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The end of tunnel vision?

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How new sequencing data could usher in an era of individuality on the mound

World Series - Cleveland Indians v Chicago Cubs - Game Four Photo by Ezra Shaw/Getty Images

Rich Hill looks, for all the world, like an outlier. With his inside-out usage patterns, comic-book pitch face and goofy exaltations, you could be forgiven for assuming that his movie-montage dominance is just a blip, that he is simply marching through the baseball landscape to the beat of his own drummer.

Baseball Prospectus recently released landmark research on tunneling, the practice of delivering sequential pitches in a way that makes one appear similar to the next. This hampers or misdirects a hitter’s decision-making process, leading to bad contact or swinging strikes. The work essentially confirmed a long-standing concept exemplified in the performance of Greg Maddux (among others), and carried it into the information age by devising forms of measurement.

For our purposes here, we’ll be focusing on the data relating to the early part of a ball’s flight, from release to the “tunnel point” where a hitter has to make the decision to swing, or not.

And this new data at our fingertips is already spitting out insights about hurlers past and present, including the ones successfully channeling Maddux, like Jon Lester and Madison Bumgarner and Kyle Hendricks. They’re practicing judo in the two-man boxing match that sits at the heart of the game.

Not unlike BP’s catcher framing work, these metrics provide a new way of understanding an old baseball truth. But this is different in that the information doesn’t scale anything or compare anything or attempt to value anything. It’s just measuring the ball in the air.

While that makes grand statements harder to come by, its development uses could be far-reaching. What does the data say about the many ways pitchers are encouraged to wield that old baseball truth? Does the focus on repeating deliveries make sense for everyone? Do all those pitchers really need cutters?

To that end, one of the most fascinating aspects of the research is the group of pitchers succeeding without following the age-old, newly quantified wisdom.

Sure enough, you won’t find Hill adhering to the old baseball way. Hill’s release point and the Release-to-tunnel ratio — which gives you an idea of how tightly packed a sequence typically looks between release and the hitter’s decision point, with lower numbers meaning a smaller differentiation — varied more on curveball-to-curveball sequences in 2016 than it did when switching between fastball and curveball.

# NAME PITCHES 1st Pitch Type 2nd Pitch Type Break:Tunnel Plate Diff Release Diff Tunnel Differential Post-tunnel Break Release:Tunnel
1 Rich Hill 343 Curve Curve 0.0999 1.4816 0.4319 0.9225 0.0922 0.4682
2 Rich Hill 244 Curve Fastball 0.7496 1.5265 0.3760 1.1140 0.8350 0.3375
3 Rich Hill 312 Fastball Curve 0.7021 1.5097 0.3617 1.1957 0.8395 0.3025
4 Rich Hill 322 Fastball Fastball 0.0738 1.2881 0.2009 0.7126 0.0526 0.2819

Without going into all of the details, that is patently absurd.

He’s not an island unto himself in this metric, though. Hill is a radical, but he’s not a solitary one. He’s banging the drum for a more individualized brand of pitcher.

Clayton Kershaw, Johnny Cueto, Michael Fulmer. These pitchers are not bad at tunneling. They are blatantly disregarding it, in one way or another.

Their performances resemble something like the inverse of tunneling. And I wonder if it might work just as well in creating that desirable end result: confusion. If you possess the skill (and reckless abandon) to make similar pitches look different, as Hill apparently does, perhaps you can make a clean break with the idea and baffle hitters just the same.

Scouring the lines of tunneling numbers, you begin to wonder if some pitchers in the middling range of tunneling metrics could benefit from following them to the “bottom” of the “leaderboard.”

This comes with a rather large caveat: It isn’t clear just how many pitchers are comfortable altering their arm angles, or their deliveries, or their pitch mix. It’s not a given that their other, more basic skills — control, command, velocity, movement — will transfer over to a modified approach. There are, however, figures worthy of examination on this front — beacons of adaptability, of that creeping individuality.

Like John Lackey.

Wait, whaaaat?

(sorts lists again)

(schedules appointment with ophthalmologist)

Yep, John Lackey.


Not so long ago, a scouting report (almost certainly accurately) described Lackey’s delivery with abundant use of the word “average” before saying that his ability to repeat its timing and positioning were the only things considered plus.

Nowadays, this is a sequence of consecutive pitches.

OK. That looks like Lackey — loose-limbed and casually slumping off the mound to the left.

That leg kick remind you of anyone?

What the? Yes. Lackey throws from a lower angle and falls off the mound in the complete opposite direction. He does this regularly! Sometimes, particularly with his cutter, he throws like this.

I don’t know how many people have noticed the broadening of his delivery repertoire, but I guarantee hitters have.


If necessity is the mother of all invention, then knowledge is its grandmother. While tunneling data might just seem like a cool new way to measure the things pitchers are doing, it will almost inevitably affect what they are doing.

Pick your innovation du jour. When someone (the Astros) spotted a pitcher with an apparently promising curveball spin rate (Collin McHugh), they didn’t say, “Hey, let’s acquire that castoff, put him in the rotation and have him do exactly what he’s been doing!” No, the information instantly re-molded him in its image, making him a project in need of a change. A team saw this possibility and stepped in, eventually engineering a pitcher to take advantage of their insight.

Someone, maybe a lot of someones, will have this experience as more detailed tunneling knowledge ripples around the league. It will undoubtedly guide some hurlers toward consistency through disciplined deception. Tom Koehler, to cherry pick an example, registers several pitch combinations that rate highly in the tunneling metrics. Maybe they are underutilized. Maybe not.

We tend to talk about development and adjustments in a prescriptive tone — like store-bought, star-branded booster packs that can just be applied to other players. That guy should do the Rich Hill.

But that isn’t how things work.

Players pick up ideas from teammates — like Jon Gray learning to shape his slider from noted non-tunneler Adam Ottavino. Or run into a coach who offers a helpful suggestion. Maybe they just figure it out on their own. How did Johnny Cueto come to realize that timing manipulation was his ticket to stardom? I don’t know, maybe he boned up on game theory. But he’s mowing down the National League with absolutely average movement thanks to unmatched delivery funk that — within the realm of tunneling — likely creates enough inconsistency to move hitters’ eyes around while they are trying to guess when he’s going to throw the ball.

The point is, this type of data doesn’t tell pitchers if they are good or bad — as so many of our metrics attempt to. It helps them see what they are.

One 30,000-foot observation of the tunneling data is that many fastball-curve pitchers, like Hill and Kershaw, just aren’t able to mask the difference between those pitches. Now, in their cases, the pure stuff probably negates most of the disadvantage. But there are lessons to be learned for pitchers not blessed with two of earth’s most unhittable offerings.

Tampa Bay Rays v Chicago White Sox Photo by Stacy Revere/Getty Images

Take newly acquired Mariners starter Drew Smyly. He goes fastball-curveball a lot, but he’s very middle-of-the-pack in terms of tunneling (ranking 56th in Release-to-tunnel ratio out of 92 pitchers who threw that sequence 75+ times in 2016). It’s probably not so far off that he’s moving a hitters’ eyes, but it’s also clearly not achieving the deception that Stephen Strasburg or Aaron Nola get from their version of this sequence — where the identities of the pitches become apparent much later in their flights. Smyly’s fastball-cutter sequences? They also fare poorly in tunneling metrics.

Again, in the Release-to-tunnel ratio, a smaller number means less differentiation, more deception (theoretically).

# NAME PITCHES 1st Pitch Type 2nd Pitch Type Break:Tunnel Plate Diff Release Diff Tunnel Differential Post-tunnel Break Release:Tunnel
1 Drew Smyly 196 Fastball Cutter 0.2619 1.5589 0.3050 0.8008 0.2097 0.3809
2 Drew Smyly 181 Cutter Fastball 0.2485 1.7985 0.3091 0.8994 0.2235 0.3437
3 Drew Smyly 285 Fastball Curve 0.7132 2.0700 0.2228 0.8541 0.6091 0.2609
4 Drew Smyly 302 Curve Fastball 0.6608 2.1719 0.2062 0.9358 0.6184 0.2204
5 Drew Smyly 671 Fastball Fastball 0.0543 1.2933 0.1113 0.7267 0.0395 0.1532
6 Drew Smyly 106 Curve Curve 0.0709 1.5756 0.0950 0.8655 0.0614 0.1098

He accompanies that with ultra-consistent tunnels on curve-curve (2nd-tightest behind Bumgarner, 75+ pairs) and fastball-fastball (33rd) sequences.

So, again, this is new data, and it’s difficult to be too sure of anything just yet, but the logical implication for a hitter would seemingly go like this: Smyly just threw a four-seam fastball. If I sit on the same look out of his hand, and get it, it’s almost certainly going to be a fastball.

What does it mean? Well, maybe Smyly — whose use of a cutter screams that he’s attempting to reap the benefits of tunneling — is a candidate to do something a bit wilder. Maybe that means adding an arm angle so one or more of his pitches has multiple looks. Maybe that means adding different shapes to his curve.

Hell, maybe he’s going to add a Cueto-esque pause in his windup. Who knows?!

The one thing I feel confident about: There is a better way forward than the way they’ve always taught it. And it may be utterly unique to him.

. . .

Zach Crizer is a featured writer at Beyond the Box Score. You can follow him on Twitter at @zcrizer.