Abstract
An important part of ensuring continuous development and betterment of performance of a robot soccer team is the process of analyzing past matches and generating insights about possible causes for good or bad outcomes the team has in the field. RoboCup robot soccer matches from leagues such as the 2D Simulation League generate log files that can help create insights using data analysis, but since there is currently no active open collaboration between teams for building an ecosystem of analysis tools, this area is underdeveloped - as a community - and could be improved. We propose an open-source data analysis library that contains all of the basic structures needed for implementing any analysis, as well as a collection of ready-to-use, quasi-agnostic analysis that can be used to analyze matches from any soccer league with little or none adaptation. We believe this can be a common ground for developers from any team to work together in the advancement of technology and lower the barrier of entry into the data analysis realm for teams that are not yet involved in the area. We also demonstrate how this library can be leveraged as a software component for other projects, by building a custom web platform that utilizes it.
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Acknowledgements
The authors would like to acknowledge the RoboCIn’s team and Centro de Informática - UFPE for all the research support. The first author was also funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). They also appreciate all the Simulation 2D and SSL teams effort for their open-source contributions.
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Pereira, F.N.A. et al. (2023). A Library and Web Platform for RoboCup Soccer Matches Data Analysis. In: Eguchi, A., Lau, N., Paetzel-Prüsmann, M., Wanichanon, T. (eds) RoboCup 2022: Robot World Cup XXV. RoboCup 2022. Lecture Notes in Computer Science(), vol 13561. Springer, Cham. https://doi.org/10.1007/978-3-031-28469-4_15
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DOI: https://doi.org/10.1007/978-3-031-28469-4_15
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