Statistical modelling of gaze behaviour as categorical time series: what you should watch to save soccer penalties

Abstract

Previous research on gaze behaviour in sport has typically reported summary fixation statistics thereby largely ignoring the temporal sequencing of gaze. In the present study on penalty kicking in soccer, our aim was to apply a Markov chain modelling method to eye movement data obtained from goalkeepers. Building on the discrete analysis of gaze employed by Dicks et al. (Atten Percept Psychophys 72(3):706-720, 2010b), we wanted to statistically model the relative probabilities of the goalkeeper's gaze being directed to different locations throughout the penalty taker's approach (Dicks et al. in Atten Percept Psychophys 72(3):706-720, 2010b). Examination of gaze behaviours under in situ and video-simulation task constraints reveals differences in information pickup for perception and action (Attention, Perception and Psychophysics 72(3), 706-720). The probabilities of fixating anatomical locations of the penalty taker were high under simulated movement response conditions. In contrast, when actually required to intercept kicks, the goalkeepers initially favoured watching the penalty taker's head but then rapidly shifted focus directly to the ball for approximately the final second prior to foot-ball contact. The increased spatio-temporal demands of in situ interceptive actions over laboratory-based simulated actions lead to different visual search strategies being used. When eye movement data are modelled as time series, it is possible to discern subtle but important behavioural characteristics that are less apparent with discrete summary statistics alone.

Perceptual-cognitive skill and the in situ performance of soccer players

“…To gain more insight into the visual search strategies of the participants, we analysed to what degree the gaze behaviour was structured or randomly distributed by calculating gaze entropy ( Allsop & Gray, 2014 ; Button, Dicks, Haines, Barker, & Davids, 2011 ; Ryu, Mann, Abernethy, & Poolton, 2016 ) for each test for each participant. To do this, we first calculated the number of fixation transitions between the 10 areas of interest by producing a first-order transition frequency matrix of p ( i to j ), in which i represents the area of interest before the transition, and j represents the area of interest after the transition.…”

Decision-making and dynamics of eye movements in volleyball experts

“…The most important critique to put forward regards the lack of sophistication with respect to attentional pattern analysis (i.e., the order in which AOIs are fixated). Button, Dicks, Haines, Barker and Davids 22 observed that attentional pattern in sports (soccer in that case) can be studied using time-series analysis, presenting a continuous and fluid evolution of attentional pattern. This novel approach informs not only on attentional cues useful in anticipation and decision-making, but also in which order those cues should be attended to.…”

The effect of fixation transitions on quiet eye duration and performance in the soccer penalty kick: instep versus inside kicks

“…Not only did the goalkeepers make more saves in the ISI condition, but they changed the location of fixations across the five conditions, with more fixations to the head/ shoulders and legs in the video simulations and significantly earlier and longer fixations durations on the ball during the ISI condition. Button et al (2010) reanalysed the data from the Dick's et al (2010a) using Markov chain modeling with the goal of determining the relative probabilities of the goalkeeper's location of gaze. Results showed that during the simulated conditions, the highest probability of fixations were toward locations on the penalty taker's body, but during the ISI condition the goalkeepers gaze shifted to the ball prior to foot-ball contact.…”