A survey of competitive sports data visualization and visual analysis
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R E G UL A R P A P E R
Meng Du
•
Xiaoru Yuan
A survey of competitive sports data visualization and visual analysis
Received: 22 July 2018 / Revised: 24 March 2020 / Accepted: 6 May 2020 The Visualization Society of Japan 2020
Abstract Competitive sports data visualization is an increasingly important research direction in the field of information visualization. It is also an important basis for studying human behavioral pattern and activity habits. In this paper, we provide a taxonomy of sports data visualization and summarize the state-of-the-art research from four aspects of data types, main tasks and visualization techniques and visual analysis. Specifically, we first put sports data into two categories: spatiotemporal information and statistical information. Then, we propose three main tasks for competitive sports data visualization: feature presentation, feature comparison and feature prediction. Furthermore, we classify competitive sports data visualization techniques based on data characteristics into five categories: high-dimensional data visualization, timeseries visualization, graph (network) visualization, glyph visualization and other visualization, and we analyze the relationship between major tasks and visualization techniques. We also introduce visual analysis research work of competitive sports, propose the features and limitations of competitive sports data, summarize multimedia visualization in competitive sports and finally discuss visual analysis evaluation. In this survey, we attempt to help readers to find appropriate techniques for different data types and different tasks. Our paper also intends to provide guidelines and references for future researchers when they study human behavior and moving patterns. Keywords Competitive sports Data visualization Visual analysis
M. Du: The presented work was done while Meng Du was a postdoctoral researcher at Peking University. Present Address: M. Du School of New Media, Beijing Institution of Graphic Communication, Beijing, China E-mail: [email protected] M. Du International New Media Institution of Industry-Education-Research Cooperation, Beijing, China M. Du X. Yuan Key Laboratory of Machine Perception (Ministry of Education), School of EECS, and Center for Computational Science and Engineering, Peking University, Beijing, China X. Yuan (&) National Engineering Laboratory for Big Data Analysis and Application and Beijing Engineering Technology Research Center of Virtual Simulation and Visualization, Peking University, Beijing, China E-mail: [email protected]
M. Du, X. Yuan
1 Introduction The main goal of competitive sports is to produce superior sporting performance, ultimately assisting the winning of competitions. At the very core of competitive sports data are the athlete and their behavior. In sports, not only do athletes themselves have physical self-behavioral activity, behavioral activities between athletes also exist in which spatiotemporal, described and counted behavior data can be logged. Therefore, the rise of compe
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