Archive for Research

Deception seems impossible to quantify. How are you going to put a number on something like the Invisiball thrown by Yusmeiro Petit? The ball is there, and then it isn’t, and then it’s back! Put that in your number machine, nerd boy.

Except, hidden in that description of the Invisiball is a possible guide. The release point. The release point is a huge moment for the hitter — it can tell him about the type of pitch and type of movement he’s about to see even before the ball is released.

Think of Brad Ziegler. You see that arm dragging along the ground, and you know the ball is going to move down and away from you. You adjust. Think of Darren O’Day. You see that arm slot, and you think the ball will drop, and when it doesn’t, you miss his Submarine Riseball. Tyler Clippard! You see that over the top and you think the ball will rise — and his fastball does, but not his changeup.

So maybe deception is deviation from the expected movement given a pitcher’s arm slot. Or at least that’s maybe one sort of deception. It probably won’t help us understand Petit’s Invisiball any better, but it may give us something with which to work on this hard-to-define aspect of pitching.

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If you’ve recently spent time with other humans, it’s likely that you noticed that they tend to be overconfident about how well they understand the world around them. Think of all of the people you know who have tried to weasel their way out of admitting they were wrong even when presented with strong evidence that they had misinterpreted a situation. Humans are bold and unapologetic in their declarations and do not like it when you point out that they’ve made a serious error.

It’s hard to criticize people for that when it seems to be a pretty fundamental aspect of the species. It’s not good or bad, it simply is. But that doesn’t mean we can’t enjoy little moments when someone makes a compelling argument and then the world totally destroys their hard work by changing around them.

For example, two political scientists once wrote a book called Congress’ Permanent Minority? Republicans in the U.S. House which was the first major scholarly account of how a minority party operates when it expects to be in the minority for the foreseeable future. It’s a well-researched book and was well reviewed when it came out. Unfortunately for the authors, it came out in January of 1994, just 11 months before the Republicans would win control of the House for the first time in 40 years. It was a perfectly fine analysis, it was just totally detached from the reality of American politics almost immediately.

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The first time Dallas Keuchel broke out, it was because he found a new pitch. Moving from the curve to the slider really pulled his arsenal together. The second time he broke out, it was for reasons that were both more complicated but also just as conventional — it looks like Keuchel merely threw fewer pitches inside the zone, while getting batters to reach and swing just as much. More swings on pitches outside of the zone means more misses and more strikeouts.

All of that is nice, but it’s hard to know which breakout is easier to believe. Our intuition probably tells us that the first is less delicate — he needed a breaking ball, and he found a good one, and it should remain good. But batters could adjust more easily to the second one, couldn’t they? Just lay off more of those balls?

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Pitch sequencing, in my opinion, is the next big thing in the field of baseball research, and despite what Samsung might like to tell you, it isn’t here yet. There has been some tremendous work done, but we’re still a long ways away from aggregating findings into one clearly defined picture of how pitch sequencing exactly works.

But we might as well continue to add to the findings. I looked at one aspect of pitch sequencing – shifts in the called strike zone – last month. Next, I’m looking at how best to set up different types of pitches. We’ll start with four-seam fastballs, and, so as to keep it simple for now, focus just on the fastball and on the pitch immediately beforehand. Not pitches before that in the same at-bat, not pitches to the same batter earlier in the game, not pitches to that batter from a different game.

Intuitively, you might expect changing speeds on the batter to be an effective way to mess with their swing and timing. A changeup, then, should be a good pitch to set up a fastball – changeups are generally 10-plus mph slower than the same pitcher’s fastball. Curveballs, too, should be decent setup pitches, as should sliders to a lesser extent. (Sliders are usually thrown harder than curves.) As it turns out, though, it doesn’t quite work that way.

Contact% = Foul balls + balls in play per swing

There’s some year-to-year variation, but, by and large, changeups are ineffective ways to get swings and misses on the fastballs which follow them. Now, bear in mind, the scale here isn’t so large – it’s a few percentage points each way. But it’s still pretty clear that changeups, as well as curveballs, don’t help the pitcher throw a better fastball the next pitch.

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My role here at FanGraphs is to write about minor league players. Nearly all of my articles focus on the output from my KATOH projection system, which produces long-term forecasts for players who are still in the minor league phase of their careers. Today, I’m unveiling some updates to my model that will be reflected in my analysis from this point forward.

I’ve been meaning to work these updates into KATOH for quite some time now, but haven’t had the chance to finish up until now. Some pieces of this took a bit longer than expected, and day job stuff along with this year’s onslaught of prospect debuts pushed things to the backseat a bit. But I’m all caught up now and ready to unveil my new and improved KATOH. Here we go!

Rather than just putting out a straight leaderboard, I thought I’d use this as an opportunity to explain some of the inner workings of KATOH. I wanted to say something more insightful than “These are the best prospects because math.” That’s why this piece runs 2,000+ words without reference to a single baseball player. If you’re just interested in the output rather than the nitty-gritty, check back after Thanksgiving for KATOH’s top 100 list. I just wanted to get all of this background stuff down in one place, rather than cluttering future pieces with extra information.

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Obligatory Technical Details

The general framework of my model is largely the same as it’s always been. As I did in the past, I deployed a series of probit regressions to see what factors are most predictive of major league performance. For each player, I generated probabilities that he would achieve certain benchmarks through his age-28 season: play in the major leagues, earn at least 1 WAR, earn at least 2 WAR, etc. These percentages gave me a probabilistic outlook for each player, and enabled me calculate an “expected value” for his WAR through age 28.

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The Reds rumors are coming in hot and fast, or at least hot and fast for the weeks right before Thanksgiving, when most of baseball seems to be watching football. Mostly, it’s the obvious scuttlebutt: Aroldis Chapman and Brandon Phillips are available, for packages of varying quality, and mostly for players close to the major leagues.

Depending on what you think of the Reds and their current competitiveness, you’ll read “listening to any and all offers” differently. If you like the Reds’ young starting rotation, you think you might sell anything that’s not nailed down for 2017, meaning the focus is on jettisoning Jay Bruce and getting a haul for the Cuban closer. If you think there’s no hope and the division is too awesome for the Reds, you think they should probably trade Todd Frazier, and maybe even Joey Votto.

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A while back, Jeff wrote this article on an Edinson Volquez pitch to Jose Bautista in the ALDS, and a commenter left this comment:

This is a good comment. I like this comment. I decided to investigate this comment. And, as it turns out, StroShow was spot on.

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During the fifth inning of Game Two, as Alcides Escobar attempted to bunt against Jacob deGrom, Harold Reynolds decried the current state of bunting in professional baseball. Even after he admitted that hard throwers are hard to bunt on, he went on a mini tirade: “I know it’s hard to get one down against a guy that throws this hard, but I’ve never seen this bad of bunting. Ever. Ever! In baseball, just across the board. I know Escobar can handle the bat, but we see this every night. They have to move the runner. Not just the Royals, but across the board.”

The moment was probably lost for a couple reasons. For one, Escobar decided to swing-away after two failed bunt attempts, and promptly tied the game with a single to center. There was too much excitement to think too deeply about the state of bunting in our game. Now we have a second to breathe, though.

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Hitters, we generally accept, are capable of controlling their balls in play (BIP) to some degree. They don’t have complete control — for example, BABIP is a much less reliable statistic than strikeout rate in the absence of huge samples — but when we see a batter with a high BABIP it’s less suspicious than it would be if that were a pitcher.

Interestingly enough, the year-to-year correlation for BABIP for hitters is quite low. The r-squared is just 0.08 (with 1 being a perfect 1:1 relationship and 0 being no relationship), even when weighting by the number of balls in play in both years. There isn’t quite a total lack of a relationship: the model’s p-value — that is, the measure of the probability that input variables have no effect on the output — is effectively 0, indicating that there almost certainly is a relationship. But knowing a hitter’s BABIP one year doesn’t tell us all that much about what it will be the next.

In graphical format, it’s easy to see the existing-but-not-very-strong relationship between a hitter’s BABIP one year and his BABIP the year after. (The size of the dots in this graph reflect the total number of balls in play the hitter had in the two years.)

I’ve always wondered, though, if batters have any ability to control things on a more granular level than this. For example, do hitters have a lot of influence over whether their ground balls turn into hits? Maybe something like BABIP on ground balls is pretty stable from year to year, and the rest of the hitter’s BABIP is just pure luck from his other kinds of batted balls. Or maybe the three are all separate skills over which the batters have a good degree of control, and the instability comes from a hitter having a down year in one category but a good year in the others.

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While working on something Erasmo Ramirez said — that his slider was always in the zone anyway, so he should probably use it to steal strikes rather than for swinging strikes — it became obvious that breaking pitches are much less effective in the zone than out when it comes to swinging strikes. Curves, in particular, are much better outside the zone. You get about one third of the whiffs on a curve in the zone as you do outside of the zone.

Separately, I’m working on a piece for The Hardball Times Annual about command. In it, a few pitchers talk about the difficulty of commanding breaking pitches. “Nobody throws anything that’s truly straight,” is how Trevor Bauer put it.

While sorting the in and out of zone whiff rates, and thinking about command, it came to me that the two are related. Maybe that’s a duh, but a big part of quantifying command is the problem of breaking balls and changeups and their movement. A breaking ball in the zone may often be a hung breaking ball, which contributes to the lower whiff rates.

Let’s take a look at the pitchers that have the most disparate results on their non fastballs inside and outside the zone first, and then try to find a way to spot these pitchers by movement.

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