It’s not all in your feet: Improving penalty kick performance with humanavatar interaction and machine learning  

作　　者：Jean-Luc Bloechle Julien Audiffren Thibaut Le Naour Andrea Alli Dylan Simoni Gabriel Wüthrich Jean-Pierre Bresciani 

机构地区：[1]Control and Perception Laboratory,University of Fribourg,Bd Perolles 90,1700 Fribourg,Switzerland [2]Motion-up,Le Prisme,Place Albert Einstein,56000 Vannes,France [3]FC Basel 1893,Birsstrasse 320A,4002 Basel,Switzerland

出　　处：《The Innovation》2024年第2期115-121,共7页创新（英文）

基　　金：This work was supported by the University of Fribourg.The authors would like to thank the managers and trainers of FC Luzern and FC Basel,with special thanks to Christian Schmidt,as well as all players who participated in the experiment.

摘　　要：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.

关 键 词：kick PENALTY learning 

分 类 号：G843[文化科学—体育训练]