Late last week I was explaining to someone in the pub why the World Cup matches are played beyond their 90 minute booking. For those among you that do not know, basically the referees add up all the stoppage time, i.e. when play stops for things like injuries or people dilly dallying, and then tack that on to the end of the match.
But it turns out that after I explained this, FiveThirtyEight published an article exploring just how accurate this stoppage time was compared to the amount of stopped time. Spoiler: not very.
In design terms, the big takeaway was the dataset of recorded minutes of actual play in all the matches theretofore. It captured everything but the activity totals where they broke down stoppage time into categories, e.g. injuries, video review, free kicks, &c. (How those broke out across an average game are a later graphic.)
Through 27 June
The setup is straightforward: a table organises the data for every match. The little spark chart in the centre of the table is a nice touch that shows how much of the 90 minutes the ball was actually in play. The right side of the table might be a bit too crowded, and I probably would have given a bit more space particularly between the expected and actual stoppage times. On the whole, however, the table does its job in organising the data very well.
Now I just wonder how this would apply to a baseball or American football broadcast…
The World Cup continues. Well for a few teams. Some have already been eliminated from the Round of 16. But for those Americans rooting for Team America, well, if you have not yet figured it out, you got knocked out well before the World Cup even started by…Panama. And so you are stuck in the question of who’s next? Thankfully FiveThirtyEight, in addition to their fantastic live probabilities that we looked at the other day, put together a little quiz to help you find your new team.
You answer seven questions and you are told your new allegiance. Questions like this:
How would you answer?
Naturally I took the quiz and discovered that in addition to England, I am cheering for…
Goal? Make that skål!
Yep. Fantastic since I was just there in December and happened to love Stockholm. But what I love about this piece is how it uses data to create the newfound bond I have with Sweden. Often times you take a quiz and are given an answer without any sense of why the answer was correct. Here, FiveThirtyEight plots the seven different variables used to create your newfound personality and then shows you how you scored.
Right in the middle there
It’s Friday, it’s the World Cup. Have a great weekend. And in addition to England on Sunday, I’ll now be cheering for Sweden against Germany on Saturday.
Credit for the piece goes to Michael Caley, Rachael Dottle, Geoff Foster, Gus Wezerek, Daniel Levitt, Emily Scherer, and Jorge Lawerta.
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.
When sums are greater or less than the whole…
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.
If you live under a rock or in America, the World Cup starts today. (Go England.) So what else to have but a chart-driven piece from the BBC from last week about the World Cup. It features seven charts encapsulating the competition. But the one I want to focus on? It’s all about the host nations, in this case Russia.
To host, or not to host, that is the question of how much can you pay FIFA officials under the table…
On its design, I could go without the football icons to represent points on the dot plot, but I get it. (Though to be fair, they work well as icons depicting the particular World Cup event in another set of graphics elsewhere in the article.) In particular, I really like the decision to include the average difference between a host nation’s points in non-hosting matches vs. hosting matches.
It does look like the host nation scores more points per match than when they are not hosting. And that—shameless plug—reminds me of some work I did a few years back now looking at the Olympics and the host nation advantage in that global competition.
Yo. C’mon, bro. This jawn is getting tired. Just stop already.
If you did not catch it this week, the most important news was Donald Trump disinviting the Super Bowl champions Eagles to the White House to celebrate their victory over the Patriots. He then lied about Eagles players kneeling during the US anthem—no player did during the 2017 season. He then claimed that the Eagles abandoned their fans. Yeah, good luck convincing the city of that.
So naturally we have a Friday graphic for youse.
That’s 25,304.
Full disclosure: I root for the Patriots. But I mean, seriously, can’t youse guys do the math?
On Friday Albert Pujols joined the very elite club of baseball players who have managed 3000 hits in their career. Thankfully FiveThirtyEight covered it with a few graphics in an article that pointed out just how hard it is to do. Especially because, and I did not know this, Pujols did it in a not terribly common fashion. (Funny story, I had to explain this past weekend how Randy Johnson was a ridiculous pitcher, in the lots-of-strikeouts-and-also-exploded-a-bird way.)
My video game version of me would probably be on there if only those games lasted more than one season…
The piece uses a ternary plot, which we can also just call a triangle chart because it is, you know, in the shape of an equilateral triangle, to look at three components of Pujols’ hit skill.
There are different types of hitters in baseball. The guys who crush home runs all the time, the guys who hit singles all the time, guys who walk a lot. (Technically a walk is not a hit, but they are still getting on base.) There are fancy metrics that can be used to tease out just how much power is in a person’s game, and when you compare that to the batting average and to their walk rate, you can see clusters of players.
These kind of charts can be difficult to read—what does it mean for a player in a certain area of the chart? But what the designer did real well here is label an example of the type of player. Ichiro, called out for being a singles machine, is notable because he just sort-of-retired last week. He also has something like another 1500 hits back in Japan. That guy can hit.
Credit for the piece goes to Neil Paine and Rachael Dottle.
Yesterday was Patriots’ Day, celebrated in Massachusetts and Maine—and in my research for this post, apparently now in Connecticut as of this year and Wisconsin of all places—with the date used as that of the famous Boston Marathon. Since I live in none of those states, I know it only because to my knowledge it is the only day we get morning baseball. As the Red Sox play in the morning with the Marathon runners passing through the neighbourhood mid-game-ish.
But yesterday was some wet weather along the East Coast and whilst the Red Sox game was postponed to May—no longer a morning game—the Marathon went on. One has to wonder, however, if those conditions affected the race—they almost certainly did—because this year’s winning times were the slowest in years. Thankfully FiveThirtyEight captured it in this graphic.
Yeah, I definitely couldn’t do that…
It makes nice use of colour to highlight the origin of the various runners and then highlights yesterday’s two winners: an American woman and a Japanese man. Those two nations have not won in a couple of years.
Overall a solid little piece that makes me sad I have to wait until 2019 for another chance at morning baseball.
Credit for the piece goes to the FiveThirtyEight graphics department.
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.)
Switch numbers one and two and I think this might be okay…
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.
What’s inside 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.
Old balls vs. new 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.
The upper tiers of baseball’s free agent market, as of 9 February
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.
Comparing the signed to the unsigned free agents
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.
How are the signed players doing versus their contract projections
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.
Are there any trends at the position level
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.
