After winning the Cy Young award in 2015, Dallas Keuchel had a follow-up campaign that really can only be described as disappointing. His ERA ballooned from 2.48 – good for second in the AL that year – to 4.55, 32nd in the AL out of 39 qualified pitchers.
A glance at Keuchel’s peripherals indicates that his fall may not have been as dramatic as it seemed, as his xFIP was a solid 3.53 — the second-best mark of his career, actually, after adjusting for league and park. But a pitcher who sees his ERA rise by over two runs certainly isn’t content to rely on things to right themselves. So Keuchel took matters into his own hands.
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Earlier today, Eno Sarris took a look at the arsenals of tonight’s World Series Game Two starters, Trevor Bauer and Jake Arrieta. In this article, I’m going to hone in on one of those pitches in particular: Bauer’s curveball.
Pitchers want to disguise their pitches. This is a pretty obvious statement – it’s harder for a batter to hit a pitch if he can’t tell what’s coming. So naturally, conventional wisdom dictates that pitchers should try to make every pitch look the same coming out of their hand. You don’t want drastically different mechanics while throwing one type of pitch than while throwing another.
So when Trevor Bauer throws his curveball from a significantly different height than all his other pitches, that stands out. It’s hard to notice on television, but Bauer releases his curve a full six inches higher than all his other pitches.
It’s been over a year now since Sean Dolinar and I published our article(s) on reliability and uncertainty in baseball stats. When we wrote that, we had the intention of running reliability numbers for even more statistics, including pitching statistics, of which we had included none.
That didn’t happen. So a little while ago, when I was practicing honing my Python skills by rewriting our code in, well, Python (it was originally in R), I figured, “Hey, why not go back and do this for a bunch more stats?” That did happen. Sean was/is swamped making the site infinitely better, though, so I was on my own rewriting the code.
In case you need a refresher, never read our original article, and/or don’t want to now, here’s a quick description of reliability and uncertainty: reliability is a coefficient between 0 and 1 that gives a sense of the consistency of a statistic. A higher reliability means that there’s less uncertainty in the measurement. Reliability will go up with a larger sample size, so the reliability for strikeout rate after 100 plate appearances is going to be much lower than the reliability for strikeout rate at 600. Reliability also changes depending on which stat is being measured. Since strikeout rate is obviously a more talent-based stat than hit-by-pitch rate (well, maybe not for everybody), the reliability is going to be higher for strikeouts given two identical samples. You can think of it like strikeouts “stabilize” quicker than hit-by-pitches.
Reliability can be used to regress a player’s stats to the mean and then to create error bars around that, giving a confidence interval of the player’s true talent. To continue with the strikeout example, I’ll add another point — namely that, the more plate appearances a player has recorded, the closer the estimate of his true talent will be to the strikeout rate he’s running at the time. In fact, strikeout rate is so reliable that, after a full season’s worth of plate appearances, a player’s strikeout rate will probably be almost exactly reflective of his true talent. The same cannot be said for many other stats, like line drive rate, which is mostly random; the reliability for LD% never gets very high, even after a full season’s worth of batted balls.
Today at FanGraphs, we’re introducing an interactive run-expectancy tool that incorporates the batter’s skill into the run-expectancy value. The tool, developed by the rather incredible Sean Dolinar, allows the user to input a few factors, including one to account for the batter, and in turn spits out a number estimating how many runs will be scored for the rest of the inning.
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.
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.
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.
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.
Jonah Pemstein and Sean Dolinar co-authored this article.
Due to the math-intensive nature of this research, we have included a supplemental post focused entirely on the math. It will be referenced throughout this post; detailed information and discussion about the research can be found there.
“Small sample size” is a phrase often used throughout the baseball season when analysts and fans alike discuss player’s statistics. Every fan, to some extent, has an idea of what a small sample size is, even if they don’t know it by name: a player who goes 2-for-4 in a game is not a .500 hitter; a reliever who hasn’t allowed a run by April 10 is not a zero-ERA pitcher. Knowing what small sample size means is easy. The question is, though, when do samples stop becoming small and start becoming useful and meaningful?
On Monday, I looked at how different spin rates for different pitches affect the way those pitches move through the air towards a batter. That post was useful for understanding the relationship between spin and velocity and movement. What it didn’t tell us, however, is too much about what the spin actually does for the pitcher: does more spin make pitches harder or easier to make contact with? Does more spin induce weaker contact? To answer those questions (as well as others), we can look at the actual production from hitters on these pitches. That’s the goal of this post.
The first such stat we’ll consider is contact rate (Contact%), or times made contact (balls in play or foul balls) per swing.
My last article was a look at the effects of pitch location on batted balls. While it ended with on somewhat disappointing note, showing that the results couldn’t really be applied to individual pitchers, it did make me think more about which components of a pitch affect the pitch, and in which ways.
So I decided to examine spin. Spin is captured by PITCHf/x in two measurements: rate (in revolutions per minute) and direction (the angle in degrees). As it turns out, the spin of a pitch has quite the effect on its outcome, much like location. Different spin rates make the pitch move differently (obviously) and get hit differently. (For a look at this topic from a physics standpoint, check out this infographic and this much more complicated article, both from the excellent Alan Nathan. And, to make sure everybody knows: I know little about the actual physics of this past what I can infer from my baseball playing and watching experience. I am just looking at the PITCHf/x data.)
Before we get right to the graphs, a quick note about my methodology. I grouped each pitch from 2009 onward — which is the year PITCHf/x started to record spin rate consistently — into buckets based on spin rate (pitches were rounded to the nearest 50 RPM) and pitch type (I included four-seam fastballs, curveballs, changeups, two-seam fastballs, cutters, knuckleballs, and sliders). I then found a multitude of stats for each bucket: contact rate, average speed, average movement, ground ball rate, and many more. I also did the same with spin angle, grouping pitches into buckets by rounding to the nearest 20 degrees, but the results weren’t particularly meaningful.
I also combined two-seam fastballs and sinkers when I was doing this. There has been some discussion in the past about whether there is a difference between those two pitches. While PITCHf/x classifies them separately, they are more or less indistinguishable, and when I first did this without combining them, they overlapped on nearly all of the various graphs.