Which Pitcher Stats Have Relevance This Early (With a Note on Clayton Kershaw)
It’s very frustrating to do baseball analysis in the offseason — there’s no actual baseball to analyze! It’s very frustrating to do baseball analysis during spring training — the results don’t matter and we don’t get all the same stats! It’s very frustrating to do baseball analysis in the first few weeks — it’s all a small sample size! The lesson overall? It’s very frustrating to do baseball analysis.
But it’s also very rewarding, and so we make a go of it even when we’ve barely completed 10 games of a 162-game season. One thing to which we turn at this point of the season is pitch velocity and movement. My personal sense is that these things become meaningful quickly. Very quickly.
While there’s research that has pushed me in that direction, I hadn’t seen work that looked at precisely how quickly movement and velocity stats stabilize, or become meaningful. So I asked Brian Cartwright to run the numbers.
First, what Cartwright did was to select players who had at reached a certain sample threshold for a metric. For each, he then selected a random number of events at each step (20, 40, 60…) then cut the sample in half and compared the two halves. This process ensured that, at each step, the exact same players, each with the exact same sample size, were used in the comparison. Then he summed the results for all players, and used a Pearson test to find correlations between each cumulative half. These are random samples, so he then reran each test 20 times before returning to the mean.
What you see below is the threshold for each stat at which the stat itself predicts more of the player’s future than the league average (the r-squared is over .5). In other words, the relevant stat becomes meaningful for future projections. It’s not immutable, it may change in many cases, but it’s stable and believable.
| Stat | Denominator | Stable At |
|---|---|---|
| Sinker Velocity | Sinkers | 10 |
| Sinker Horizontal Move | Sinkers | 10 |
| Sinker Vertical Move | Sinkers | 10 |
| Changeup Velocity | Changeups | 10 |
| Changeup Horizontal Move | Changeups | 10 |
| Changeup Vertical Move | Changeups | 10 |
| Contact% | Swings | 40 |
| Changeup Contact% | Changeup swings | 50 |
| Sinker Contact% | Sinker swings | 70 |
| O-Zone Swing% | Pitches outside of zone | 120 |
| First Strike | First pitches | 250 |
| Zone% | Pitches | 330 |
There are, of course, more than a few caveats with this sort of work. The spread on velocity stats — on the actual, most seen velocity — goes from 88 to 95 or so. A half-tick is meaningful, and so narrowly missing the mark, predictively, might be more meaningful with velocity and movement than it would be with other stats.
There’s no guarantee, of course, that a pitcher is certain to maintain the same sinker velocity or contact rate after reaching the sample size at which those two metrics become stable. Rather, what these thresholds signify are points at which the numbers produced by that pitcher so far describe more of the variance going forward than league-average numbers would. In other words, these stats have become meaningful at these (rounded to the nearest ten) benchmarks. For analysis, I treat it as a simple nudge towards the types of stats we can look at in smaller samples, not some sort of bible.
It’s also worth mentioning that different methods have produced different results. Jonah Pemstein looked at some of the stats above and found many similar results for velocity and movement and spin rate. But he also found, for example, that pitcher swinging-strike rate stabilized around 440 pitches, which would be closer to 200 swings rather than the 40 swings we found. Why is one different than another? I don’t know why, go ask my brother — or, in this case, my cousin Brian.
Also: wow. Jeff Zimmerman once found that one game of velocity readings was meaningful for starting pitchers returning from the disabled list, so maybe we already knew this, but it’s really remarkable to see it in this format. After five in-game sinkers, you can predict more than half of the variance in the remaining sample of sinkers from that player. Five fastballs, and you have a good sense of how hard a pitcher is throwing.
Cartwright wanted to point out that it technically even reaches the stable level after one pitch, but I was free to round. That’s probably a good idea, if just to make sure you were looking at five sinkers and you didn’t mistakenly look at a hard changeup in the group.
But when Jeff Sullivan reports that Shelby Miller has added more than two ticks, or that Jake Arrieta is down almost three ticks, that stuff is meaningful. Tyler Skaggs is down almost 2 mph, Dan Straily is up a tick-plus. Lance Lynn is down one to two ticks. It’s not the sexiest content, it might get repetitive, but it’s meaningful and backed by the data.
Context is important, of course. You want to compare like velocities, not velocities from two different sources. You want to make sure the pitcher hasn’t changed roles. You want to make sure he hasn’t just hit the disabled list.
The movement numbers are a little messed up right now, but our best minds are at work recalibrating the different parks for the new pitch-tracking equipment, but perhaps you can expect a post on the most improved changeup of the early going, considering that — calibration issues aside — the actual movement numbers become meaningful super early.
But look at contact rate. That might surprise you. A typical pitcher would elicit more than 42 swings in two starts. In fact, all 85 qualified starters have gotten more than 42 swings on their pitches so far.
| Name | 2017 Contact% | 2016 Contact% | Difference |
|---|---|---|---|
| Clayton Kershaw | 82.6% | 70.2% | 12.4% |
| Jeremy Hellickson | 88.1% | 77.2% | 10.9% |
| Corey Kluber | 83.2% | 73.9% | 9.3% |
| Jhoulys Chacin | 89.6% | 81.2% | 8.4% |
| Patrick Corbin | 87.1% | 78.8% | 8.3% |
| Cole Hamels | 81.9% | 74.7% | 7.2% |
| Yu Darvish | 80.3% | 73.4% | 6.9% |
| Jon Gray | 81.7% | 75.1% | 6.6% |
| Justin Verlander | 81.0% | 75.9% | 5.1% |
| Madison Bumgarner | 80.6% | 75.9% | 4.7% |
| Kendall Graveman | 77.3% | 83.4% | -6.1% |
| Tyler Anderson | 71.7% | 78.7% | -7.0% |
| Alex Cobb | 76.6% | 83.7% | -7.1% |
| Derek Holland | 75.0% | 83.8% | -8.8% |
| Miguel Gonzalez | 74.5% | 83.5% | -9.0% |
| Jeff Samardzija | 71.0% | 80.9% | -9.9% |
| Masahiro Tanaka | 67.9% | 78.3% | -10.4% |
| Danny Duffy | 63.6% | 74.9% | -11.3% |
| Brandon Finnegan | 66.7% | 78.7% | -12.0% |
| Sean Manaea | 60.9% | 77.1% | -16.2% |
Now it’s my duty to tell you that Clayton Kershaw has had the biggest downturn in contact rate (+12 points) among qualified starting pitchers in baseball this year. He hasn’t lost any velocity, but it’s notable that his slider has lost more than three inches of drop relative to his four-seamer, a relationship which should remain accurate even with the the early-season calibration issues.
It seems too early to take this to the bank, especially with the differences in the results surrounding contact rate. Let’s all watch his next start. And those by Jeremy Hellickson (+11 points) and Corey Kluber (+9), too. We’ve already pointed out how great Sean Manaea (tops with -16 points) and Brandon Finnegan (-12) have looked, but next on the list are Danny Duffy (-11) and Masahiro Tanaka (-10). Interesting deviations from their norm, in somewhat believable samples.
Maybe this isn’t so frustrating, after all. There are a few things to explore, at least.
With a phone full of pictures of pitchers' fingers, strange beers, and his two toddler sons, Eno Sarris can be found at the ballpark or a brewery most days. Read him here, writing about the A's or Giants at The Athletic, or about beer at October. Follow him on Twitter @enosarris if you can handle the sandwiches and inanity.
This is like eating peanut M&Ms, thank you.
They get stuck in my teeth.
*braces
Seeing as this doesn’t adjust for park factors or quality of opponents, I think this is a little premature to draw literally any conclusions on the raw numbers. For example, Kershaw has had 50% of his starts in Coors, whereas in 2016 he had 0%. It would also explain the reduced movement on the slider.
v good point about the drop on the slider in Coors! I’ve seen v much reduced drop on curves, so it would follow that the same is true of slider. *Stephen A voice* however, his drop was also half as much as usual in his home start: http://www.brooksbaseball.net/velo.php?player=477132&b_hand=-1&gFilt=&pFilt=FA|SI|FC|CU|SL|CS|KN|CH|FS|SB&time=game&minmax=ci&var=pfx_z&s_type=2&startDate=01/01/2017&endDate=01/01/2018
It’s no secret kershaws been using his changeup more often this year, yet pitchfx doesn’t seem to recognize that. Could it be grouping the changeup and slider into the slider category, thus skewing the data?
I’m not sure it follows that reduced drop on curves at Coors ought to meaningfully translate into reduced drop on sliders at Coors. Dan Rozenson found that sliders are especially resistent to the Coors effect and the curveballs are much more vulnerable: http://www.baseballprospectus.com/article.php?articleid=20069
“There is strong evidence that the slider performs in absolute and comparative terms better than the curveball in Coors Field. Part of this can probably be attributed to the fact that sliders deviate from the “gyroball” trajectory of a pitch thrown in a vacuum the least of the major pitch types.”
Kershaw has pitched half his games (1 out of 2 so far) at Coors field which limits breaking ball movement and thus increases contact rate. So while the first half of the article is interesting it’s way too early to draw conclusions on specific pitchers (I know you said that but it bears repeating).
Edit: Drats, hadn’t read ericdykstra’s comment.
Shelby Miller – 1st start: 95.7mph, 2nd start: 93.9mph. Granted, last start was a cold, rainy night in SF but still, the velo wasn’t there.
I saw that for Matt Moore, too. It’s a bit of a flaw in the analysis: it only has to predict 50+% of the variance going forward to be *right*
Very interesting analysis. However, I am wondering how useful contact % is as a statistic. Tanaka and Finnegan both show improvement in contact %, but at the cost of giving up a lot of BBs. It lets me wonder how much is the change driven by decrease in strikes. That being said, their Z-contact and O-contact both improved, so the improvement could be real.
Strike pct combined with contact rate are very good estimators of walks and strike out rates. What you’ve observed is that allowing less contact means going into deeper counts which increases the walk rate as well. For guys who pound the strike zone this isn’t much of an issue, but less contact can balloon the walk rates of a pitcher with less than average control. Check out this piece I wrote a while ago http://www.baseballprospectus.com/article.php?articleid=9227
Someone I haven’t heard about yet this season is Jimmy Nelson. Maybe it is because he has shown flashes in the past, only to disappoint. Through 2 starts, he is showing a 1.9 MPH increase in 4-seam fastball velocity. His Z-contact has gone down by 7.1% while his zone % has gone up by 7.8%. So he is throwing more pitches in the zone while hitters are making less contact. That sounds like a good recipe to me.
I’d argue that for this analysis you either have to toss Kershaw’s start at Coors Field, or compare his contact% to his other Coors Field starts, because his career splits there suggest he’s a markedly different pitcher at Coors than anywhere else.
For his career, Kershaw’s slash against is .202/.263/.303 but at Coors its .261/.318/.418 and ERA (since BP doesn’t track FIP by park) 2.38 vs 4.71……which is all to say he’s a materially different guy at Coors, so much so, that it’s probably safe to say (since I can’t find the data) that his career contact% at Coors is likely markedly higher than his career% overall.
Eno, firstly, I didn’t understand the reference to “Brian” above, but it made me think you were referring to Brian Eno. I don’t have anything else to say about that.
Secondly, you mention Kershaw’s slider “drop”, but of course his slider has never “dropped”, it has only done what a “sinker” does, which is rise less than the typical fastball. Since Kershaw has a great rising fastball, his slider “drops” a lot by comparison without dropping. However, I would think Coors would make it drop more, by making it rise less.
Also, the numbers on the fangraphs embedded PitchFX list show his slider dropping almost as much as normal, within an inch of normal, that being 4.5 inches rise. Brooks baseball showed his slider “rising” 8 inches though, which is crazy, something I have never seen before. Two questions: Is it meaningful that his FG PitchFX numbers are mostly normal, and why is Brooks reporting something so different?
Movement numbers are all messed up because of the new system. I was trying to define slider ‘drop’ vis a vis the fastball to sort of recalibrate for the new movement numbers. I see a smaller difference between the fastball and slider this year (in both starts) than last year.
Also, I have to admit I was looking totally at the wrong thing, and now I see the fangraphs chart also shows about 9 inches of rise on his slider. However, it also says he has thrown more sliders than fastballs so far. He has reduced his fastball rate every year, but I don’t think he is particularly close to throwing more sliders than fastballs. It signals to me there is a definite classification issue going on, with both fastballs and changeups likely being called sliders, which is interesting in itself, since Kershaw has generally been one of the easiest pitchers to classify. This will substantially raise his “rise” numbers on his slider, because both his fastball and changeup rise a whole lot.
“Brian” was referring to me. However, my cousin Eno was named after Brian Eno.
This is very interesting. Thanks Eno!
I do want to point out something that has been bothering me for a while and that the baseball statistics community needs to confront: the idea that just because you can get a .5 R-squared between two sets of the same thing, that you can truly find “stabilization,” which people are interpreting to mean a pitcher’s true skill having been discovered.
I think this is wrong for (at least) two reasons. First, it treats a .5 R-squared as a fixed point estimate, when in fact it is just a midpoint with LOTS of probable error around it. Second, it presumes that ALL variation not explained by the statistic at hand, is (a) random, (b) otherwise unexplainable, and (c) occurring at a league average rate. That’s quite a series of assumptions and I haven’t seen a defensible basis given for them. (I don’t think I will either; it’s just not reasonable).
Certainly the methods outlined here (thanks Eno and Brian!) give you good reason to zero in on these aspects once a certain number of these types of pitches have been thrown. But I think references to the concept of stabilization as a reference point for future performance have gotten way out of hand. I say this not to be critical, but to say that we all (myself included, probably) need to find a better way to describe these phenomena when we see them.
Happy Friday everyone.
I appreciate these concerns! I try to use it merely as a guidepost to what numbers are *at all* meaningful. I don’t want to look at a walk rate right now, but maybe after a couple starts I could take a glance at zone percentage to see if they’ve changed their approach wrt the zone, perhaps? Doesn’t mean they can’t change again, but it’s more meaningful looking backwards, maybe, then saying player A walked six guys in the last two starts.
Absolutely agree, and I like I said, I enjoyed the article. Thanks for writing it.
Agree wholeheartedly. The rates are useful for determining which stats are easier to predict than others, but the so-called stabilization point is a complete misnomer.
It would be interesting to see a 3-D version of the first table based on different (greater) r-squared values.
I noticed Kershaw as well. Note he pitched at Coors in one of these starts, explains some of the movement changes.
ok, something weird is going on. I know Kershaw threw 100 pitches yesterday because everyone was talking about him maybe throwing a Maddux, and when he missed it. Brooks is only reporting 95 pitches though. if they changed the system, it seems to have some kinks in it still.
Good news for Kershaw in terms of the break difference last night; his fastball rose 0.38 in. more then in his last start and the slider fell 1.32 in. more then in his last start. So the difference in break increased 1.70 in. to 6.17 in. That compares to a September 24, 2016 to October 22, 2016 average difference of 6.27 in.
H/t: Brooks Baseball