How to Choose the Match to Broadcast

I was reading the Sunday paper yesterday and whilst I normally skip the sports section, especially during baseball’s offseason, this time a brightly coloured map caught my attention. Of course then I had to read the article, but I am glad that I did.

On Sunday the New York Times ran a print piece—I mean I assume I can find it online (I did.)—about CBS chooses which American football matches to air in the country’s markets. It is a wee bit complicated. And if you can find it, you should read it. The process is fascinating.

But I want to quickly talk about the design of the thing. Remember how I said a map caught my attention. That was pretty important, because the map was not the largest part of the article. Instead that went to a nice big photo. But the information designer I am, well, my eyes went straight to the map below that.

The story dominated the section page
The story dominated the section page

There is nothing too special about the map in particular. It is a choropleth where media markets are coloured by the game being aired yesterday. (The piece explains the blackout rules that changed a few years ago from what I remember growing up.)

But then on the inside, the article takes up another page, this time fully. It runs maps down the side to highlight the matches and scenarios the author discusses, reusing the same map as above, but because this is an interior page, in black and white. It probably looks even better online as they likely kept the colour. (They did. But the maps are smaller.)

To have that much space in which to design an article…
To have that much space in which to design an article…

Overall, I really enjoyed the piece and the maps and visuals not only drew me into the piece, but helped contextualise the story.

Credit for the piece goes to Kevin Draper.

Trumping (Most) All on Twitter

Initially I wanted today’s piece to be coverage of the apparent coup d’état in Zimbabwe over night. But while I have found some coverage of the event, I have not yet seen a single graphic trying to explain what happened. Maybe if I have time…

In the meantime, we have the Economist with a short little piece about Trump on Twitter and how he has bested his rivals. Well, most of them at least.

Trumping one's rivals
Trumping one’s rivals

The piece uses a nice set of small multiples to compare Trump’s number of followers to those of his rivals. The multiples come into play as the rivals are segmented into three groups: political, sport, and media. (Or is that fake media?)

Small multiples of course prevent spaghetti charts from developing, and you can easily see how that would have occurred had this been one chart. But I like the use of the reddish-orange line for Trump being the consistent line throughout each. And because the colour was consistent, the labelling could disappear after identifying the data series in the first chart.

And worth calling out too the attention to detail. Look at the line breaks in the chart for the labelling of Fox News and NBA. It prevents the line from interfering with and hindering the legibility of the type. Again, a very small point, but one that goes a long way towards helping the reader.

I think the only thing that could have made this a really standout, stellar piece of work is the inclusion of another referenced data series: the followers of Barack Obama. At 97 million followers, Obama dwarfs Trump’s 42.2 million. Would it not be fantastic to see that line soaring upwards, but cutting away towards the side of the graphic would be the text block of the article continuing on? Probably easier for them to do in their print edition.

Regardless, this is another example of doing solid work at small scale. (Because small multiples, get it?)

Credit for the piece goes to the Economist Data Team.

Power Sapped

Following on yesterday’s post about the Red Sox offence, I wanted to follow up and look into their power numbers. So here we have a smaller scale graphic. Nothing too fancy, but the data backs what my eyes saw all year. A definite power drain up and down the Red Sox lineup in 2017.

Where did all the power go?
Where did all the power go?

The Red Sox Offence in 2017

Like I said yesterday, the Red Sox season is over. And the coverage on offseason needs began in the morning papers. But I wanted to follow up on the data from yesterday and delve a bit more deeply into the offence.

Yes, we know it was roughly league average across the team. And we know it took a hit with David Ortiz’s retirement at the end of last year. But what happened? Well, I took those same OPS+ numbers for the starting nine and compared 2017 to 2016. I then looked further back to see how those same players performed throughout their careers (admittedly I skipped Hanley Ramirez’s 2 plate appearances in 2005.)

You should take a look at the full graphic, but the short version, pretty much everyone had an off year. And when everyone has an off year, it is a pretty safe bet the team will have an off year.

You can't all take a break…
You can’t all take a break…

A Brief Review of the Boston Red Sox Season

Well the 2017 season ended yesterday afternoon for my Boston Red Sox as we lost 5–4 to the Houston Astros and they took Game 4 of the ALDS. So this morning we will surely see the critiques and hot takes on what to do to improve the team begin to make the internet rounds.

But before we get into all of that, I wanted to take a look at the 2017 season from a data perspective. At least, the regular season. After all, we can see how Sale in Game 1 and Kimbrel in Game 4 just had poorly timed bad days. But what about the other 162 games? After all, we will need to win a lot of them if we want to make it back to the playoffs in 2018.

I just pulled a couple quick stats from Baseball Reference. Now we can quibble about which stats are best another time, but from my experience the more sabremetric datapoints are lost on a general audience. So here we are using OPS, basically a hitter’s average combined with his power/slugging ability, and ERA, the amount of runs a pitcher can be expected to allow every nine innings. I also threw in defensive runs saved above average, i.e. is the player saving more runs than an average player.

You can read the graphic for the details, but the takeaway is that Boston, we need not panic. The 2017 Red Sox were a good team. Far from perfect—here is looking at you lack of middle-of-the-order power—but a solid lineup, good rotation, good defence, and a fantastic bullpen. How can we add without subtracting too much?

Overall, not a bad team
Overall, not a bad team

2-point Conversions

I rarely watch American football. But I do like charts about it. So today’s post looks at a piece from Benjamin Morris who explored the scenarios in which a team should opt for the two-point conversion. For those of you who know even less about American football, you can attempt such a conversion after your team scores a touchdown. More often than not your team will go for the far safer and more assured one-point conversion, which if made makes a touchdown of seven points.

It turns out that teams should probably be looking for those two points a wee bit more often than they presently do. And to help teams figure that out, Morris made a small multiple chart looking at many different scenarios.

When to do it
When to do it

Credit for the piece goes to Benjamin Morris.

The Best Rivalry Is Back

But to be honest, it never really went anywhere. As you know, your humble author visited Boston this past weekend and got to see two games of his Red Sox against Tampa Bay. Tampa, of course, is not the rivalry to which I am referring, but things were heated back in the days when Maddon managed Tampa.

No, I am of course talking about the Red Sox–Yankees rivalry. Two weeks ago FiveThirtyEight posted an article about the rivalry and how it has returned. Admittedly, they meant not from the perspective of bitter hatred for all things Yankees, but rather that the Yankees are attempting to be good again.

This chart from the article is nothing more than a line chart. But I just wanted to point out that the rivalry lives, though in my mind it never really went away.

Down with the Empire…
Down with the Empire…

Credit for the piece goes to the FiveThirtyEight graphics department.

Colin Kaepernick

Colin Kaepernick is a contentious figure in American football because of the protests he started against the US national anthem. While other protesting players remain on teams and play, Kaepernick remains unsigned despite what some say is a talent above other players. And as the American football season just began, this article from the Washington Post caught my attention.

Some of the arguments I have seen for Kaepernick’s unsigned status allege he just is not very good. But is that so? What does the data show? Well thankfully the Post dived into that and is running what we can best call a Kaepernick tracker comparing him to qualified quarterbacks in the NFL.

Clearly better than a host of other quarterbacks
Clearly better than a host of other quarterbacks

It turns out, he is a middle-of-the-pack quarterback, demonstrably better than half-a-dozen and sitting solidly amongst an almost third-tier or cluster of players. The data clearly shows that poor performance is not the reason for remaining unsigned, otherwise he would have replaced any number of quarterbacks. True, it could come down to his dollar cost, but most likely his remaining unsigned, compared to almost a dozen players underperforming him, is related to his protests.

Now from the design standpoint, I also wanted to call attention to this article because of the way it handles definitions. The article uses the statistic adjusted net yards per attempt to assess performance. But what does that actually mean? Well, in the digital margins of the piece, the designers include an explanation of that statistic. I thought this was a really well-done part of the article, not interrupting the main narrative flow for a definition that a portion of the audience probably knows. But the more casual followers or people more interested in the political nature of the story would have no idea, and this does a great job of explaining it to us laymen.

What does it all mean?
What does it all mean?

Credit for the piece goes to Reuben Fischer-Baum, Neil Greenberg, and Mike Hume.

The NFL Draft Comes to Philadelphia

The joke I have been telling everyone in person this past week: I changed jobs and moved 750 miles from Chicago to Philadelphia, but I still cannot escape the NFL Draft. The two previous drafts occurred across the street from my last job and this year they are three blocks away from my new flat. Traffic is a bloody nightmare. So while there is a lot of news to cover through data visualisation and design, the local story is the NFL Draft that begins tonight next to the Rocky statue and in front of the Art Museum. We will return to trade wars with Canada, tax cuts for the rich, North Korea, climate change, and other things over the next few weeks.

Today’s piece comes from the Washington Post and looks at NFL Draft success across the NFL. Unfortunately for all of you, I know almost nothing about the NFL except Free Tom Brady. (I have to transfer my Red Sox allegiance somewhere, right?) But this set of small multiples looks fantastic and generally tells me that the Colts and Packers—the latter likely to the chagrin of my Chicago-born followers—have historically done well.

While I fail to understand the references/anecdotes, the grid looks nice…
While I fail to understand the references/anecdotes, the grid looks nice…

Aesthetically, I am not sure about the handwriting typeface. I wonder: could the content have been handled better by a more traditional face?

Credit for the piece goes to Reuben Fischer-Baum.

Andy Murray

A changeup from the political coverage, here we have sportsball! And by sportsball I mean tennis, if you did not get that from today’s post title. Andy Murray won the ATP World Tour finals, and with it won the number one seed in tennis, displacing Novak Djokovic.

When Andy Murray surpassed Novak Djokovic
When Andy Murray surpassed Novak Djokovic

Nothing super fancy going on here, just a line chart. But, it does do a good job of showing how over the last year, the slow decline of Djokovic and the ascendance of Murray.

Credit for the piece goes to John Saeki.