Match Radar

The graphic above is a “Match Radar” chart of the 2007 Wimbledon Final between Rodger Federer and RafaelNadal [TAVA link].

The Match Radar is intended to provide a compact visual comparison of the key statistics for players of a tennis match. This is in contrast to the Points-to-Set, Horizon and Radial Horizon charts which aim to depict the dynamics of a match with respect to the scoring of each set.

The Match Radar enables a quick assessment of whether and how one player dominated another, whether a match was lob-sided, and where players differed on key statistics.  It is not intended as a tool for in-depth analysis.

In the match shown above you can see that Federer (blue) and Nadal (purple) were very close in terms of 1^st and 2^nd serve statistics, with Federer having only a very slightly higher 1^st serve percentage and percentage of 2^nd serve points won. Similarly, both players were very close on percentage of return points won for both 1^st and 2^nd serves, with Federer again having only slightly better numbers. Where Federer really stood out was in Aces, Serve Winners, Percentage of Returns-in-play and Forcing Errors. Nadal had more outright winners and more breakpoints, but he failed to convert on enough of the breakpoints to win the match; there was only a difference of seven points at the end of the match.

In the current version of TAVA, the Match Radar appears as both a dashboard icon, with no legend, and a full-size chart; in both cases the graphic is interactive. Values appear in a “tooltip” when the mouse hovers over any point on the chart.

Here is a Match Radar for the 2013 US Open Final between Victoria Azarenka (blue) and Serena Williams (purple); Williams won 5-7, 7-6, 6-1. [TAVA link].

I made a number of changes to the Radar Chart examples found in the various D3Galleries (here and here). You will find a recent D3 Example here. The most notable addition I made to the Radar Chart is the adoption of support for diverse types of axes. This addition was inspired by “Parallel Coordinates” charts, which you can read about here and here.  You can find examples of Parallel Coordinates charts in the current version of TAVA.  I haven't spent a great deal of time trying to optimize their use, but they do seem to be unwieldy; at a size where the labels could be read and the various matches being charted could be discerned I found it necessary to make the graphic horizontally scrollable.

In the Match Radar chart, the majority of the statistics are given as percentages, but there are some statistics (aces, serve winners, winners, forcing errors, and breakpoints) which are given as “extents” where the axes ranges from zero to the maximum value achieved by either player.

I have also modified the standard Radar Chart to support inverted axes, where the high value appears at the center of the Radar with the low value on the outer edge. This can be used to depict Unforced Errors or Double Faults, where the low value is deemed “better” and should enlarge the player's color area of the radar, rather than pull it toward the center. Additionally, the Match Radar supports “bounded extents” where the extent values can be set arbitrarily. This is appropriate when displaying Aggressive Margins, for instance, when values can range either side of zero.

In a future version of TAVA I plan to make the Match Radar “dynamic” such that it can support the real-time display of a selection of points (“brushing” a range of points on the Horizon Chart, for instance); this capability would also make it possible to “Play” the match from the beginning and watch the changes in the shapes of each player's radar as the match progresses.

I also plan to enable users to configure their own views, selecting which statistics are most relevant for their purposes, and in which order they should appear. I haven't yet decided which statistics should appear as the default, and which order makes the most sense.

This is a selection of matches played by Novak Djokovic (blue). Once you are familiar with the layout of the axes on the Match Radar, you can begin to compare matches and to look for patterns. You might want to look for matches that appear very unbalanced, or very close, for instance.

The match below is the 2013 US Open Semifinal between Novak Djokovic and Stan Wawrinka. [TAVA link]. You can see iconic representation of this match in the bottom row above (2^nd from right).

The right side of the radar is dedicated to service statistics, while the left ranges from Returns-in-Play and Return Points Won (at the bottom) to Winners, Forcing Errors and Breakpoints (at the top). Djokovic won this match 2-6, 7-6, 3-6, 6-3, 6-4, so it was indeed close.

The Match Radar can also be used to quickly look for changes in key statistics across sets:

This may give an idea of how the “brushing” will work: dragging across a range of points in the horizon chart (below the Points-to-Set chart), would dynamically update the Match Radar to reflect the statistics for the selected range of points. A number of coaches have asked to be able to identify those moments in a match when a specific statistic changed dramatically... I'm not sure how that will surface, as yet, but these “discovery” tools may aid in the development of new ideas.

Going forward I also want to use the selection of statistics chosen for the Match Radar to drive views of a player's statistics across a series of matches, within a tournament, or within an arbitrary date range.  These views would not employ the Match Radar.

Radar Charts have often been critiqued, along with other polar coordinate charts (see here and here). I understand these critiques and, for the most part, agree with them; nevertheless, I find the iconic version of the Radar Chart gives a good "at-a-glance" gut feel for how players differed on key statistics, particularly after spending time looking at a large number of matches and becoming familiar with how to interpret the layout. The Match Radar is far more compact than the alternatives when quickly comparing matches or sets and can serve as a control structure to drive other, more in-depth, analysis tools and visualizations.

I think a significant difference in the application of the Match Radar Chart in the TAVA application is that it is a standard visualization that can be used across potentially thousands of matches (I now have approximately 3000 matches in the Tennis AiP database), as opposed to a one-off article by a journalist or researcher where a novel analysis is being presented.

I'm on the lookout for compact alternatives to the Match Radar Chart, but I believe it will persist in future versions of TAVA.  It is likely the Match Radar will become more of a control structure that drives a variety of complementary "drill-down" visualizations.