms, which constituted a 28% reduction with respect to the baseline threshold. Importantly, redirection threshold reduction was observed for all trained players, and all things being equal, it corresponded to an estimated 35% improvement of the success rate.