This week at BP has been a celebration of the pitchers with baseball’s best command and consistency. The new metrics we highlighted on Monday (control, as expressed by Called Strike Probability, and command, as expressed by Called Strikes Above Average) capture the degree to which pitchers are able to work the edges of the strike zone and help their catchers frame it. The new metrics rolled out since Tuesday focus on pitch tunnels, and what we’ve learned from them is that there’s a meaningful difference from one pitcher to another when it comes to the ability to disguise pitches.
Repeating one’s release point and throwing consecutive pitches such that they look alike for the first 60 percent of their flight to the plate (with regard to both location and velocity) does seem to be a skill. (We should note that, while a cursory look at the leaderboards will tell you it’s a valuable tool, we haven’t yet developed any stat that assigns value to this skill. For now, we’re just speaking qualitatively about the way pitchers pitch.)
The best tunnelers are interesting and there’s a lot of good work left to do with them. Both ends of a leaderboard can be of interest and I think I found something worth noting at the bottom of one of the new ones we put up this week. We’re going to talk today about Raisel Iglesias and Zach Duke, the two guys who accomplish the same thing the elite tunnelers do, but in a completely different way. These guys aren’t tunnelers. They’re avalanche artists.
In 2016, 280 pitchers threw at least 700 pitch pairs that we can use to analyze their tunneling performances. If you sort those 280 hurlers according to the average differentials in their release points across all those paired pitches, the top of the leaderboard reads:
We might expect better pitchers, in general, to repeat their delivery better, but then again we’re still learning this. On the whole, the pitchers near the top of the list are better than the ones further down. Madison Bumgarner, Kyle Hendricks, Stephen Strasburg, Corey Kluber, Jacob deGrom, Jose Quintana, and Gerrit Cole all join Lester among the 25 best repeaters of release points.
Notably, the margins between pitchers are very small. Cole is 25th on the list with a 0.1400 average release differential. Wily Peralta ranks 69th at .1587. Scott Kazmir is 100th at 0.1718. Braden Shipley, the 200th name on the list, checks in at 0.2257. Johnny Cueto and Clayton Kershaw, those wily occasional droppers-down, rank side by side in the 230s at just over 0.2500. All the way down at the bottom of the list, Brad Ziegler ranks 260th with a 0.2861 release differential. Rich Hill is 274th, at 0.3453. John Lackey seems extreme, at 0.3834, and is easily the lowest starter on the list.
Here are the final four names:
That’s radical. Iglesias and Duke play a different game on the mound. Good tunnelers want you to be unable, no matter how hard you strain, to pick up the difference between a cutter and a sinker before you must make your reflexive choice and swing. Those pitchers invite you to think like an assassin: aim small, miss small. They intend to frustrate that effort and to take advantage of your aggressiveness.
Iglesias and Duke, though, change arm angles even on back-to-back pitches with shocking regularity. The effect is to force the batter to widen his field of vision, delay his pickup of the ball itself, and force his eyes to narrow their focus quickly with the pitch already in flight. In so doing, they make the batter less like a sniper than like an agent scanning the crowd for the culprit. That puts the batter on his heels. He’s not playing “Big Buck Hunter” anymore. He’s playing “Whack-A-Mole."
One of the delightful things about the discovery of catcher framing (and not only the discovery of it, but the degree to which it turned out to be a vital part of baseball as we know it) was that it didn’t represent a new thing about which old-school baseball people and stat-heads could bicker. To the contrary, it affirmed something baseball people had been saying for years: that the strike zone was a fluid thing, shaped and reshaped by each combination of pitcher, catcher, batter, umpire, and count. Our newfangled numbers burped up one clear point on which, while still speaking a different language, Brooklyn Dodgers fans and Arizona Diamondbacks fans could agree.
The fun thing about tunneling data is that it’s another such area. Hopefully, you’ve already read about Greg Maddux’s vaguely gross “column of milk” imagery. Check out this video of Rick Ankiel, talking about the way Zach Duke attacks hitters and why it works:
This is 90 seconds on tunneling. Ankiel’s description of the way hitters key on a slot, then seek the variations within that slot in order to identify pitches, makes clear how valuable it can be for a pitcher to repeat his release point so well that even the minute changes for which the hitter is looking are imperceptible. It also makes clear why the things Iglesias and Duke do work so well. Hitting is a defensive art. We already know that the hitter isn’t going to see the ball for the last few feet of the ball’s flight. If he also can’t see the ball for the first few feet, because he doesn’t know where to look for it, he’s losing crucial information.
That varying one’s release point is an effective way to maximize deception is conventional wisdom. It’s nothing new. It’s always been hard to study, though, for anyone with less than eight hours a day they could dedicate to poring over charts on Brooks Baseball. (Hi, Harry.) We’ve had scatter plots of release points, color-coded by pitch type, for individual at-bats, starts, months, seasons. But until now we haven’t had a way to contextualize what we were seeing.
What constituted a wide band of release-point variation? To what extent is doing so a skill? Which pitchers do it most often, and how wide is the spread in frequency among the population of pitchers? Perhaps most importantly (and the hardest to suss out from the raw data): how often did a pitcher change his release point radically from one pitch to the next, instead of merely within an inning or an outing?
This data gives us that power. We know that Tony Watson hardly ever gives himself away to the opposing hitter at release, which is one reason why he’s been an effective reliever for several years without the top-end velocity or wicked breaking ball usually required for bullpen lefties. We also know that two pitchers—Iglesias and Duke—stand out from the crowd by taking away batters’ ability to spot the ball out of the hand. It’s practically possible, for the first time, to tell who is really making tangible, consistent, unpredictable use of the best weapon in a junkballer’s arsenal.
Since we’re here, and from the “research I’m not prepared to undertake” department, there are a couple of other things into which we should dig when it comes to this data set.
- Iglesias and Duke are former starters, pushed to the bullpen by a mixture of injury and ineffectiveness. In the video above, Ankiel mentions that Duke gets away with his deception better as a reliever than he could in the rotation. There’s something to that. It’s perfectly possible, and in fact even probable, that varying release points as much as those two do simply poses more problems than it solves for any starting pitcher.
- One of the problems extreme release-point variation might pose is a health risk. I would not be surprised to find that a lower average release point differential implies lower overall injury risk. It probably means you are rarely pitching tired, that you have clean mechanics, and that you’ve conditioned your body to get even small muscle movements right over and over again.
- I’m almost sure that, whenever we get a handle on all of this, we’re going to find a relationship between tunneling (and maybe release point, specifically) and CSAA. For catchers, we know that framing is largely about keeping the head up, the glove quiet, and any movements small. That’s hard to do if you’re watching your batterymate release each pitch in almost precisely the same spot, but (even if you know which arm angle is coming) quite a bit harder if you’re guessing along with everyone else.
I’m sure there’s a limit to the amount of fascinating fodder for study this week’s pitching stats can yield—a bottom to this barrel. We’ve got a whole of monkey arms to link together before we find that bottom, though.