Visualizing the overall structure of a tennis match
Still browsing documents in the Information Vizualization world, I ran across this paper: Liqun Jin; Banks, D.G. (1997): TennisViewer: a browser for competition trees, IEEE Computer Graphics and Applications, 17 (4), 63 - 65 It's about a tool called "TennisViewer" that aim at enabling coaches, players,and fans a new way to analyze, review, and browse a tennis match; a very interesting cultural practice IMO.
A tennis novice watching a match for the first time might be surprised that the crowd erupts with cheers when a player wins one point, then barely applauds when he wins the next. The crowd is not necessarily fickle; some points are genuinely more important than others because a tennis match is hierarchically structured. One match consists of several sets. One set consists of several games. One game consists of several points. The match-winning point is the most important one. How can we make that importance visible? Our goal is to let a fan, a player, or a coach examine tennis data visually, extract the interesting parts, and jump from one item to another quickly and easily. The visualization tool should help parse the elements of a match. We developed an interactive system called TennisViewer to visualize the dynamic, tree-structured data representing a tennis match. It provides an interface for users to quickly explore tennis match information. The visualization tool reveals the overall structure of the match as well as the fine details in a single screen. It uses a 2D display of translucent layers, a design that contains elements of Tree-Maps and of the Visual Scheduler system, which was designed to help faculty and students identify mutually available (transparent) time slots when arranging group meetings. TennisViewer provides MagicLens filters to explore specialized views of the information and a time-varying display to animate all or part of a match (...) TennisViewer displays a computer-generated tennis match. (a) A serve (top) is returned out of bounds (bottom). (b) One Magic Lens filter lies on top of another, revealing ball traces within a point:
Why do I blog this? This paper is related to my current research project. I like this idea of showing the "overall structure" of the match, that's basically my aim with the Catchbob data; the point would be to show some underlying phenomenons (exchange of coordination information) and this make them visible.