For the better part of this decade, we’ve repeatedly published an article you can more or less predict. Nearly every year, a version of the same idea gets published. “You’re never going to believe it,” the article starts, “but fastballs got faster again this year.” There are usually some GIFs, maybe a winking joke about how we write this article every year and it keeps being true, and bam, 1,500 words out the door. Oh yeah! There’s also a kicker: “Fastballs keep getting thrown less frequently, too.”

Normally, I’d be writing that article again this year. There’s just one problem: four-seam fastballs didn’t get faster this year; in fact, they’ve been plateauing for a few years. This year’s four-seamers checked in at an average velocity of 93.9 mph. Adjusting for time of year (I used only data from August onward in each season so that we didn’t have any weather effects unique to 2020), here are the last five years of four-seam velocity:

The 2019 season was the fastest on record, and 2020 fell short of that mark. In fact, the last five years look overall unchanged. Look instead at sinkers, though, and you’ll see some velocity improvement:

Which one should we believe? Four-seamers are more common than sinkers, so the blended average looks like this:

Okay, so fastballs didn’t get any faster this year. Sinkers did, and that’s interesting for sure, but at the highest level, it feels like the inexorable march towards higher velocity might have stalled for the moment.

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You’ve heard of home field advantage. It’s simply a part of sports, like gravity or Tom Brady being competent and obnoxious. Here’s a dirty little secret, though: A decent chunk of home field advantage is actually first-inning advantage. Here, take a look at how home and away batters performed in the first inning and thereafter from 2010 to ’19:

The first inning has the biggest gap, with only the fifth coming even close. It’s a consistent effect year-to-year, and it’s a big deal: A 22-point edge in wOBA works out to three-quarters of a run per game, which would work out to roughly a .570 winning percentage, significantly higher than the actual edge. If you could bottle that edge and apply it to every inning, baseball would look very different.

This isn’t some novel effect I’ve just discovered. It’s well-established, though I’ve never seen a completely satisfactory explanation for it. Could it be that the home team’s defensive turn in the top of the first warms them up for their turn at bat? Maybe! One counterpoint here: Home DHs have a 20-point wOBA advantage on away DHs in the first inning, then only a six-point advantage thereafter. Maybe it’s not that, then.

A theory that makes more sense to me is that home pitchers have a unique advantage in the first inning. In that inning, and that inning alone, they can exactly predict when they’ll be needed on the mound. Have a perfect warmup routine? You can finish it just before first pitch, then transition directly to the game. Visiting pitchers are at the mercy of the game. Start too late, and you won’t be ready in time for the bottom of the first. Start too early, and an extended turn at the plate might leave you cold. Read the rest of this entry »

Here’s the honest truth about baseball analysis: Most of the ideas I look into don’t work. That’s mostly hidden under the surface, because it’s not very interesting to read an article about absence of evidence. Hey, did you know that batters who hit very long home runs see no meaningful effect on the rest of their performance that day? I did, because I looked into that at one point, but imagine an article about that and you can kind of see the problem. Read a whole thing looking for a conclusion and find none, and you might be more than a little irritated.

Now that I’ve told you how bad of an idea it is to write about failed ideas, I’d like to introduce you to an article series about ideas that didn’t pan out. I know, I know: I was bemoaning the difficulty of writing such an article just sentences ago. Some failures, however, are more interesting than others, and I’d like to think that I know how to tell the difference. In this intermittent and haphazardly scheduled series, I’ll write about busted ideas that taught me something interesting in their failure, or that simply examine parts of the game that might otherwise escape notice.

In September of this year, I came up with an idea that spent the next month worming its way into my brain. We think of pitch movement as relative to zero, but that’s obviously not true. Sinkers rise more than a spin-less pitch thrown on the same trajectory would; they’re “risers”, in fact. Don’t tell a player that, though, because they’re not comparing these pitches to some meaningless theoretical pitch that no one throws. They’re comparing them to other fastballs, four-seamers to be specific, and if your brain is used to seeing four-seamers, sinkers do indeed sink. Read the rest of this entry »

Last week, I dug up an old concept: expected home run rate. The idea is deceptively simple: assign some probability of a home run to each ball a batter hits in the air, then add them up. It tells you some obvious things — Fernando Tatis Jr. hits a lot of baseballs very hard — and some less obvious things — before getting injured, Aaron Judge had lost some pop.

One question that many readers raised — reasonably so! — is whether this expected home run rate actually means anything. The list of over-performing hitters was full of sluggers. How good is this statistic if it tells you that good home run hitters are, in fact, not as good as their home runs? Sounds like a bunch of nonsense to me.

In search of truth — and, let’s be honest, article topics — I decided to do a little digging. Specifically, I wanted to test three things. First, how stable is expected home run rate? In other words, if a player has a high expected home run rate in a given sample, should we expect them to keep doing it? If the statistic isn’t stable, what’s the point?

Second, how does it do at predicting future home runs? In other words, does an expected home run rate in, say, July predict what will happen the rest of the year? It’s also useful here to see if expected home run rate (from here on in, I’ll be calling this xHR% for brevity) outperforms actual home run rate as a predictor. If xHR% doesn’t do a better job of explaining future home runs than actual home runs, what use is it? Read the rest of this entry »

Last year, I came up with a simple idea: estimate home runs based on exit velocity. That sounds pretty straightforward, and it mostly is. For example, here are your odds of hitting a home run at various exit velocities when you put the ball in the air in 2020:

Of course, some caveats apply. I’m only looking at batted balls between 15 and 45 degrees, and the sample size is still small. But for the most part, and excluding the vagaries of that small sample, the conclusion makes sense. Hit the ball harder, and you’ll find more home runs.

Of course, real life is notoriously fickle. Sometimes you mash the ball and it’s a degree too low, or you hit it to the wrong part of the ballpark, or a gust of wind takes it. Sometimes you play in Yankee Stadium and get a cheapie, or smoke a line drive that leaves a dent in the Green Monster. Sometimes you make perfect contact, and it’s at 15 degrees instead of 25 so it’s a smashed single to right instead of a bat flip highlight.

Wait — hit it at the wrong angle? That seems like something in a batter’s control. It partially is, but I’ve chosen to exclude it for two reasons. First, I’ll point again to this excellent Alex Chamberlain article. You should really read it, but the conclusion is basically this: batters control exit velocity and pitchers control launch angle. That’s not exclusively true, and there are obviously fly ball and groundball hitters, but if you start giving batters credit for the exact angle of their batted balls instead of just generally saying “in the air” or “not,” you might be going too far.

Second, this way is simpler! Simplicity has value. Overspecify a model, and you can get very precise results that are also hard to interpret, or that depend heavily on small fluctuations in initial conditions. That’s not to say that such a model is a bad idea — merely that it’s not strictly upside to add more and more gadgets and whizbangs to it. You also risk losing the signal you’re looking for, which in this case is the ability to absolutely hit the snot out of the ball, sending it skyward at stupid speeds. Read the rest of this entry »

Pinch hitting is hard. Baseball is a rhythm game, and pinch hitters are denied any semblance of routine. They’re on the bench, swinging a bat back and forth to get the blood pumping in their arms, and then just like that, they’re in the game. They might have been daydreaming about what they plan on ordering from room service, and here’s Jacob deGrom throwing 92 mph sliders. Good luck!

That’s the classical conception of a pinch hitter, and it explains why Tom Tango, Mitchel Lichtman, and Andrew Dolphin found a significant pinch hitting penalty in The Book. They found a 24-point wOBA penalty for pinch hitters, which is a large cost. That’s roughly equivalent to the platoon advantage a lefty gets when facing a right-handed pitcher.

That’s a pretty striking difference. When your team gets a lefty batter up against a righty pitcher in a big spot, it feels great. Imagine that pitcher being replaced by a left-hander. Feels pretty awful, right? That’s the same swing in effectiveness you get when a batter pinch hits rather than batting regularly.

You don’t always hear about this effect on broadcasts, because there are other decisions that go into pinch hitting. You’re getting a diminished version of whichever hitter you select, but other advantages can still tip the scales in a batter’s favor. Read the rest of this entry »

Every year, I help write the Fantasy Profiles you see on FanGraphs player pages. One of my assigned players for the 2020 season was Michael Pineda. Pineda is a bit of a mystery. In 2019, his fastball was a unicorn. Nothing in his profile made sense. I decided to investigate, and tweeted out my initial findings:

Pineda's 2019 fastball is a unicorn.

Velo down over 1 mph
Spin down
Bauer units (spin/velo) down
Drop in its vertical break

But
Career-high SwStr%
Career-low GB%

The only change I noticed what that in 2017 and 2019, he started living up in the zone more.

— Jeff Zimmerman (@jeffwzimmerman) January 5, 2020

Here’s a detailed breakdown of the above numbers:

No improved performance indicators stick out quite like higher velocity, greater spin, or a pitcher living in the strike zone more. Sometimes a pitch will improve if it’s thrown less often since batters don’t expect it, but Pineda’s fastball usage jumped. The flashing red lights are with the groundball rate; Pineda’s fastball’s groundball rate was almost halved. Maybe he was throwing higher in the strike zone. Here are his pitch location heat maps over those three seasons. Read the rest of this entry »

One of the joys of baseball, and sports in general, is that the narrative arc of the game isn’t preordained. You can’t know when the most important pitch of the game will be before the game starts. This isn’t a TV procedural, where nothing decisive can happen in the first 20 minutes. The visiting team might go up 3-0 in the first inning and never relinquish the lead, or they might rally furiously from down five only to lose in the bottom of the ninth.

Even though the most exciting pitch of the game isn’t a given, one thing more or less is: the first pitch of a game won’t be the most exciting one. That’s partially due to the rules of baseball — no one is on base, and most at-bats take more than one pitch — but the first pitch is unique in its own way. For one, no one swings. Combining the first pitches thrown by each starter in a game, batters swing at 23% of offerings, significantly lower than the 29% overall swing rate on 0-0 counts.

Secondly, it’s almost always a fastball. Sam Miller delved into the thinking behind game-opening fastballs, and pretty much everything from his piece still holds. Pitchers throw fastballs because batters don’t swing, and batters don’t swing because they already don’t swing much on 0-0, and particularly so when they haven’t seen the pitcher throw anything yet.

But batters aren’t static opponents. In 2010, they swung at 25.1% of 0-0 pitches. In 2019, that number was a meaty 29.4%. Strikeouts are rising, pitchers are fastball-happy on 0-0 counts, and batters increasingly can’t afford to hang around waiting for something to hit given the decline in overall fastball usage. Read the rest of this entry »

How do you describe a batter’s plate discipline? I sometimes struggle with it. I might describe their walk rate and strikeout rate, maybe add in something about how often they swing. I’m never sure how much to weight walk rate and how much to care about strikeouts. How does someone with a 25% strikeout rate and 10% walk rate compare to someone with a 20% strikeout rate and a 7% walk rate?

What about Anthony Rizzo? He gets on base without swinging the bat fairly often, but it doesn’t show up in his walk rate, only in bags of ice and bruises. Getting hit by a pitch is marginally more valuable than a walk if you listen to our linear weights (because walks happen more often when there are bases open, while HBP tend to be random), but it doesn’t show up in the “plate discipline” numbers we’re used to looking at.

I’ve danced around this concept a few times here at FanGraphs. When I wrote about Joey Gallo’s new approach, I touched on how his strikeout and walk rates related to how good he needed to be on contact to succeed. When I wrote about Luis Arraez’s unique talents, I framed his walks and strikeouts in terms of what it meant for the rest of his contact. Behind the scenes, I’ve been using a standardized version of this calculation for quite a while. Today, with no baseball coming to save us, it’s time to explain my method.
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When I first learned about a mysterious cabal of smart nerds who were analyzing baseball, I took the words I got from them as though passed down from heaven. I read Moneyball, of course. But I also read about DIPS theory, wOBA, and whatever else I could get my hands on. I read The Book so many times I wore it out and had to buy a new copy. It felt like there were cheat codes just under the surface of the sport that someone was highlighting for me.

Many of those lessons from 15 years ago are still kicking around in my head. I’m skeptical of BABIP-driven hitters, perhaps more skeptical than I should be. I dismiss batters with anomalous platoon splits, even if there’s something about them that really does make them unique. And recently I realized that I might be misunderstanding the signaling value of strikeout rate.

Back in the early 2000s, batters who struck out more hit better. That sounds counterintuitive, because strikeouts are bad. It’s actually not that weird though. Barry Bonds struck out more than Ozzie Smith in his career, just to pick two illustrative examples. Bonds isn’t even a great example, because his batting eye was otherworldly. Alex Rodriguez struck out twice as often as Omar Vizquel.

The popular opinion was that strikeouts weren’t really a negative indicator. A strikeout was bad, sure, but it was often a hidden indicator of some positive process under the hood. No one would say that being sore is good for your health, and yet people in great shape are probably sore more often than sedentary types, what with all the exercising. Amount of time spent being sore very likely has a positive correlation with health.
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