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 »

Have you ever been to a September game between two teams out of playoff contention? I have, and while I like a nice afternoon in the sun as much as anyone, the lack of excitement in the stadium is contagious. Empty seats are demoralizing to fans who want to root for the team — there’s no one around to echo their cheers, so the cheers start to feel perfunctory. If you went to the game to get the thrill of baseball rather than for a pleasant afternoon, you’re often in for a disappointment.

Of course, that feeling isn’t exclusive to September. Last June 14th, for example, the Pirates took on the Marlins in a Friday night game. Per our playoff odds, the Pirates stood a 1.4% chance of reaching postseason play. The Marlins’ odds rounded to 0%, and we have a lot of decimal places to round to. It was only June, but the two teams were already playing out the string. The crowd of 8,340 filled the stadium to roughly one-quarter capacity.

When pundits talk about baseball’s competition problem, these games are the ones they mean. There are bound to be meaningless games throughout the course of the season: a 162-game schedule leaves plenty of time to separate the wheat from the chaff, and by September many teams are simply wrapping things up. Even then though, games don’t have to be completely meaningless; even if the home team is out of it, an exciting visiting team can provide some motivation to fans.

When the streaking Mets visited the Pirates on August 2, for example, PNC Park drew an above-average number of fans for the Friday night clash, even though our playoff odds gave them a scant 0.1% chance to make the playoffs. There was at least still a reason to attend the game — the Mets were interesting, and there’s some measure of joy to be gained from seeing your club take on a contender, and a vicarious thrill to beating them.

So if you want to get to the heart of what baseball’s competitive balance problem does for interest in the game, look to the games played with no stakes. What exactly no stakes means depends on your philosophical bent, and I’ll go into several variations, but first consider this definition: a game with no stakes is one where neither team falls in the 5%-95% playoff odds range at the start of the game.
Read the rest of this entry »

Here’s a question for you: does Mike Trout hit more home runs against bad pitchers? The answer is yes, of course, but we can parse the question a little differently to make it more interesting. How about this one: does Mike Trout hit more home runs per fly ball against pitchers who are home run-prone? That at least has some intrigue.

Here’s one way you might do this study. Take every pitcher in baseball and group them into quartiles based on their home run per fly ball rate. I’m using line drives and non-pop-up fly balls to make a slightly different rate, but the idea is the same. With the pitchers bucketed like so, simply observe Trout’s home run rate against each quartile:

But before Tom Tango pulls his hair out, let me add something important: This is a bad way to do this study. There’s a big problem here. Trout’s home runs and the pitchers’ home run rate aren’t independent of each other. If Trout tags a guy for a few home runs, that pitcher’s home run rate goes up. If Trout doesn’t hit any out against a pitcher, that pitcher will tend towards the stingiest quartile. Even if Trout’s home runs were randomly distributed across pitchers, this data would tend towards shape. Read the rest of this entry »

Yesterday, I put every fastball thrown in baseball last year into a giant spreadsheet to come up with expected pitch values. Well fine, it was a small snippet of code, not a giant spreadsheet. But the output came in a giant spreadsheet! In any case, the idea is pretty straightforward: look at a player’s pitches, substitute in xwOBA-based contact numbers instead of actual results, and call it a metric.

Today, I’m completing the set. Well, I’m kind of completing the set; I ignored knuckleballs because there aren’t enough of them, and secondary offerings are more complex. Due to differing classification systems, I scraped breaking balls (sliders, curveballs, knuckle curves, and even cutters) and offspeed pitches as a single pitch type. Otherwise, we might end up with something like Nick Anderson — classification systems can’t decide if he throws a curve or a slider.

One more thing: the system is heartless. No human could argue that this wasn’t the best curveball of the year:

Or if not that one, then this one, with bonus Eric Lauer bewilderment and Greinke sprinting:

Slow curves are undoubtedly the best curves, results be damned. But the soulless calculation robot doesn’t agree with me on that, caring about “whether the opposition hit it” and “whether it gets strikes” instead of “whether Ben audibly giggles when the pitch is thrown.” To each their own, I suppose. Read the rest of this entry »