Expertise Classification of Soccer Goalkeepers in Highly Dynamic Decision Tasks: A Deep Learning Approach for Temporal and Spatial Feature Recognition of Fixation Image Patch Sequences - Hosp, Benedikt; Schultz, Florian; Kasneci, Enkelejda; Höner, Oliver (Frontiers in Sports and Active Living, 2021)
The focus of expertise research moves constantly forward and includes cognitive factors like visual information perception and processing. In highly dynamic tasks, such as decision-making in sports, these factors become more important to build a foundation for diagnostic systems and adaptive learning environments. A lot of research focuses on behavioral features. However, the underlying cognitive mechanisms have been poorly understood, since there is a lack of adequate analysis methods of complex eye-tracking data, going beyond aggregated fixations and saccades. There are no generally applicable statements about specific perceptual features that explain expertise. However, these mechanisms are an important part of expertise, especially in decision-making in sports games, as highly trained perceptual-cognitive abilities can give athletes some advantage. We developed a deep learning approach that independently finds latent perceptual features in fixation image patches and derives expertise based solely on these fixation patches of the gaze behavior of athletes in an elaborately implemented virtual reality setup. We present a CNN-BiLSTM-based model for expertise assessment in goalkeeper-specific decision tasks on initiating passes in build-up situations. The empirical validation demonstrated that our model is able to find valuable latent features to detect the expertise level of 33 athletes (novice, advanced, expert) with 73.11% accuracy.