Bloechle Jean-Luc, Audiffren Julien, Le Naour Thibaut, Alli Andrea, Simoni Dylan, Wüthrich Gabriel, Bresciani Jean-Pierre
Control and Perception Laboratory, University of Fribourg, Bd Perolles 90, 1700 Fribourg, Switzerland.
Motion-up, Le Prisme, Place Albert Einstein, 56000 Vannes, France.
Innovation (Camb). 2024 Feb 6;5(2):100584. doi: 10.1016/j.xinn.2024.100584. eCollection 2024 Mar 4.
Penalty kicks are increasingly decisive in major international football competitions. Yet, over 30% 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 toward 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 13 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 20 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.
点球在重大国际足球比赛中越来越具有决定性作用。然而，超过30%的点球会罚失。点球的结果通常取决于罚球者利用守门员的预判动作将球踢向球门空当一侧的能力。不幸的是，使用传统方法很难训练这种能力。我们使用了一个显示全息守门员的增强现实模拟器，对13名年轻的精英球员进行点球表现测试和训练。机器学习算法通过维持最佳训练难度水平来优化学习率。进行10次每次20次罚球的训练后，重新调整方向的阈值降低了120毫秒，相对于基线阈值降低了28%。重要的是，所有接受训练的球员都出现了重新调整方向阈值降低的情况，在其他条件相同的情况下，这相当于成功率估计提高了35%。
