A few days ago, a confidential report by the British Treasury was leaked to the press. It confirmed what many had feared, that the economic forecasts for the regions of the UK were not that rosy under different models for different Brexit scenarios.
The scenarios looked at the change in growth for regions over the next fifteen years under three conditions: leaving the European Union, but remaining in the single market; leaving the single market but crafting a free trade agreement with the EU; and no deal, the so-called Hard Brexit.
I charted the data and it speaks for itself. Brexit is bad. The least worst option would be to remain in the single market. The North East, for my non-British readers that’s the area south of Scotland and home to Newcastle, is particularly not forecast to do so well in a hard Brexit.
As the debate rages on in the UK about how to proceed, the data should contribute to the conversation. While forecasts and projections can be wrong—what is the certainty of these forecasts, for example—it does make one wonder that if a better economy was a selling point of Brexit, do these forecasts make the idea of Brexit still worth it?
Data comes from the BBC. Credit for the design is mine.
Baseball season begins next week. For different teams it starts different days, but for the Red Sox at least, pitchers and catchers report to Spring Training on Tuesday. But the Red Sox, along with many other teams throughout baseball, have holes in their roster. Why? Arguably because nearly 100 free agents remain unsigned.
I do not intend to go into the different theories as to why, but this has been a remarkably slow offseason. How do we know? Well using MLB Trade Rumours listing of the top-50 free agents this year, and the signings reported on Baseball Reference, we can look at the upper and middle, or maybe upper-middle, tiers of free agency.
Kind of messy to look at with all the player labels, but we can see here the projected contracts, in both length and total value, along with the contracts players signed, if they have. And for context we can see how those contracts compares to the Qualifying Offer (QO). What’s that? Complicated baseball stuff that is meant to ensure teams that lose stars or highly valuable players are compensated, especially since they might come from smaller market teams that cannot afford superstar prices. The QO is meant to help competitiveness in the sport. How does it do that? Let’s just say complicated baseball stuff. We should also point out that some players, most notably the Yankees’ Masahiro Tanaka, were expected to opt out of their contracts and try the free market. Tanaka did not, which is why his projection was so far off.
So is it true that free agency is or has moved slowly? Consider that approximately 100 free agents remain unsigned as of late Thursday night—please no big signings tomorrow morning—and that of the top 50, 22 of them remain unsigned. And if we take the QO as a proxy for the best players in the game, add in two players who were exempt because baseball stuff, we can say that 8 of the 11 best players remain unsigned. Though, in fairness to ownership, three of those players are reportedly sitting on multi-year offers in the nine-figure range.
But if players are unsigned, does that mean they are competing for lower value contracts? Possibly. If we use MLB Trade Rumours’ projected contracts, because in years past they have proven smart at these things, we can see that for the 28 who have signed, it’s a roughly even split in terms of the number of players who have signed for more or less than their projection. Sometimes however, non-monetary factors come into play. Two notable free agents, Todd Frazier and Addison Reed, both reportedly signed lesser value contracts to play closer to a specified geography, in Frazier’s case the Northeast and in Reed’s the Midwest.
But the telling part in that graphic is not necessarily the vertical movement, i.e. dollars, but the horizontal movement. (Though we should call out the cases of Carlos Santana and Tyler Chatwood, signed by the Phillies and Cubs respectively, who did far better than projected.) Consider that a team might not have a lot of money to spend and so might extend a contract over additional years, offering job security to a player. Or in a bidding war, the length of the contract might be what leads a player to pick one team over another. In those cases we would expect to see more left-to-right movement. So far we have only had one player, Lorenzo Cain, who signed for more years than expected. Most players who have signed for less have also signed for fewer years. Note the cluster of right-to-left, or shorter-than-expected, contracts in the lower tiers versus the small, vertical-only cluster in the same section for those signing greater than projected contracts.
Lastly, are these trends hitting any specific positional type of player? Well maybe. Ignoring the market for catchers because of how small the pool was—though the case of Jonathan Lucroy as the unsigned catcher is fascinating—we can see that the market has really been there for relief pitchers as there are few of the top-50 remaining on the market. Starting pitchers and outfields, while with quite a few still on the market, have generally done better than projected. But infielders lag behind with numerous players unsigned and those that have signed, most have signed for less than projected.
But at the same time, I would fully expect that once these higher level free agents come off the board—while one would think they would certainly be signed, who knows in such a weird offseason as this—the unsigned middle and lower tiers will quickly follow suit.
Of course none of this touches upon age. (Largely because lack of time on my part.) Though, in most cases, getting to free agency in and of itself makes a player older by definition the way baseball’s pre-arbitration and arbitration salary periods work. (Again, more baseball stuff but suffice it to say your first several years you play for peanuts and crackerjacks.)
Hopefully by this afternoon—Friday that is—some of these players will have signed. After all, baseball starts next week. If we are lucky this post will be outdated, at least in terms of the dataset, come Monday. Regardless, it has been a fascinating albeit boring baseball offseason.
Credit for the data goes to MLB Trade Rumours and Baseball Reference.
January is the month of forecasts and projections for the year to come. And the Economist is no different. Late last week it published a datagraphic showcasing the GDP growth forecasts of the Economist Intelligence Unit. I used to make this exact type of datagraphic a lot. And I mean a lot. But what I really enjoy is how successfully this piece integrates the map, the bar chart, and the tables to round out the story.
The easy thing to do is always the map, because people like maps. They can be big, and if the data set is robust, full of data and colour. But maps hide and obscure geographically small countries. And then you have to assume that people know all the countries in the world. Problem is, most people do not.
So the bar chart does a good job of showing each country as equals, a slim vertical bar. In such a small space, labelling every country is impossible, but the designers chose a select number of countries that might be of interest and called them out across the entire series.
Lastly, people always like to know who is #winning and who is a #loser. So the tables at the extreme ends of the chart showcast the top and last five.
I may have rearranged some of the elements, and dropped the heavy black rules between the bins on the legend, but overall I consider this piece a success.
Credit for the piece goes to the Economist Data Team.
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.
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.
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?
Well after the last two weeks of recording solo trivia performances, I decided that this week I would showcase a team effort.
And we finally placed, ending the performance tied for first place. But if you look closely you will see the final score has us at second. Why when we were tied with the same number of points? Because tiebreaker. And after I was selected to represent the team, I needed to respond, within three seconds, with the names of Tom Hanks films in a back-and-forth response.
I could name only Saving Private Ryan and Castaway. My competitor, she named three. They won.
This past Wednesday I once again ended up playing trivia at the pub solo. Once again, I decided over the final pint that I would attempt to visualise my performance.
One thing to keep in mind is that on Wednesday there were fewer teams competing—five instead of nine. And while I never placed higher than tied for third, this week’s bar charts show how I was incredibly competitive until the final music round.
Despite an abysmal performance at naming celebrities as they were as children, my near-perfect second round kept me only five points behind first place. And a perfect fourth round meant heading into that final round I climbed back to being only three points back. Thankfully I knew more of the songs this past week. And enough to not finish last. But, I was close enough that a perfect round would have been enough to still place first.
Super helpful that Lord of the Rings questions appeared a few times.
Wednesday night I had the dubious honour of playing the weekly pub trivia solo. Accordingly my team name was Hats Solo. (After I opted not to wear my fedora one night, another regular team called itself Where’s Your Hat?) I started strong, had a second wind, but still faded to a seventh place (of nine) finish. As I finished my G&T, I decided I would visualise the results. Here, two days later, are my results.
Credit for the highlights, mine. The lowlights, someone else.
Pennsylvania was the country’s first state to operate a nuclear power plant for electricity generation and is today the second-largest nuclear-generated electricity state after Illinois. But in recent years the triple threat of the Pennsylvania Marcellus Shale natural gas boom, wind power subsidies, and solar power subsidies have hit the state’s nuclear industry hard. Consequently the power company Exelon has announced plans to shutdown the generating station at Three Mile Island—yes, that Three Mile Island—in 2019 if Pennsylvania does not rescue the industry as have the states of Illinois and New York, each facing similar challenges.
I wanted to take a look at the electricity generated by nuclear power in Pennsylvania, but had to settle for energy produced. And while the data was only as recent as 2014, it did extend back to 1960 thereby dating back almost as far back as nuclear power in Pennsylvania—it began in 1957.
The subject has always been of interest to me and was the focus of one of my first data visualisation pieces back at university. And so while the data is not quite the same, nor over the same geographic area, it is interesting to see the spike since even 2008. (Worth noting that even in a coal state the long, slow decline of coal even before President Obama is self-evident.)
Unfortunately the EIA data came through a .pdf and not a more accessible data file so I spent most of my time recreating the data. Consequently, I had little time to do more than track these changes. But even still, I think you would agree the message is clear: natural gas has quickly disrupted the market. (Let’s again ignore the fact I could not plot renewable energy sources.)
Small disclaimer I suppose, I have always supported nuclear power as part of a non-carbon energy portfolio. But I also grew up within sight of and fascinated by the Limerick Generating Station steam clouds, so call me biased.