Abstract
This study introduces DataGoal, an open-source MATLAB-based toolbox designed to process, analyze, and visualize positional data in soccer obtained from Global Positioning System (GPS/GNSS), Local Positioning System (LPS), and video-tracking technologies. The increasing availability of tracking data has created new opportunities for investigating the spatial–temporal dynamics of team sports; however, the complexity of these datasets often limits their effective use. DataGoal addresses this challenge by providing an integrated analytical framework that combines data importation, spatial calibration, preprocessing, metric computation, and graphical visualization within a single workflow. The toolbox implements a comprehensive set of linear and non-linear metrics to quantify individual and collective behaviors, including physical demands, team compactness, spatial organization, and dynamic coordination patterns. Through an interactive graphical user interface, DataGoal enables users to configure analyses and process positional datasets without extensive programming requirements, while its modular MATLAB-based architecture allows users to extend or customize analytical routines. By integrating multiple analytical procedures within a unified environment, DataGoal facilitates reproducible positional-data analyses and supports investigations of collective dynamics in soccer. The framework is suitable for applications in scientific research, performance analysis, and sports science education. As an open-source project, DataGoal is intended to evolve through community contributions and future extensions, contributing to the advancement of data-driven approaches in soccer performance analysis.
Get full access to this article
View all access options for this article.