We are in the midst of basketball playoffs right now. And one of the teams participating is the Golden State Warriors. They are pretty good at this whole basketball thing. One of the reasons is their star player Steph Curry. And it turns out that he is an enormous fan of popcorn. So much so that despite the widespread focus on power foods and healthy eating and wellness lifestyle, he devours the stuff before matches. So much so that NBA minders had to remove it from his hands during an all-star match last year.
He agreed to a request from the New York Times to rank each stadium, from 1 to 29, on the best popcorn. But he then went further and suggested that he rank the popcorn on a five-point scale on five different metrics: freshness, saltiness, crunchiness, butter and presentation. Naturally, the Times agreed. And he prepared a dataset that the Times turned into this heat map.
The whole article is well worth a read for more insights into the player and his take on popcorn. I don’t know a thing about basketball, but if a player agrees to a request to rank stadiums based on their popcorn, but then goes further to create additional data that can be used to turn into a visualisation, he’s probably my favourite player. If only someone had asked this of Pedro, Nomar, or Big Papi back in the day. Here’s looking at you, Laser Show.
Happy Friday, everyone.
Credit for the piece goes to Steph Curry and Marc Stein.
The World Cup has had some impressive matches and some stunners. (And the two are not mutually exclusive.) But if you are like me and have to work during most of the broadcasts, how can you follow along? Well thankfully FiveThirtyEight put together a nice statistical model that provides the probability of a team winning—or drawing—in real time.
The design is fairly simple: a small table with the score and probability followed by a chart drawn as the match goes on. (Clearly I took this image at the half.)
I included a snippet of the table below to show the other work the FiveThirtyEight team put out there. You can explore the standings, the screenshot above, as well as the matches and then the brackets later in the competition.
The table makes nice use of the heat map approach to show is likely to make easy of the different stages of the competition. Like I said the other day, they are high on Brazil, because Brazil. But a little lower on Germany. But never count Germany out.
The only unclear thing to me in the table? The sorting mechanism. In Group B, at least whilst the Portugal match is ongoing, should probably have Iran at the top. After all, as of writing, it is the only team in the group to have won a match. The only thing I can guess is that it has to do with an overall likelihood to advance to the next round. I highly doubt that Iran will defeat either Spain or Portugal. But as with many knockout-style championships, anything can happen in a single match sample size.
Credit for the piece goes to Jay Boice, Rachael Dottle,Andrei Scheinkman, Gus Wezerek, and Julia Wolfe.
Last week we talked a lot about trade—and we will get back to it. But the World Cup is now in full swing and I want to take a look at a couple of things this week. But to begin, the Economist published an article about the difficulty of predicting the outcome of World Cups. It looks at the quirks of random events alongside more quantitative things like ranking systems and their differences.
But one graphic in particular caught my attention. It explore the difference between the ranking in individual players versus the teams as a whole. In short, some teams are valued more highly than their constituent players and others vice versa. The graphic is fairly straightforward in that it plots the team value on the y-axis and the players’ on the x.
Personally? I would never bet against Germany. Or Brazil.
But if your author is lucky, he’s going to enjoy the England–Tunisia match this afternoon for lunch—rooting for England, of course. Though thanks to some online tools that’s not the only team I’m rooting for this year. But more on that later this week.
Credit for the piece goes to the Economist graphics department.
I’m working on a set of stories and in the course of that research I came across this article from Philly.com exploring traffic accident in Philadelphia.
The big draw for the piece is the heat map for Philadelphia. Of course at this scale the map is pretty much meaningless. Consequently you need to zoom in for any significant insights. This view is of the downtown part of the city and the western neighbourhoods.
As you can see there are obvious stretches of red. As a new resident of the city, I can tell you that you can connect the dots along a few key routes: I-76, I-676, and I-95. That and a few arterial streets.
Now while I do not love the colour palette, the form of the visualisation works. The same cannot be said for other parts of the piece. Yes, there are too many factettes. But…pie charts.
From a design standpoint, first is the layout. The legend needs to be closer to the actual chart. Two, well, we all know my dislike of pie charts, in particular those with lots of data points, which this piece has. But that gets me to point three. Note that there are so many pieces the pie chart loops round its palette and begins recycling colours. Automotives and unicycles are the same blue. Yep, unicycles. (Also bi- and tricycles, but c’mon, I just want to picture some an accident with a unicycle.)
If you are going to have so many data points in the pie chart, they should be encoded in different colours. Of course, with so many data points, it would be difficult to find so many distinguishable but also not garish colours. But when you get to that point, you might also be at the point where a pie chart is a bad form for the visualisation. If I had the time this morning I would create a quick bar chart to show how it would perform better, but I do not. Trust me, though, it would.
I know, I know. You probably expect some sort of climate post given the whole Paris thing. But instead, this morning I came across an article where the supporting chart failed to tell the story. So today we redesign it.
The BBC has an article about MPs backing a tax on sugary drinks. Within the text is a graphic showing the relative importance of sugary drinks in the sugar consumption of various demographics. Except the first thing I see is alcohol—not the focus of the article. Then I focus on a series of numbers spinning around donuts, which are obviously sugary and bad. Eventually I connect the bright yellow to soda. Alas, bright yellow is a very light colour and fails to hold its own on the page. It falls behind everything but milk products.
So here is 15 minutes spent on a new version. Gone are the donuts, replaced by a heat map. I kept the sort of the legend for my vertical because it placed soda at the top. I ran the demographic types horizontally. The big difference here is that I am immediately drawn to the top of the chart. So yeah, soda is a problem. But so are cakes and jams, you British senior citizens. Importantly, I am less drawn to alcohol, which in terms of sugars, is not a concern.
Credit for the original goes to the BBC graphics department. The other one is mine.
From time to time in my job I hear the desire or want for more different types of charts. But in this piece by Nick Brown over on Medium, we can see that there are really only a few key forms and some are already terrible—here’s looking at you, pie charts. How new are some of these forms? Turns out most are not that new—or very new depending on your history/timeline perspective. Brown illustrated that timeline by hand.
Worth the read is his thoughts on what is new for data visualisation and what might be next. No spoilers.