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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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 »

Earlier this offseason, I wrote a few articles about whether pitchers or batters had more influence over different events. There’s nothing groundbreaking about my conclusions — in fact, they specifically reinforce prior studies. Despite that, however, I think there’s value in these refreshers.

Concepts like “batters control home runs” and “pitcher groundball rate matters” are implicit in many of the statistics that you see on this site and certainly in many of the articles that you read here. When we cite xFIP or talk about what a pitcher can do to control his groundball rate, we’re drawing on these concepts.

You don’t need to know these basic concepts to accept the conclusions, but it certainly helps. Appealing to authority (hey, these stats are good because smart people made them) is a pretty bad way to convince someone, and understanding the reason behind a metric is the quickest way to accept its conclusions.

In that spirit, I thought I’d round out the series by looking at a few more common events and working out whether pitchers or batters do more to influence them. Today I’ll be looking at line drive rate and also popup rate, the percentage of fly balls that become harmless popups. Later this week, I’ll cover walks and strikeouts. Then we can move on to more pressing matters, like I don’t know, José Altuve tattoo investigations or what would happen if Mike Trout knew what was coming.

Before looking at line drive rate, I had a rough idea of what to expect. There are plenty of hitters I think of as line drive machines — peak Joey Votto, Miguel Cabrera, even Nick Castellanos. I had trouble placing a pitcher in the same category, unless you count “your favorite team’s fifth starter.” Read the rest of this entry »

The Astros cheated. That’s not in dispute. The search for just how much the banging scheme helped the team, however, is ongoing. Rob Arthur got the party started. Tony Adams chronicled the bangs. Here at FanGraphs, Jake Mailhot examined how much the Astros benefited, which players were helped most, and even how the banging scheme performed in clutch situations. In a recent press conference, owner Jim Crane downplayed the benefit, saying “It’s hard to determine how it impacted the game, if it impacted the game, and that’s where we’re going to leave it.” It’s a rich literature, and not just because it’s fun to write “banging scheme” — but I didn’t want to leave it there.

I thought I’d take a different tack. All of these studies are based on reality, and reality has one huge problem: it’s so maddeningly imprecise. You can’t know if we captured all the right bangs. You can’t know if the system changed, or if it had details or mechanisms we didn’t quite understand or know about. And even when everything is captured right, those sample sizes, those damn sample sizes, are never quite what you need to feel confident in their results.

If we simply ignore what actually happened and create our own world, we can skip all that grubby, confusing reality. Imagine, if you will, a player who makes perfectly average swing decisions and achieves perfectly average results on those decisions.

Let’s further stipulate, while we’re far off into imaginary land, that pitchers attack our perfectly average batter in a perfectly average way. For each count, they’ll throw a league average number of fastballs, and those fastballs will be in the strike zone at — you guessed it — a league average rate. The same is true for all other pitches — with cut fastballs included in “all other pitches” in this analysis. Read the rest of this entry »

Last week, in a bit of a horror story for pitchers, I demonstrated that they have little control when it comes to suppressing HR/FB rate. That’s quite depressing — if you face a big, strapping boy of a hitter, the fly balls aren’t likely to stay in the yard, no matter who you are. It’s enough to make you sad.

But rejoice! Baseball is more than just what percent of fly balls leave the yard. In fact, it’s a lot more than just that. For one, you could just strike people out. It’s hard to hit a home run if you don’t even hit the ball. Short of that, you could just induce a grounder. Unless the aerodynamics of the baseball and also the rules of baseball change markedly, no one’s hitting any home runs on the ground.

Intuitively, pitchers can do a lot more to control groundball rates than home run rates on fly balls. For one, name a pitcher who’s really good at suppressing home runs over a long career. I’m talking really good, not just kind of good. Did you come up with Adam Wainwright, Justin Verlander, and Clayton Kershaw? They’re the three best at it with enough innings pitched for the data to look meaningful, and even then they’re only a few percentage points better than league average.

On the other hand, it’s easy to name groundball pitchers. Zack Britton is the archetypal example, but Marcus Stroman, Dallas Keuchel, Charlie Morton, and plenty of others come to mind as well. Those guys may not do a great job of limiting home runs when opposing batters put the ball in the air, but they limit overall home runs all the same. Read the rest of this entry »