I sketched out this blog post right before the Superbowl - and was really worked up as I happened to be flying into Atlanta right after they won (well, according to any of our favorite "prediction engines," the Falcons had 95%+ chance of winning it all a minute from the end of the 4th quarter!) What I'd give to be in the SuperBowl-winning city the day after the victory!
Maybe next year. I didn't feel like publishing about SuperBowl graphics when the wound was so very raw. But now is the moment.
The following chart came from Orange County Register on the run-up to the Superbowl. (The bobble-head quarterbacks also came from OCR). The original article is here.
The choice of a set of dot plots is inspired. The dot plot is one of those under-utilized chart types - for comparing two or three objects along a series of metrics, it has to be one of the most effective charts.
To understand this type of design, readers have to collect three pieces of information: first is to recognize the dot symbols, which color or shape represents which object being compared; second is to understand the direction of the axis; third is to recognize that the distance between the paired dots encodes the amount of difference between the two objects.
The first task is easy enough here as red stands for Atlanta and blue for New England - those being the team colors.
The second task is deceptively simple. It appears that a ranking scale is used for all metrics with the top ("1st") shown on the left side and the bottom ("32nd") shown on the right. Thus, all 32 teams in the NFL are lined up left to right (i.e. best to worst).
Now, focus your attention on the "Interceptions Caught" metric, third row from the bottom. The designer indicated "Fewest" on the left and "Most" on the right. For those who don't know American football, an "interception caught" is a good defensive play; it means your defensive player grabs a ball thrown by the opposing team (usually their quarterback), causing a turnover. Therefore, the more interceptions caught, the better your defence is playing.
Glancing back at the chart, you learn that on the "Interceptions Caught" metric, the worst team is shown on the left while the best team is shown on the right. The same reversal happened with "Fumbles Lost" (fewest is best), "Penalties" (fewest is best), and "Points Allowed per Game" (fewest is best). For four of nine metrics, right is best while for the other five, left is best.
The third task is the most complicated. A ranking scale always has the weakness that a gap of one rank does not yield information on how important the gap is. It's a complicated decision to select what type of scale to use in a chart like this, and in this post, I shall ignore this issue, and focus on a visual makeover.
I find the nine arrays of 32 squares, essentially the grid system, much too insistent, elevating information that belongs to the background. So one of the first fixes is to soften the grid system, and the labeling of the axes.
In addition, given the meaningless nature of the rank number (as mentioned above), I removed those numbers and used team logos instead. The locations on the axes are sufficient to convey the relative ranks of the two teams against the field of 32.
Most importantly, the directions of all metrics are now oriented in such a way that moving left is always getting better.
While using logos for sports teams is natural, I ended up replacing those, as the size of the dots is such that the logos are illegible anyway.
The above makeover retains the original order of metrics. But to help readers address the key question of this chart - which team is better, the designer should arrange the metrics in a more helpful way. For example, in the following version, the metrics are subdivided into three sections: the ones for which New England is significantly better, the ones for which Atlanta is much better, and the rest for which both teams are competitive with each other.
In the Trifecta checkup (link), I speak of the need to align your visual choices with the question you are trying to address with the chart. This is a nice case study of strengthening that Q-V alignment.