Facebook’s data visualization contest Viz Cup gives me mixed emotions — excitement about the growing interest in smart data visualization, and sadness about the poor data viz practices among the entrants.
Case in point is last year’s winner, Eric Rynerson, with this entry. Now, to be fair, Eric only had an hour to put this data viz together (he studied the data the night before) and there are a lot of good practices here. But as an educational opportunity for you, dear reader, I want to point out the weaknesses in this data viz and suggest a makeover.
Here’s Eric’s winning entry:
1. Bubble Chart
Let’s start with a critique of the bubble chart in the upper left. What I like about it:
- a. The graph title summarizes the main point, requiring less work for the reader
- b. It’s a good choice for comparing two groups. You can tell from the slope if there is a bias toward penalizing the Away team
- c. The graph is relatively subdued with light gridlines whispering in the background and muted colors for the bubbles
But here’s the things that don’t work
- a. First, the graph title is too small. It’s the same size font as the axes numbers. It should be more prominent
- b. The bubbles are different colors but I don’t know why. Is this so you can distinguish the tiny bubbles clustered in the lower left? Or is it to add variety to the graph? Generally, the reader will assume the colors have meaning. In this case, I’d avoid the randomness of the many colors and use red for the refs that hand out more red cards to Away teams, and gray for refs with no apparent bias
- c. And I’m not going to just use any red. I’m going to use the exact red on a penalty card because those who are familiar with that red have learned a subconscious reaction to that particular shade of red. And I want to generate that reaction
- d. The bubbles are also different sizes. Again, it isn’t clear if this is supposed to mean something or not. If it does, indicate that somewhere on the graph. If not, then keep them all the same size
- e. Eric has added a regression line to the chart. But a better option would be to add a 45-degree line, showing the expected position of refs who penalize Home and Away teams evenly
- f. You should also add text labels to each half of the chart, so readers clearly understand what it means to be above or below that line
- g. The annotation explains there is a similar but weaker bias for yellow cards. But the extensive text clutters the graph unnecessarily. Better to remove that annotation, or minimize the amount of text
So my makeover of the bubble chart would look like this
2. Column Charts
The column charts in the upper right are intended to appear as you click on each bubble. I like a couple of things about these charts
- a. First, I like that we’re seeing some trend data tracking how a referee’s bias may, or may not, change over the years. The basic format of a story is a beginning, middle and end and so a timeline graph gets us closer to a story
- b. Eric shows the absolute number of cards for Home and Away teams, but then he also explicitly calculates those differences and plots them on another chart
- c. The mirrored bar chart is generally a good choice for showing how two data series compare
What I don’t like
- a. The text block to the left of the graphs is supposed to invite the reader to click. But it’s too much text to draw attention. Better would be to use an image with a prominent call to click
- b. Almost always, time series data should go left to right. The reader intuitively understands this. A top to bottom timeline is less intuitive
- c. When comparing two data series, and one is clearly larger than the other, I prefer to use an overlapping column chart
- d. The legend at the bottom should be integrated into the graph, where it’s easier to read, and not set outside the graph
- e. The two charts should use a similar scale. Right now, the differences look enormous compared to the absolute values, just because that scale is stretched out
- f. I’m also not a fan of the monochromatic purple for the mirrored bar chart. The purple doesn’t complement the red and seems to be chosen at random. Better to select from our current red color palette and complement it with a strong but otherwise neutral black.
We end up with a graph like this:
Finally, we study the overall layout, including the title, pictures and flow through the piece. I like
- a. The attempt to add a (clip art) picture next to the title, increasing visual interest and suggesting quickly the topic of the data visualization
- b. The title summarizes the conclusions of the graphs, not leaving that work to the reader
- c. The use of faces is especially effective at drawing the reader into the data visualization and adding some human drama and emotion to what would otherwise be emotionally inert data
What I’d improve
- a. This clip art is weak, has no emotion and generally trivializes the importance of the piece. I’d choose an image with more emotion, like a referee forcefully holding up a red card
- b. The title is a bit vague. I’d write it more directly
- c. The face images below are good, but they are a bit remote from the column charts. In this case, the data in the column charts applies to only one referee — the one in the pictures below. We need to link this more clearly perhaps using a color band
Adding all these elements together gets us a final data visualization that look like this, and tells a better story too, don’t you think? (Hover over the image to compare with the original).
What do you think? Is the story clearer? What other changes would you make? Leave a comment in the comments section. And if you’re interested in knowing when my new book “Storytelling with Graphs” is available, just sign up for my LinkedIn group or subscribe to this blog.
About the author: Bruce Gabrielle is author of Speaking PowerPoint: the New Language of Business, showing a 12-step method for creating clearer and more persuasive PowerPoint slides for boardroom presentations. Subscribe to this blog or join my LinkedIn group to get new posts sent to your inbox.