Now that the second issue of the inde­pen­dent maga­zine I edit and design, Road Grays, has been released, I thought I’d write a bit about the process behind one of my favorite pieces.

Road Grays is a base­ball maga­zine, but it focuses more on the stories behind the game than on stats or stars. In Quantified, a recur­ring depart­ment, I’ve been using data visu­al­iza­tion to explore ideas that typi­cal “stats” aren’t concerned with—because they happen off the field. (In the first issue, for exam­ple, I created a taxon­omy of minor league team nicknames.)

By diving deep into some­thing that “doesn’t matter,” I suppose we’re gently making fun of many fans’ obses­sion with statis­tics. But the fun part is that I do actu­ally find some fasci­nat­ing insights into the state of the game, simply through the act of making these visu­al­iza­tions. And that works by not having precon­ceived notions of what I’m going to find.

For the current issue’s piece, I had a general idea that “value” would be an inter­est­ing thing to explore. There’s a belief in modern sports analyt­ics that one can calcu­late “the cost of a win”: essen­tially, the money it takes to sign a hypo­thet­i­cal player who improves the team’s perfor­mance by one win. I thought it might be fun to turn that inside out some­how, to talk about costs and value in a differ­ent way.

First I pulled as much data as I could find, from publicly-​available sources, that seemed like it could be useful: atten­dance, ticket prices, payroll, and wins from last season. I hoped that some combi­na­tion of these might yield an inter­est­ing corre­la­tion or two, but I had no idea what that might be. 

It wasn’t until I started to trans­late it visu­ally that I discov­ered some possi­bil­i­ties. (The design process can be an uncov­erer of mean­ing, not merely a conveyor of it.) There’s a great open-​source online tool called Rawgraphs that’s invalu­able for this: You can input sets of data, assign them to differ­ent vari­ables (X axis, Y axis, and size or color, for exam­ple) and see the results in seconds. Then you can reas­sign them and see if some­thing else inter­est­ing pops up. 

What I’m look­ing for at this stage is: What patterns can I find that are inter­est­ing? What outliers might there be that help tell a story, too? And then, what’s the clear­est way to present them?

I settled on the idea of show­ing ticket prices vs home wins, as a way to illus­trate how likely a fan was to “get their money’s worth” when attend­ing a game. My first big deci­sion: Which is the X axis and which is the Y? 

With a couple clicks on Rawgraphs I could compare both options, and one seemed to make the trend line more obvi­ous. Serendipitously, it also worked better with my layout: I knew this chart would need to span a full spread, and the clearer option didn’t have any data points situ­ated in the gutter.

Next came a number of visual design deci­sions. In order to help make sense of the teams’ place­ment on the graph, I wanted to repre­sent how far from the “aver­age” cost per win each team was—so at first I tried using differ­ent colors for the team names based on their distance from the trend line. But it ulti­mately felt more clear to put those colors in the back­ground to create a sort of “value map,” on top of which each team’s loca­tion could be knocked out in white.

With any data visu­al­iza­tion project, I like to find addi­tional “layers” of infor­ma­tion, to deepen the story being told. In this case, display­ing the median values, as well as a line sepa­rat­ing winning from losing records, add another such layer: They help create points of compar­i­son (and under­score how some teams can have good “value” despite perform­ing poorly and vice versa.) Secondary mini charts pull out a few inter­est­ing outliers, acting as guides to some of the insights the data provides, and they make use of some of the atten­dance info I’d gath­ered earlier. Never throw data away: You might find a place for it later!

The final piece can be found in the second issue of Road Grays.