Sports data…Eva’s favorite! I like giving her challenges, kind of my way to drive her nuts and question why she does this project with me. I see it as a growth opportunity.

This week’s data set of rankings of the toughest sport by a “panel of experts” in America drew some critique, exactly what I had hoped for. Since the rankings are so subjective, people questioned the validity; this is a great sign that people are thinking about the source of the data, which affects their analysis. In addition, there was some funny discussion about whether some of these should be classified as “sports”.


This week’s data contained only one metric: difficulty rating from 1-10. The Makeover Monday community being what it is, people found inventive ways to spin this metrics into something meaningful for their analysis. However, when you do create a new metric or way to slice the data, keep in mind that your audience needs to be able to understand the metric without a lot of mental gymnastics.

During Viz Review, Eva and I reviewed this visualization from Rosario Gauna.

Rosario makes incredible contributions week after week and has one of the most analytical minds I have ever seen. In her viz, she added quite a bit of detail to the header for each skill: Skill, Score, Rank, and Decile. When we reviewed this on the webinar, we didn’t quite understand the purpose or value of the decile. Yes, we understand what a decile is, but we thought this overcomplicated the visualization and it distracted us from understanding the rest of the viz.

In addition, we didn’t get the blocks under each skill. Each skill is ranked on a scale of 1-10, so why not show it as a scale?

The problem with adding “complicated” metrics or views into a viz is that the reader essentially stops and tries to understand the metric before moving on. This breaks the flow of the analysis and likely causes the audience to forget what they were looking at in the first place. We gave this feedback during the webinar and Rosario took it to her, considered our feedback, and iterated on her viz.

Compare this to the original. Everything is now clear and easy to understand. These changes didn’t take a lot of time, but they made a big difference to comprehension.



In Stephen Few’s article Data Visualization: Rules for Encoding Values in Graph, he notes that:

One of the most important guidelines to keep in mind about lines is that they should only be used to connect values that are themselves intimately connected to one another. Changes in the amount of sales from one month, quarter, or year to the next are intimately connected to one another. On the other hand, a set of values that measure the expenses of different departments are not intimately connected; they are discrete and should be displayed in a manner that visually suggests their discreteness. Connecting discrete values with a line does not properly depict the relationship between those values.

As an example from Viz Review, consider this visualization by Samantha McKinlay:

Samantha has used a line chart to connect discrete values, implying that there is a connectedness to their order. However, there is not; the line has no meaning. A dot plot would be a much more effective display. Samantha iterated on the feedback and created this viz:

The dot plot forces the audience to compare the dots within each skill, rather than implying that there is some natural pattern across the skills. Here’s a great dot plot by Liyang Wang. She makes it very easy for her audience to understand how the selected sport ranks amongst all others within each skill.

Remember, line are used to connect values that have a natural sequence. If your data does not have such a sequence, consider another visualization.