Previously on Dragon Ball Z, we discussed whether it’s better to run hot and cold like a reheated frozen burrito or show up at the plate with the comforting and consistent warmth of a hearty helping of mac and cheese. Specifically, when you’re a hitter trying to microwave some offense in the playoffs. The tl;dr of that article: When comparing streaky hitters to their more consistent colleagues, the streaky hitters came closer to replicating their regular season numbers in the postseason. Despite the fluky nature of playoff series and their bite-sized samples that leave no space for slumps, hitters prone to slumping still bring enough electricity when they do get hot to maintain a charge in their individual numbers.

But individuals don’t win the World Series, teams do. In the context of a team playing a sport where the superstars don’t necessarily factor into every plate appearance, individual performances don’t carry the same weight that they do in other sports. Not only do teams need contributions from multiple hitters in the lineup, but the sequence of those contributions matters too.

In my last article, I used wOBA, which is derived from the run values associated with specific events (i.e. walks, doubles, home runs), to measure individual output. In turn, run values are historical averages of the number of runs scored following the given event. Those historical averages assume that what follows a given plate appearance is a league-average hitter doing whatever is most statistically likely. But that’s not how it works irl. The player on deck might be better or worse than league average, might have distinct tendencies toward hitting the ball on the ground or in the air, might be 0-fer their last eleventy-billion, or might be hotter than soup in the summertime. Read the rest of this entry »

Programming note: I’m taking a break from my Five Things column this week, as I’m traveling to Chicago for Saberseminar. Five Things will return next week with events from the last two weeks. In the meantime, please enjoy a ridiculous hypothetical.

This week, someone in my chat asked me an interesting hypothetical: How different would baseball be if the fences were the same distance from home plate all the way around? It would obviously be wildly different from how the sport currently works. Center field is the deepest part of the park by far, of course, and it’s hard to picture exactly what an equidistant fence would look like. You might think it’s a triangle, but that’s not right – it looks more or less like an arc, which is what an actual stadium looks like, only with a much sharper curvature.

That sounds so darn weird that I wanted to see what it would mean for offense. I don’t have any strong analytical reason for doing so. We aren’t plumbing the depths of smart baseball analysis here; we’re making up a dumb world and wondering what kinds of dumb things would happen in it.

First things first: There would be more home runs. I picked 370 feet as the distance because it feels reasonably close to the real world average of fence depths. I picked a 10-foot tall wall for similar reasons; if we’re getting weird in some ways, I’d prefer to standardize the others. There’s an easy math trick you can use here; baseballs tend to fall at roughly a 45 degree angle by the time they’re descending, their forward momentum getting slowly blunted by air resistance. That means that a ball that clears the wall by a millimeter would travel 10 more feet before hitting ground that was at field level – in an outfield bullpen, say. In other words, every ball that travels 380 or more feet in the air is going to be a home run now. Read the rest of this entry »

As the man who inspired Brad Pitt’s most memorable role once said, “My shit doesn’t work in the playoffs.” Assuming Billy Beane wasn’t explaining an October Metamucil purchase to a grocery store cashier who simply asked how his day was going, what Beane likely meant was that the statistics used to construct his major league rosters don’t accrue large enough samples during postseason series to eventually even out in his favor. Over the course of 162 games, a team’s production settles into a reasonable representation of the squad’s true talent. But zoom in on any random seven-game stretch and the team on the field might look like a bunch of dudes in baseball player cosplay.

What applies to team outcomes applies just as well to player outcomes. A player with a perfectly respectable stat line in the regular season might morph into a pumpkin as the calendar shifts to fall, or on the flip side, an unlikely hero may emerge from the ashes of a cruel summer and put the whole team on his back.

With the law of averages in mind, I’d always assumed that the more consistent hitters would be better positioned to perform well in the playoffs. My thinking went like this: The natural variation in these hitters’ performances would never wander too far from their season-long average, making them the safer, more predictable options. Whereas streaky hitters — the ones with high highs, low lows, and steep transitions between the two — would be too reliant on “getting hot at the right time” to be the type of hitter a front office should depend on in the postseason.

Reader, I was incorrect. Read the rest of this entry »

Last week, I cracked the PitchingBot black box open a tiny bit and asked it to show me the worst pitches of the year. It was for fun, mostly; I think there are some interesting data in there, but the main thing I learned was that the worst pitches are non-competitive balls. That’s always a tough concept to grasp, because the ones that stick with us are the hanging sliders and no-ride fastballs right down the pipe, the kind of pitch that we see and go, “Oh I could hit a home run on that.” Like this one:

That’s the worst pitch in baseball this year by one specific metric: the likelihood that PitchingBot assigns it of turning into a home run. I’ll show you some more of them in a moment, but first I thought I’d lay out how I did this so you can get a sense of how the model is reaching its conclusions.

Cameron Grove, the creator of PitchingBot, wrote about this idea back before he started working for the Guardians, and he was kind enough to nudge me in the right direction when it came to looking at pitches not just for the “worst,” but the ones that are the most crushable.
Read the rest of this entry »

This is Michael Rosen’s first piece as a FanGraphs contributor. You may have read his previous work at the site, including his article about the Kirby Index, a metric he created to measure command using release angles. He lives in Los Angeles and works as a transportation planner.

Earlier this year, I tried to solve the riddle of how Shota Imanaga threw his invisible fastball. The pitch had (and still has) a rare combination of traits: At the time of writing, only Imanaga and Cristian Javier threw fastballs from super flat vertical approach angles (VAA) with elite induced vertical break (IVB). A fastball with a flat VAA or high IVB plays a trick on the hitter’s perception; a fastball with both qualities becomes nearly unhittable, or invisible, when located at the top of the zone. I posed two questions in that piece: Why was this invisible fastball so rare? And what was Imanaga specifically doing to throw a fastball with these traits?

The first question can be answered, my research shows, by looking directly at release angles. Release angles reflect the direction that the pitcher is aiming the ball at release, which I wrote about at length in my article on the Kirby Index from May. That act of aiming — specifically, the direction the ball is oriented out of the pitcher’s hand — also affects the amount of backspin on a four-seam fastball. Read the rest of this entry »

I’m sorry, assorted old people and grumps of the world. Michael Baumann got you all riled up yesterday by looking into whether clutch exists. It does! It’s inarguably a real thing. It’s also not very predictive, and even maybe not predictive at all. I know! It’s shocking (note: it’s not shocking). After reading that, I had no choice but to look into that other baseball truism: momentum.

There have been plenty of studies about it. The findings are consistently uninteresting. It’s basically this: Momentum probably has some effect, but it’s minimal. You can slice it a ton of different ways and get some version of that conclusion, whether you’re talking about a big win helping the next day or a string of important games begetting more.

I thought I’d add to the literature with a different study. I can’t remember which game in particular, but I was watching some ball last week when a team tied the game in the bottom of the fifth or sixth. One announcer mentioned offhandedly that they were heading in the right direction and had the opposition right where they wanted them. This isn’t rare. If you watch baseball, you’ve heard some version of it for sure. I tuned out before the end of the game, so I can’t tell you whether they were right, but I made a note to look at it later.

That particular definition of momentum – rallying to tie the game in the bottom half of the inning – felt ripe for study. I grabbed game logs from every game played since 2000 to take a crack at finding this effect. I went through the score after every half inning and noted a few things. First, I noted the score differential. Next, I noted the change in differential since the last half inning. Finally, I checked who won the game in the end. That let me find whatever subset I wanted and study the difference between games that were tied when the half-inning began and the ones where the home team tied it up during that half-inning.
Read the rest of this entry »

The other week, I was talking to a friend as she fretted over her lack of productivity. Her struggle wasn’t with laziness or a lack of motivation, but rather a severe case of burnout, following her around the way No-Face follows Chihiro in Spirited Away. In the moment, I could see her desperately reaching for more energy to get through the day, like when the chip bag is nearly empty, so you bring it to your mouth and tilt your head back to suck down whatever salty goodness remains.

But instead of encouraging her to power through, I went a different way.

“Y’know, it’s okay to take a break.”

She laughed and said, “That’s what my therapist keeps telling me.”

If you’ve ever described yourself as a perfectionist or a people pleaser, or tied your self-worth to your measurable output, you know taking breaks can be hard.

Athletes learn from the beginning to idolize hard work. Hard work is the salve for every ailment. Wanna get stronger? Work harder. Wanna play better? Work harder. Wanna go pro? Work harder than everyone else. The hardest workers earn their own dedicated titles and recognition, separate from their actual production. Cal Ripken Jr.’s consecutive games played streak earned him the Ironman title. MLB gives out a Heart and Hustle award to whichever player’s heart tells him to hustle the hardest. And every year, we read stories about players and coaches who are the first to arrive to work each morning and the last to leave at night. Read the rest of this entry »

Last Friday, Davy Andrews put Aaron Judge into context. That’s fun for the usual reasons – “Wow, look how dominant Aaron Judge is!” never gets old. But his final conclusion – Judge hits like most batters do when opponents hang a bad slider middle-middle – got me thinking. We have a pitch-level model that estimates the worst pitches. Can we use it to get an idea of what it looks like to throw a pitch so bad it turns your opponents into Judge-esque offensive producers?

There’s an easy way to sort this out. When you look at a pitcher’s player page here at FanGraphs, you can see how we model each pitch. There are a ton of scores, but I’m going to be focusing on PitchingBot today, for reasons I’ll explain shortly. The player pages break each pitch down by command and stuff. In our internal database, it’s even better: You can look at any individual pitch and get a grade on it. I set out to find the worst pitches on the year to see whether they made hitters look invincible.

Ironically, the worst pitch thrown this year made the batter look extremely vincible. Here it is in all its glory:

Read the rest of this entry »

“An egregious error of Umpire Hurst in construing the rules helped Boston to two runs and added to the confusion of the Orioles. In the fourth inning Boston had three men on bases and one out. Ryan came to the bat and scratched out a short fly over third base. Jennings ran for the ball, got under it and muffed it. According to Rule 45, Section 9, a batter is out ‘if he hits a fly ball that can be handled by an infielder while first base is occupied with only one out.’ Ryan should have been declared out whether the ball was muffed or not…

When seen at the club-house after the game he started in defense of his position by attempting a distinction between the outfield and infield, claiming that the ball was not hit to the infield, but when his attention was called to the wording of the rule, which does not state that the ball must be hit to the infield, but simply that it shall be such a ball as an infielder can handle, he abandoned that position, and argued that it was not a fly ball, but a line drive. He soon saw the absurdity of that argument, as a line drive which does not touch the ground is as much a fly ball as if it were hit 100 feet up into the air.”

– “Errors Lost the Game,” The Morning Herald, April 26, 1894

The graph below has been haunting me for weeks now. I made it, but there’s nothing unique about it. You can find an identical graph in this Alex Chamberlain piece, this Tom Tango blog post, or any number of other articles. It shows the batting average and wOBA for every batted ball, based on launch angle.

I cut off 20 degrees from either side, but you get the point. Worthless groundballs and popups are on the sides, and valuable line drives and fly balls make up a narrow sliver in the middle. It occurred to me a few weeks ago that we’ve been splitting batted balls into those same four categories for a very long time now. Moreover, one of those categories is suspect. If you’ve been reading FanGraphs for a while, you know that line drive rate is considered fluky rather than sticky. Only a handful of elite players – Luis Arraez, Freddie Freeman, maybe Steven Kwan – are capable of consistently putting up top-10 line drive rates. According to Baseball Savant, batters have a .639 wOBA on line drives this year. Hitting line drives is what every single batter is trying to do, and yet somehow what Russell Carleton wrote seven years ago still holds true: “There is some skill in hitting line drives, but it is hard to repeat, and how many line drives you hit seems to be unrelated to where you fall on the ground-ball/fly-ball spectrum.” I set out to find some new way to look at this old puzzle, figuring that with all of the tools as our disposal, there had to be a better way to slice this particular pie. I failed, but I came across some interesting things along the way, and that (I have decided after the fact) is what’s really important. Read the rest of this entry »

Imagine an auction that takes place between three bidders. The item in question? An envelope filled with money. All three bidders employ teams of analysts that attempt to ascertain how much money is in the envelope, based on a variety of evidence that isn’t important for this analogy. Each bidder thus arrives at an estimate of the fair value of the envelope. Then they place a single sealed bid. The highest bidder out of the three gets the envelope.

What bidding strategy would you employ? Here’s a bad one: Just bid what your team of analysts calculates as the expected value of what’s in the envelope. The reason this is bad is known as the winner’s curse. If each bidder comes up with an estimate of fair value and bids that number, the winner will be the one with the highest estimate of fair value. In other words, you’ll only win if your estimation of the envelope’s value is higher than everyone else’s, and since you’re always paying exactly what you’re hoping to gain, you’ll tend to lose in the long run.

Allowing for a lot of approximation, this situation describes free agency in major league baseball. Every free agent has an unknowable amount of expected future production. Teams employ armies of analysts who attempt to estimate that production. Then, armed with that knowledge, they make contract offers to that free agent, in competition with other teams.

As I said, there’s a ton of approximation and simplification going on here. Players aren’t envelopes filled with money. Team context matters. Players don’t have to accept the highest bid. Tax regimes aren’t equal, and non-monetary incentives matter, too. Contracts are complex, and there’s no requirement that they be the same number of years, have the same number of options, no trade clauses, or anything of the sort. There’s no agreed-upon universal value system; different players present different value to different teams.

But that doesn’t mean the abstracted case has no use. As we approach the trade deadline, I think there’s one clear one: dispelling the myth that teams refuse to give up much to trade for a player who just signed a big free agent deal — after all, if they valued them enough for a blockbuster, they would have just offered a bigger contract, right? That’s a great soundbite, so you hear it all the time, but it doesn’t jive with established economic theory. Read the rest of this entry »