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International Conference on Theory and Application of Diagrams

Diagrams 2014: Diagrammatic Representation and Inference pp 38-44 | Cite as

Tennis Plots: Game, Set, and Match

  • Michael Burch
  • Daniel Weiskopf
Open Access
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8578)

Abstract

In this paper we introduce Tennis Plots as a novel diagram type to better understand the differently long time periods in tennis matches on different match structure granularities. We visually encode the dynamic tennis match by using a hierarchical concept similar to layered icicle representations used for visualizing information hierarchies. The time axis is represented vertically as multiple aligned scales to indicate the durations of games and points and to support comparison tasks. Color coding is used to indicate additional attributes attached to the data. The usefulness of Tennis Plots is illustrated in a case study investigating the tennis match of the women’s Wimbledon final 1988 between Steffi Graf and Martina Navratilova lasting 1 hour, 19 minutes, and 31 seconds and being played over three sets (5:7, 6:2, 6:1). Interaction techniques are described in the case study in order to explore the data for insights.

Keywords

time-varying data sports data hierarchical data 

References

  1. 1.
    Tversky, B., Bauer Morrison, J., Bétrancourt, M.: Animation: Can it Facilitate? International Journal of Human-Computer Studies 57(4), 247–262 (2002)CrossRefGoogle Scholar
  2. 2.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer (2011)Google Scholar
  3. 3.
    van Wijk, J.J., van Selow, E.R.: Cluster and Calendar Based Visualization of Time Series Data. In: Proceedings of Infovis, pp. 4–9 (1999)Google Scholar
  4. 4.
    Klaassen, F.J.G.M., Magnus, J.R.: Forecasting the Winner of a Tennis Match. European Journal of Operational Research 148(2), 257–267 (2003)CrossRefGoogle Scholar
  5. 5.
    Terroba Acha, A., Kosters, W.A., Varona, J., Manresa-Yee, C.: Finding Optimal Strategies in Tennis from Video Sequences. IJPRAI 27(6) (2013)CrossRefGoogle Scholar
  6. 6.
    Burch, M., Weiskopf, D.: Visualizing Dynamic Quantitative Data in Hierarchies – TimeEdgeTrees: Attaching Dynamic Weights to Tree Edges. In: Proceedings of International Conference on Information Visualization Theory and Applications, pp. 177–186 (2011)Google Scholar
  7. 7.
    Jin, L., Banks, D.C.: TennisViewer: A Browser for Competition Trees. IEEE Computer Graphics and Applications 17(4), 63–65 (1997)CrossRefGoogle Scholar
  8. 8.
    Johnson, B., Shneiderman, B.: Tree Maps: A Space-Filling Approach to the Visualization of Hierarchical Information Structures. In: Proceedings of IEEE Visualization, pp. 284–291 (1991)Google Scholar
  9. 9.
    Shneiderman, B.: Tree Visualization with Tree-Maps: 2-D Space-Filling Approach. ACM Transactions on Graphics 11(1), 92–99 (1992)CrossRefGoogle Scholar
  10. 10.
    Kruskal, J., Landwehr, J.: Icicle Plots: Better Displays for Hierarchical Clustering. The American Statistician 37(2), 162–168 (1983)Google Scholar
  11. 11.
    Tu, Y., Shen, H.-W.: Visualizing Changes of Hierarchical Data Using Treemaps. IEEE Transactions on Visualization and Computer Graphics 13(6), 1286–1293 (2007)CrossRefGoogle Scholar
  12. 12.
    Cleveland, W.S., McGill, R.: Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association 79(387), 531–554 (1984)CrossRefGoogle Scholar
  13. 13.
    Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, pp. 336–343 (1996)Google Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Michael Burch
    • 1
  • Daniel Weiskopf
    • 1
  1. 1.Visualization Research CenterUniversity of StuttgartStuttgartGermany

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