A Winter Games Viz Tetralogy
Mar 18, 2018Klaus Schulte
In the past weeks, I had quite some fun to create visualizations for the Olympic and the Paralympic Winter Games. Beside a visualization of the most successful Olympic athletes of all times I created three visualizations on luge, on biathlon and on alpine skiing.
The feedback from the Tableau-Community was overwhelming: 2 x Viz of the Day on Tableau Public, favourited at #MakeoverMonday, a Bronze Medal at the #SportVizSunday Olympic Challenge as well as numerous features on Twitter are more appreciation than I have expected and might expect.
Viz of the Day: Past Winter Games winners, by @ProfDrKSchulte https://t.co/8TuKQXsHjF #VOTD
— Tableau Public (@tableaupublic) 6. Februar 2018
#MakeoverMonday Week 7 Recap: The Winter Olympics | Thanks @RodyZakovich for letting us use your viz and thanks @CharlieHTableau for stepping in for #MMVizReview https://t.co/eW5cBvzH04 pic.twitter.com/dOeZLFpTIt
— Andy Kriebel (@VizWizBI) 16. Februar 2018
Selecting a #SportsVizSunday was such fun this week thanks to a week of amazing #MakeoverMonday #WinterGamesViz!
Here is my #SportsVizSunday podium:
— Simon Beaumont (@SimonBeaumont04) 18. Februar 2018
Viz of the Day: Skiing at the winter games, by @ProfDrKSchulte https://t.co/tVDajmteUT #VOTD #WinterGamesViz pic.twitter.com/2HFhTzijgK
— Tableau Public (@tableaupublic) 13. März 2018
In this blogpost I would like to explain briefly the design process of each of the four visualizations as well as some technical challenges I was faced with.
Click to get to the interactive version on Tableau Public
From a technical perspective, I would still call myself a Tableau beginner. For example, I regularly fail to recreate the #WorkoutWednesday-visualizations. What works fine for me is to explore new chart types based on blogs or visualizations, which I have seen mostly during the #MakeoverMonday challenges. Therefore The Chartmaker Directory of Andy Kirk is very helpful for me. I try to apply the different chart types bit by bit, always finding blogposts and example workbooks linked in the Chartmaker Directory.
This visualization was a contribution to the #SportVizSunday Olympic Challenge by Simon Beaumont, Spencer Baucke and James Smith. I wanted to create a Sankey chart and followed a blogpost by Chris Love. However, I don’t want to talk about the steps to create a sankey. Chris describes these in his blogpost perfectly.
When I designed the viz I tried to copy a glow effect which I have seen first in some of Jonni Walker’s visualizations. While Jonni generates the effect for example in this visualization by using shapes, I brought in my “Curve” twice into the visualization and created a double axis. Then I changed settings of size and colour opacity to create this effect, which is most effective with a black background.
Concerning the font, I chose “Bauhaus 93”, which in my opinion contributes well to the atmospheric and dignified mood of the visualization.
In my second project, I wanted to visualize the success of German athletes at the Olympic Winter Games. I had seen visualizations on the success of the Dutch speed skaters (e.g., this of Aline Leonard or this of Kate Brown). To find my own angle, I first informed myself on Wikipedia about the different Olympic disciplines. Eventually my eyes fell on luge where always three competitions had taken place.
Since I know about the great success of the German athletes in luge a first sketch for the visualization was created very quickly:
In Tableau I only had to create a heatmap and a calculated field, colouring the medals of the German athletes accordingly:
Furthermore, I calculated the share of German medal winners by event and showed these figures in three BANs.
I am a big fan of Neil Richards’ design-driven-data-approach. Against this background, I have already had the idea to create a visualization on biathlon for some time, because the biathlon targets have a high recognition effect. However, the idea which data I could visualize as the targets has only come to me when I have analysed the data on medal winners more closely. I realized that there are five women’s competitions and five men’s competitions. Therefore, every target should represent the medal winners of a competition and a filter should provide the possibility to switch between women’s and men’s competitions. Besides, the medal winners should be arranged by the respective nations.
To execute my plan in Tableau, I did some research and found Adam E. McCann’s American Whiskey Wheel, which I then reverse engineered.
I was quite satisfied with my first draft. Then, however, I got some great feedback during #mmvizreview from Eva Murray and Charlie Hutcheson as well as from Steve Fenn on Twitter, which have clearly improved the visualization. This showed to me once again how important feedback and exchange are.
By the way, my personal favourite of my four Winter Games Vizzes is this biathlon viz.
My fourth viz also follows the design-driven-data-approach. I had a closer look at a visualization by Staticum, which he created as part of #makeovermonday 10/2018, and learnt how to visualize data as curves in a jumpplot.
Moreover, I remembered a visualization by Neil Richards and the idea for my Paralympics-viz was born.
I considered to visualize the total number of medals of each country at the alpine skiing events of the respective Paralympics as curved “medal-lanes” to generate the impression of a ski descent. Moreover, I wanted to colour the Medal-Lanes according to the colours of the flags of the respective country. Not to overload the visualization, I concentrated upon the TOP 5-nations. It was a pleasant side effect that I did not have to talk about the fraud of the Russian team in 2014.
Like the glow-effect in my first visualization, I generated the flags by bringing in my curve three times and again setting size and colour accordingly to generate the flag look.
I also used the „viz-in-tooltips” feature for the first time.
Once again many thanks to everybody who have inspired me during my Winter-Games-Viz-Tetralogy, as well as have supported me actively and passively.
In Order of Appearance:
Tableau Public, Andy Kriebel, Simon Beaumont, Spencer Baucke, James Smith, Chris Love, Andy Kirk, Jonni Walker, Aline Leonard, Kate Brown, Neil Richards, Adam E. McCann, Eva Murray, Charlie Hutcheson, Steve Fenn, Staticum and of course Tableau Software
I’m really looking forward to any kind of feedback!