Open AccessSoccer goalkeeper expertise identification based on eye movements
Author(s) -
Benedikt Hosp,
Florian Schultz,
Oliver Höner,
Enkelejda Kasneci
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0251070
Subject(s) - perception , league , computer science , identification (biology) , athletes , artificial intelligence , perspective (graphical) , gaze , eye movement , eye tracking , presentation (obstetrics) , human–computer interaction , psychology , medicine , botany , physics , radiology , astronomy , neuroscience , biology , physical therapy
By focusing on high experimental control and realistic presentation, the latest research in expertise assessment of soccer players demonstrates the importance of perceptual skills, especially in decision making. Our work captured omnidirectional in-field scenes displayed through virtual reality glasses to 12 expert players (picked by DFB), 10 regional league intermediate players, and13 novice soccer goalkeepers in order to assess the perceptual skills of athletes in an optimized manner. All scenes were shown from the perspective of the same natural goalkeeper and ended after the return pass to that goalkeeper. Based on the gaze behavior of each player, we classified their expertise with common machine learning techniques. Our results show that eye movements contain highly informative features and thus enable a classification of goalkeepers between three stages of expertise, namely elite youth player, regional league player, and novice, at a high accuracy of 78.2%. This research underscores the importance of eye tracking and machine learning in perceptual expertise research and paves the way for perceptual-cognitive diagnosis as well as future training systems.