Testing decision-making skills in football using augmented reality
Decision-making skills are fundamental to successful performance in team sports. Current approaches to assess decision-making skills are incredibly resource intensive for both athletes and researchers. Athletes are required to travel to testing facilities and researchers invest time and effort to create representative experiments. A less resource intensive procedure is highly desired.
In this study, the feasibility of an on-field decision-making task was assessed. The task was presented in augmented reality. The situations in the task were retrieved from open-source tracking data. Short sequences of build-up play were arranged into a testing battery based on a similarity score. For the similarity score, we harnessed the Kuhn-Munkres algorithm (Kuhn, 1955; Munkres, 1957) to select similar situations based on player positions and movement directions. The sampled situations preserve the natural variability experienced by football players in the performance environment. With the algorithmic approach, the need for selecting and creating situations manually was minimized.
The display, which rendered the view of a regular 11v11 football situation was shown from the perspective of a midfield player. The content was displayed using a head mounted augmented reality device. The participant was free to move on a real football field onto which the other players were augmented as virtual avatars. The participant was presented with a freeze-frame of the situation and triggered the situation by moving into a visually designated area. Participants were instructed verbally to offer support to the ball-carrying team-mate. The situation ended with the participant receiving a virtual pass from the team-mate.
Experienced football players were drawn from a pool of university students to participate in the study. During the experiment, the movement trajectory of the participant was recorded using IMU data retrieved from the head-mounted display. The movement patterns were analyzed using a timeseries-clustering approach to examine the characteristics of different movement solutions the participants deployed across the situations (Chow et al., 2008). Deviations from the original trajectories (i.e., the open-source data) were measured to gaze at the natural variability that occurs in build-up situations in football.
In conclusion, the feasibility study attempted to facilitate the assessment of decision-making skills in team-sports. Two promising means were introduced: (1) The selection of the situations was automated to reduce efforts needed by the researcher. (2) The use of an augmented-reality device enabled testing directly on the field. The decision-making skills were assessed through the observed movement patterns which provided new insights on how players move when making decisions.
Chow, J. Y., Davids, K., Button, C., & Rein, R. (2008). Dynamics of movement patterning in learning a discrete multiarticular action. Motor Control, 12(3), 219-240. https://doi.org/10.1123/mcj.12.3.219
Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1‐2), 83-97. https://doi.org/10.1002/nav.3800020109
Munkres, J. (1957). Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics, 5(1), 32-38.
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