The 2018 season starts today with I think every team playing—the Red Sox open down in St. Petersburg against the Rays. So today’s post is on the light side as I could not find the awesomest baseball graphic. But FiveThirtyEight did at least preview the season and ran some projections. Naturally, I disagree with their projections. But I think finally this year the Yankees will be more of a threat to the Red Sox than they have been in years. The rivalry is back. (Though it never really went away in my mind.)
The above is the screenshot for the American League East, because Boston. But, the rest of the AL is on that page as well. For those of you from my more National League-following cities like Philadelphia and Chicago, FiveThirtyEight also previewed the NL divisions here.
Baseball is finally back as Spring Training continues to push through March, getting us closer to Opening Day. But one lingering question from last year remains: why the increase in power and home runs? While Major League Baseball (MLB) says there has been no change to the baseball, many think otherwise.
FiveThirtyEight published a piece looking at the insides of eight baseballs, four predating the power surge, which began after the 2015 All Star Game, and three balls since in addition to a newly manufactured and unused ball.
The piece uses a few graphics to showcase the differences, including this cutaway diagram highlighting the different layers of a baseball.
But the real gem is the X-ray photography done to examine the balls without cutting into them. Thankfully for those of us unfamiliar with x-rays, the designers provided a legend showing the clearly different core densities in the balls.
If you are interested in baseball, and in particular the increase in home runs, the whole article is worth the short read. And if you’re not, well, the x-ray views of baseballs are still pretty neat.
Credit for the piece goes to Rob Arthur and Tim Dix.
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.
The Winter Olympics are creeping ever closer and so this piece from the New York Times caught my eye. It examines the impact of climate change on host cities for the Winter Olympics. Startlingly, a handful of cities from the past almost century are no longer reliable enough, i.e. cold and snow-covered, to host winter games.
This screenshot is of a bar chart that looks at temperatures, because snow and ice obviously require freezing temperatures. The reliability is colour-coded and at first I was not a fan—it seemed unnecessary to me.
But then further down the piece, those same colours are used to reference reliability on a polar projection map.
That was a subtle, but well appreciated design choice. My initial aversion to the graphic and piece was changed by the end of it. That is always great when designers can pull that off.
Credit for the piece goes to Kendra Pierre-Louis and Nadja Popovich
Whilst away, I came upon this piece in the following of my offseason baseball news. The New York Times published it between Christmas and New Years and the piece looks at the origins of sports persons in European football leagues compared to several American sports leagues, including American football, baseball, and basketball.
The piece features an opening set of small multiples comparing all the leagues. Maddeningly, I wanted details and mouseovers and annotations at the start. Fortunately, as the reader continues through the article, each small multiple becomes big and the reader can explore the details of the league.
Credit for the piece goes to Gregor Aisch, Kevin Quealy, and Rory Smith.
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.
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.)
Overall, I really enjoyed the piece and the maps and visuals not only drew me into the piece, but helped contextualise the story.
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.
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.
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?