iTTVis: Interactive Visualization of Table Tennis Data

The interactive user interface of the iTTVis system with a unified color encoding (A-1). The interface comprises five well-coordinated views: the match view (including a tailored step chart (B-1), a point outcome bar (B-2), and a selection panel (B-3)), the stroke view (collapsed and minimized in (C)), the stat view (D), the tactic view (E), and the history view (F).

The rapid development of information technology paved the way for the recording of fine-grained data, such as stroke techniques and stroke placements, during a table tennis match. This data recording creates opportunities to analyze and evaluate matches from new perspectives. Nevertheless, the increasingly complex data poses a significant challenge to make sense of and gain insights into. Analysts usually employ tedious and cumbersome methods which are limited to watching videos and reading statistical tables. However, existing sports visualization methods cannot be applied to visualizing table tennis competitions due to different competition rules and particular data attributes. In this work, we collaborate with data analysts to understand and characterize the sophisticated domain problem of analysis of table tennis data. We propose iTTVis, a novel interactive table tennis visualization system, which to our knowledge, is the first visual analysis system for analyzing and exploring table tennis data. iTTVis provides a holistic visualization of an entire match from three main perspectives, namely, time-oriented, statistical, and tactical analyses. The proposed system with several well-coordinated views not only supports correlation identification through statistics and pattern detection of tactics with a score timeline but also allows cross analysis to gain insights. Data analysts have obtained several new insights by using iTTVis. The effectiveness and usability of the proposed system are demonstrated with four case studies.

[VIS17 Preview] iTTVis: Interactive Visualization of Table Tennis Data from VGTCommunity on Vimeo.

The authors would like to thank Hui Chen from the College of Computer Sciene and Technology, Zhejiang University, and Zheng Zhou from the Department of Physical Education, College of Education, Zhejiang University for providing valuable feedback and suggestions for this project. The work was supported by National 973 Program of China (2015CB352503), NSFC (U1609217, 61502416), the Fundamental Re- search Funds for Central Universities (2017XZA217, 2016QNA5014), and 100 Talents Program of Zhejiang University.