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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., Comment: 5 pages, 1 figure. For associated package, see https://pypi.org/project/floodlight/. For source code, see https://github.com/floodlight-sports/floodlight . For documentation, see https://floodlight.readthedocs.io

Computer-aided support and analysis are becoming increasingly important in the modern world of sports. The scouting of potential prospective players, performance as well as match analysis, and the monitoring of training programs rely more and more on data-driven technologies to ensure success. Therefore, many approaches require large amounts of data, which are, however, not easy to obtain in general. In this paper, we propose a pipeline for the fully-automated extraction of positional data from broadcast video recordings of soccer matches. In contrast to previous work, the system integrates all necessary sub-tasks like sports field registration, player detection, or team assignment that are crucial for player position estimation. The quality of the modules and the entire system is interdependent. A comprehensive experimental evaluation is presented for the individual modules as well as the entire pipeline to identify the influence of errors to subsequent modules and the overall result. In this context, we propose novel evaluation metrics to compare the output with ground-truth positional data., Comment: Accepted for publication at WACV'22; Preprint