A survey of competitive sports data visualization and visual analysis

• Published in: Journal of visualization Vol. 24; no. 1; pp. 47 - 67
• Main Authors: Du, Meng, Yuan, Xiaoru
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021; Springer Nature B.V
• Subjects: Classical and Continuum Physics; Computer Imaging; Data analysis; Engineering; Engineering Fluid Dynamics; Engineering Thermodynamics; Heat and Mass Transfer; Multimedia; Pattern Recognition and Graphics; Regular Paper; Scientific visualization; Sports; Taxonomy; Vision; Visualization; Competitive sports; Data visualization; Visual analysis
• ISSN: 1343-8875; 1875-8975
• DOI: 10.1007/s12650-020-00687-2

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, time-series 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. Graphic abstract