clock menu more-arrow no yes mobile

Filed under:

What do the Rays see in Michael Wacha?

Why Wacha is basically Max Scherzer, part 1

New York Mets v New York Yankees - Game One Photo by Jim McIsaac/Getty Images

The Rays were fast movers on the free agent pitching market this off season, declining to bring back Charlie Morton and replacing his roster spot with . . . Michael Wacha, who the Rays tabbed as a “priority signing” this offseason. How did the Rays reach that conclusion?

There are many ways of evaluating pitchers.

I like to look at a pitch shape graph first before I’ve checked any statistics, watched any video, or thought about pitch location, tunneling, or sequencing. I do this because I want to give myself the chance to take an unbiased impression of the pitcher on the most basic level — what does the ball do after it leaves his hand?

Consider the following graph. It’s from the catcher’s perspective, with values expressed in inches of movement, as compared to a theoretical spinless pitch.*

*That is to say, if a pitcher were to throw a pitch without any spin, and without any other forces due to uneven drag, that pitch would be shown at (0, 0), the center of this graph. This theoretical pitch, depending on how hard it was thrown, would look to us like it’s falling off the table — something like a nasty splitter or slider. A four-seam fastball, which we commonly think of as “straight” and to which our baseball eyes are calibrated, has significant rise and some armside run due to its spin.

The average location of each pitch type (using algorithmic MLBAM classifications) is the center of the circle, while the size of the circle denotes how often the pitcher threw that pitch type.

Michael Wacha, 2020
Texas Leaguers

Go ahead and put together your impressions of this stuff. Try to pretend that you don’t know we’re talking about Michael Wacha, that you don’t know he’s coming off 34 innings with a 6.62 ERA (5.25 FIP, 4.30 xFIP), that he hasn’t just signed a one-year $3 million contract that screams “reclamation project.” Separate this graph from your disappointment that neither Blake Snell nor Charlie Morton will be in a Rays uniform next year.

What do you see?

The Brooks Baseball trajectory and movement tab set to display as Z-scores is a good way to double check and calibrate your perceptions. Note that Brooks uses different pitch classifications but that for Wacha this difference is negligible. Brooks also calculates movement and velocity slightly differently, so don’t read anything into absolute comparisons between their numbers and MLBAM’s, but relative comparisons still work.

Michael Wacha 2020
Brooks Baseballs

A Z-score of zero means that pitch shape dimension is exactly average, and Z-scores approaching one are very notable differences from the average.

Here we see that Wacha’s four-seam fastball was harder than average and had more armside run than average (armside run for a righty means a negative number), while rising just a little bit less than did the average four-seam fastball (more rise is a positive number, less rise or more drop is a negative number). His changeup was extremely fast for a changeup while both running and dropping more than the average. His cutter was harder than average, cut more than average, and produced significantly more downward motion than did most cutters. His curve swept gloveside plenty, but it had exactly average drop, while coming in slower than the majority of curves.

Put in simpler English, I see an average fastball, a hard changeup with pretty good run and drop, a hard cutter with exceptional downward bite and decent sweep, and an unexciting sweeping curve that doesn’t get thrown much. It’s four major league pitches — two above average, one average, one below — all with good separation from each other.

What exactly makes a pitch good is a complex question, but very generally, speed is good, unusual movement is good, and separation is good (except when it’s not).

Put together, this looks like a solid major league starter, right?


Another way to think about stuff is through player comps. This way of describing players is usually reserved for prospects who don’t already have a long track record in the majors. At this point, major league fans have seen enough of a pitcher like Wacha that we don’t need to comp him — he is what he is, the comp for Wacha is Wacha.

But what if we wanted to have some fun? What if there was a computer that would take all the shapes of all the starting pitchers’ pitches in a single MLB season, do some inscrutable math, and then tell you who else was most similar to the pitcher you’re evaluating?

Well, have you heard the good news? There is a computer that does inscrutable math! And Michael Wacha’s similar pitchers are a doozy.

Michael Wacha Similar Pitchers
Baseball Savant

Listed out with an arbitrary cutoff (I’ve been thinking of anything above .8 as a noteworthy comparison), that list is:

  1. Michael Wacha, 1.0
  2. Brandon Bielak, 1.0
  3. Max Scherzer, 1.0
  4. Griffin Canning, .97
  5. Luke Weaver, .97
  6. Tony Gonsolin, .95
  7. Zach Plesac, .90
  8. Lance Lynn, .85

So the first thing you’ll notice is Scherzer, who, according to the algorithm, is just as similar to Wacha as Wacha is to himself. On the whole this is a strong group of pitchers, lead by Scherzer and 2020 breakout stars Plesac and Gonsolin. Lynn has been an above average starter for a long time now, and Weaver and Canning are both good bets to be average or better going forward.

Brandon Bielak on the other hand, who the algorithm also rated as basically identical to Wacha (but that wouldn’t have gotten you to click, would it), has struggled to establish himself as a valuable major league pitcher.

Yes, but what does “similar” mean?

The name “Scherzer” gets a person’s attention, but let’s take a look at what is and isn’t actually similar about the two.

Max Scherzer
Texas Leaguers

Comparing this image to the one of Wacha above, quite honestly I don’t quite see it. Scherzer throws both a hard cutter and a slower slider while Wacha’s harder breaking ball sits in the middle of those. Their fastball movement is similar, but Scherzer throws his faster. Scherzer also gets more velocity separation between fastball and changeup, as well as more drop on that changeup. Really, the greatest similarity is in the shape of their curves, and Wacha only threw eight curves in 2020 (more on this later).

I suspect the disconnect between common sense similarity and algorithmic similarity lies in two factors.

First, that this algorithm notices similarities in the relationships of shapes between pitch shapes that human pitching evaluators may not care about. This may be a strength or a weakness, or maybe both. I also suspect that the shortened 2020 season poses a challenge for the algorithm, and that a 1.0 similarity score means less in 2020 than it did in 2019 — this is definitely a weakness.

But secondly (and spoiler alert, this one is going to be important later in this series), the algorithm isn’t thinking in terms of pitch type buckets. Where we see Scherzer throwing a cutter at 91 mph and a slider at 85 mph, it sees a bunch of individual hard breaking pitches ranging from 94 mph to below 82 mph. And where we see Wacha throwing an 89 mph cutter, it sees a bunch of individual pitches, some with more velocity (up to 93 mph) and less break, others with less velocity (down to 84 mph) and more break.

Wacha Cutters Shape, 2020
Data from Baseball Savant

As you can see, there’s an overall speed gradient from less movement to more movement, but sometimes a pitcher manages his best pitch that is both faster than normal and breaks more than normal.

We bucket because much of the time pitchers are thinking of their pitches as separate, and because bucketing helps us analyze pitching, but if you ever do pitch classifications yourself (and I do recommend everyone give it a shot), then you’ll find that the actual lines between buckets are rarely as clean as they appear on the final graph.

Some windows are lighted. But mostly they’re darked.

Now for the bait-and-switch

The important questions for the Wacha-Scherzer similarity is “How different, really, are Scherzer’s cutter and his slider?” and “How uniform, really, is Wacha’s cutter?”

Taking this pitch classifications and graphs at face value, I think that the more insightful comparisons are not Scherzer but Canning...

Griffin Canning
Texas Leaguers

and Weaver...

Luke Weaver
Texas Leaguers

The thread uniting this group of pitchers is a good-not-great four-seam fastball** and then significant use of a changeup and a cutter that have similar vertical behavior at different horizontal movement points. None of those three pitches blow hitters away on their own, but they all have a relationship to each other that, if sequenced and located right, should present the batter with a complex set of perceptions to keep the ball off the barrel. Each of those pitches can theoretically be thrown to either lefties or righties, and I think should be thought of as a system.

**Note that Wacha’s fastball has had greater ride in past years, and potentially could again. Maybe a plan to get back to his old fastball shape was part of Kyle Snyder’s lengthy pitch to Wacha in free agency that got him to sign so early.

There’s a lot more to say about these ideas, so we’ll continue the analysis shortly, making this what I think will be a three-part series.

Up next in part two we’ll dive into some more detailed small sample size analysis, and try to figure out what to do with this group of similar pitchers, like: What can the other pitchers tell us about what to expect from Wacha? And do any of them suggest a roadmap for approaches Wacha might try? Do you dare to stay out? Do you dare to go in?

Until then, I can’t help but notice that this group of similar pitchers has at least one name who should be available on the trade market in Luke Weaver. If the Rays think they can pull off the renaissance with Wacha, would they be interested in trying it on Weaver as well?