Papers › floodlight -- A high-level, data-driven sports analytics framework
floodlight -- A high-level, data-driven sports analytics framework
Dominik Raabe, Henrik Biermann, Manuel Bassek, Martin Wohlan, Rumena Komitova, Robert Rein, Tobias Kuppens Groot, Daniel Memmert
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The present work introduces floodlight, an open source Python package built to support and automate team sport data analysis. It is specifically designed for the scientific analysis of spatiotemporal tracking data, event data, and game codes in disciplines such as match and performance analysis, exercise physiology, training science, and collective movement behavior analysis. It is completely provider- and sports-independent and includes a high-level interface suitable for programming beginners. The package includes routines for most aspects of the data analysis process, including dedicated data classes, file parsing functionality, public dataset APIs, pre-processing routines, common data models and several standard analysis algorithms previously used in the literature, as well as basic visualization functionality. The package is intended to make team sport data analysis more accessible to sport scientists, foster collaborations between sport and computer scientists, and strengthen the community's culture of open science and inclusion of previous works in future works.
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