Last night was the third episode of the final series of Game of Thrones and thus marked its midway point. I shall save you from any spoilers, but I thought we could do a lighter post to start the week. This comes from the Economist and simply plots the characters and their implied probability of winning the Iron Throne.
For me, there are too many lines, too many colours and we get the usual spaghettification. But, c’mon, it’s a chart about Game of Thrones. That said, some small multiple grid of characters, sorted by probability would be pretty neat.
Credit for the piece goes to the Economist’s graphics department.
Yesterday we looked at the isolation of the US and Canada in keeping the Boeing 737 Max aircraft in the air. Later that day, both countries grounded those aircraft. Today in the print edition of the New York Times the front page used significant space to chart the vertical speed of the two crashed aircraft.
It uses the same scale on the y-axis and clearly shows how the aircraft gaining and losing vertical speeds. I am not sure what is gained by the shading below the 0 baseline. I do really enjoy the method of using a chart below the airspeeds to show the periods of increasing and decreasing vertical speed.
Credit for the piece Jin Wu, K.K. Rebecca Lai, and Joe Ward.
No. Definitely not. But, the position of this article by FiveThirtyEight is that the Phillies, the Philadelphia baseball team that just made the largest guaranteed contract in North American sports, may have purchased the rights to somebody who is a few years past his prime.
The author tracked the performance of similar baseball players over history and found that they peaked earlier and tailed off earlier.
Now, the obvious thing about this graphic that I dislike is the spaghetti-fication of the lines. What does help alleviate it, however, are the greying and lighter weight of the non-identified lines in the background. Interestingly, they are even lighter than the axes’ value lines. There is also a thin outline to the lines that helps them standout against each other.
I also wonder if a few more added benchmark lines would be useful. Elite seasons are defined as those with 8+ wins above replacement (WAR), an advanced measurement statistic. Could that level not be indicated with a line on the y-axis? What about the age of 26, before which the players would have had to produce one and only one 8+ WAR season to be eligible for the data set?
Of course, as I said at the beginning, the answer to this post’s title is no. Harper will make the Phillies a better team and the length of his contract will not be the albatross that was Ryan Howard’s. However, the Phillies may be paying for 13 years of subprime Harper.
Credit for the piece goes to the FiveThirtyEight graphics department.
This piece from the BBC is a few years old, but it provides some interesting nuggets about North Korea. Unsurprisingly it appeared on my radar because of the coverage of the Trump–Kim summit in Vietnam. The article says it is nine charts that tell you all you need to know about North Korea. Now, I do not think that is quite true, but it does contain the following graphic—I hesitate to call it a chart—that illustrates one of my favourite details.
The two figures illustrate the average height of a person from North Korea and then South Korea. What do you see? That the North Korean is shorter. This is despite the fact that the populations were the same just a few decades ago. The impact of years of malnutrition, undernourishment, and general lack of well-being have manifested themselves in the physical reduction of size of human beings compared to their nearly identical population to the south.
Thankfully the rest of the piece contains data on things like GDP, birth rates, and life expectancy. So there are some things in there that one should know about North Korea. As much as I find the story of height interesting, I struggle to think it is one of the nine things you should really know about the state.
Credit for the piece goes to Mark Bryson, Gerry Fletcher, and Prina Shah.
On Tuesday the San Diego Padres signed Manny Machado to a guaranteed contract worth $300 million over the next ten years—though he can opt out after five years. Machado was one of two big free agents on the market, the other being Bryce Harper. One question out there is whether or not these big contracts will be worth it for the signing teams. This piece yesterday from the New York Times tries to look at those contracts and how the players performed during them.
Like the piece we looked at Tuesday, this takes a narrative approach instead of a data exploratory approach—the screenshot above is halfway through the read. Unlike the Post piece, this one does not allow users to explore the data. Unlabelled dots do not reveal the player and there is no way to know who they are.
Overall it is a very strong piece that shows how large and long contracts are risky for baseball teams. The next big question is where, for how long, and how much will Bryce Harper sign?
Credit for the piece goes to Joe Ward and Jeremy Bowers.
Yesterday the New York Times published a fascinating piece looking at the data on how often President Trump has gone after the Special Counsel’s investigation. (Spoiler: over 1100 times.) It makes use of a number of curvy line charts showing the peaks of mentions of topics and people, e.g. Jeff Sessions. But my favourite element was this timeline.
It’s nothing crazy or fancy, but simple small multiples of a calendar format. The date and the month are not particular important, but rather the frequency of the appearances of the red dots. And often they appear, especially last summer.
Credit for the piece goes to Larry Buchanan and Karen Yourish.
Back in 2012 the New York Times ran what is a classic data visualisation piece on Mariano Rivera. It tracked the number of saves the legendary Yankees closer had over his career and showed just how ridiculous that number was—and how quickly he had attained it. Last week, the Washington Post ran a piece that did something very similar about LeBron James, a future basketball legend, and Michael Jordan, definitely a basketball legend.
The key part of the piece is the line chart tracking points scored, screenshot above. It takes the same approach as the Rivera piece, but instead tracks scored points. Unlike the Rivera piece, which was more “dashboard” like in its appearance and function, allowing users to explore a dataset, this is more narratively constructed. The user scrolls through and reads the story the authors want you to read. Thankfully, for those who might be more interested in exploring the dataset, the interactivity remains intact as the user scrolls down the article.
While the main thrust of the piece is the line chart, it does offer a few other bar and line charts to put James’ career into perspective relative to the changing nature of NBA games. The line chart breaking down the composition of James’ scoring on a yearly basis is particularly fascinating.
But, don’t ask me about how he fits into the history of basketball or how he truly compares to Michael Jordan. Basketball isn’t my sport. But this is a great piece overall.
Credit for the piece goes to Armand Emamdjomeh and Ben Golliver.
As someone who loves geography and maps, I have plenty of printed atlases and map books. One year, as a gift, my family gave me an early 20th century atlas. That one in particular is remarkable because of how much the world changed between 1921 and 2019—what was French West Africa is now several independent countries.
But our maps may be changing again as Greece has now formally recognised the Former Yugoslav Republic of Macedonia as North Macedonia, what most of the world simply calls Macedonia. But Greeks do not want you to confuse that Macedonia with the Macedonia (or Macedon) of Ancient Greece and Alexander the Great. The squabble over the name has prevented what will be North Macedonia from joining the European Union and NATO because of Greek objections.
As the Economist recently showed, however, it might take a little while before the name Macedonia catches on with the public at large. (Note, I intended to type North Macedonia but instead went with Macedonia. I opted to leave it incorrect just to show how difficult it will be.)
The plot uses my favourite small multiples to look at six countries whose names have changed. Some of you may be unfamiliar with the originals. Bechuanaland may be the most obscure, but Burma and Ceylon may be far more familiar. Of course the historian in me then wonders why the mentions of countries spiked in books. But small multiples are usually not the place to do detailed annotations to humour an audience of one.
In terms of its design, we have an effective use of colour and line. I may have dropped the thin red line for the max 100 value as it makes the piece a bit busy overall, but that might just be house style.
Of course for this graphic in particular, we will have to wait several years before we can add Macedonia/North Macedonia to the plot.
Credit for the piece goes to the Economist Data Team.
January has ended, and with it for, apparently, a very few Britons, Dry January. The Economist looked at alcohol consumption, using a proxy of beer sales, and compared that against the number of times people searched for “Dry January” on Google.
What I really like about this chart is that it does not try to combine the two series into one. Instead, by keeping the series separate on different plots, the reader can clearly examine the trends in both searches and consumption.
You also run into the problem of how to overlay two different scales. By placing one line atop the other, the user might implicitly understand that as higher or better than the lower series when, one, that may not be true. Or, two, the scales are so different they prevent the direct comparison the chart would otherwise imply as possible.
Here, the designers rightly chose to separate the two plots, and then highlighted the month of January. (I also enjoy the annotation of the World Cup.) I might have gone so far as to further limit the palette and make both series the same colour, but I understand the decision to make them distinct.
But, overall, as the piece points out, drinking in Britain seems to correlate to the weather/temperature. People go out to the pubs more on warmer days than colder. But regardless of any post-holiday hangover, they still consumer beer in January.
I’ll drink to that.
Credit for the piece goes to the Economist Data Team.