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.
Read the rest of this entry »

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.
Read the rest of this entry »

Back in November, I wrote a piece on spin mirroring in which I broke down the phenomenon and its applications, along with theories on its effectiveness. There have been some misconceptions about how spin mirroring actually works. I’m going to attempt to break down how to create “true” (or parallel) spin mirroring, which is based on much more than just opposite spin directions. Spin direction, spin axis, tunneling, and “seeing” spin are all factors that make up this phenomena.

The premise of the strategy is based on a hitter’s potential to recognize spin and the pitcher having the ability to tunnel two pitches, which can create a repelling effect in terms of opposing Magnus force. This juxtaposing effect can create a large spread ratio between the tunnel point and the position of the pitches when they cross home plate. So long as the spin direction contrast is somewhere between 170 and 190-degrees, and their gyro degrees (where the spin axis is pointed in space) are similar, true spin mirroring can be facilitated.

The below example shows how spin direction and the spin axis of two pitches are affected by the contrary Magnus effect (as well as gravity), which creates the appearance of them almost pushing off from each other. There is no additional force from the balls themselves acting on each other; it’s simply how each pitch, individually, responds to this law of physics:

One thing I want to point out as we dive into this is that the Driveline EDGE tool I’ll be using doesn’t account for gravity, drag, or the effect seam orientation might have on ball flight, as well as any park factors like air pressure. These are provided to add visual context to reinforce my statements. That isn’t to say the tool doesn’t have uses otherwise; it relies more on the movement the pitcher is able to generate by himself, which is elaborated on here. Read the rest of this entry »

I’d like to show you a graph. It’s not a surprising graph, nor a shocking one. Here’s the production on batted balls across all hitters in 2019, grouped by launch angle:

It’s not exactly rocket science. Hitting the ball straight down is death, hitting the ball straight up is just as bad, and most of the juice comes in line drives and fly balls that don’t approach popup status. There’s even a cute little dimple right around 15 degrees, where the ball has too much loft to be a flare but not enough that you’re all that likely to hit a home run. That all seems to make sense.

Next, let’s complicate it slightly. Here’s the same graph, only with batted balls hit less than 95 mph excluded: Read the rest of this entry »

Last week, when giving our playoff odds a quick once-over, I stumbled across something interesting. In translating from player statistics to our projections, we strip out the impact of reliever leverage. That seems intuitively weird, so I wanted to delve into the thinking behind it and see if I could find a workaround.

First, a quick recap of the issue. When we calculate WAR for relievers, we include the impact of leverage. This makes sense — the last reliever off the bench is mostly pitching in blowouts, so their contribution, good or bad, is less important than the closer’s. If you used a dominant reliever in a mop-up role, they’d be far less valuable than if they got to pitch in games where the outcome was uncertain.

How do we adjust for leverage? It’s reasonably straightforward. Take a reliever’s gmLI, which you can find in the Win Probability section. Kirby Yates, for example, had a gmLI of 2.16 last year. gmLI is the average leverage index when a pitcher enters the game. You can find a recap of leverage index here, but it’s essentially a measure of how important a given plate appearance is. A leverage index of 1 means that the situation is exactly as important as the average plate appearance, 2 means the situation is twice as important, and so on. Read the rest of this entry »

This is the last of a set of articles I’ve written over the past few weeks. Each one tries to determine what’s real and what’s noise when it comes to the outcome of a plate appearance. For the batted ball articles, the conclusions generally tracked. Variations in home run rate are largely due to the batter. Pitchers and batters both show skill in groundball rate. And line drives and popups are somewhere in between — batters exhibit a little more persistence in variation than pitchers, though neither does so strongly.

Strikeouts and walks are a different beast. It’s pretty clear that pitchers and batters can be good or bad at them. No one looks at Chris Davis or Tyler O’Neill and thinks “eh, that’s pretty unlucky to have all those strikeouts, I bet they’re average at it overall.” Likewise, Josh Hader isn’t just preternaturally lucky — he’s good at striking batters out.

So rather than attempt to prove that pitchers can be good or bad at striking out batters and vice versa, I’m interested in whether one side has the upper hand. I’m adapting a method laid out by Tom Tango here, but I’ll also repeat the same methodology I used in the previous pieces in this series. Read the rest of this entry »