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Spin Rate Kings Who May Be Toppled

The talk of the town is spin rate. If you spend any time following MLB discussion, you have certainly heard all about the league's crackdown on pitchers using foreign substances to help their grip and spin on the baseball.

Like most other topics, there are lots of people on both sides of the argument. No matter your opinion on what's going on, it's important to accept the reality and adapt your play accordingly (if it's even necessary, maybe we'll find out here!).

For this analysis, we will talk about spin rate more generally, and then get into which pitchers have seen the most drastic changes in their spin rate recently.

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Does Spin Rate Even Help?

The theory is that higher spin generates pitches that are harder to make contact with, which generates more strikeouts. We know that the league has made a change in recent weeks which has resulted in pitchers losing less (or no) foreign substance. If our first theory is correct, that should coincide with decreased whiff and strikeout rates.

It turns out, that's exactly what we have seen:

You can see the downtrend is clear.

Now let's get more specific. Here are the league-wide average spin rates on each pitch type:

Pitch Avg Spin Rate (RPM)
Curveball (CU) 2,552
Slider (SL) 2,455
Cutter (FC) 2,409
Four-Seam (FF) 2,313
Sinker (SI) 2,156
Changeup (CH) 1,783
Splitter (FS) 1,485

You can see, and it's not surprising, that the breaking balls have the most spin. When it comes to the off-speed stuff, spin isn't much of a consideration.

Let's dive more into the question concerning spin and its effect on whiff rates, looking at a more granular level. This isn't an easy task. Simply taking all of the pitches of a certain type and checking the swinging-strike rate on different spin rates might tell us something, but that wouldn't do enough to take out of some of the other noise. For example, location and deception factor heavily into getting a whiff, so we want to try to isolate just spin.

What I did was take every qualified pitcher, and for each of their pitch types, I compared the swinging-strike rates on pitches thrown in the bottom half of their spin rate range against the pitches in the upper half of their spin rate range. For example, Trevor Bauer has thrown 172 curveballs this year. I ordered each curveball by its spin rate and then split the data in half. The first group of pitches was Bauer's lower spin rates, and the second group was his higher spin rates. The swinging-strike rate on the first group turned out to be 9.3%, while the second group was at 16.3% - a huge difference.

For curveballs, it turned out that 80% of the pitchers I studied achieved higher swinging-strike rates on their higher spin pitches. The SwStr% difference seems to be most significant on curveballs. Here's the breakdown of that number for each of the other pitch types I looked at:

Pitch %
Curveball (CU) 80%
Splitter (FS) 75%
Four-Seam (FF) 64%
Slider (SL) 64%
Cutter (FC) 62%
Changeup (CH) 54%
Sinker (SI) 43%

Splitters made the top of the list as well, however, that might just be noise in the data considering only 12 pitchers have thrown enough splitters to qualify for this - I wouldn't read much into that.

Curveballs seem to be the only pitch that has its swinging-strike rate hugely dictated by how much spin is on the pitch. You can see that four-seamers, sliders, and cutters are all well above 50% there, but it's not huge, meaning that it's not uncommon to see a pitcher getting just as many or more whiffs with lower spin variations of their pitch.

So which pitchers get the most spin on their curveballs? Here's the leaderboard, you can search and scroll through:

 

It is not enough to just point to the leaders in spin rate and say "these pitchers will likely see their performance decline with the new MLB crackdown on foreign substances". That would be assuming that these pitchers were being aided by substances beforehand. It's certainly possible that some pitchers haven't been using more impactful substances or any substance at all.

We need to compare early season spin rates with very recent spin rates to really have some evidence about which pitchers were benefiting from a substance that they are now no longer using.

For curveballs, the biggest spin losers after June 1st are these names:

Pitcher Spin Before 6/1 Spin After 6/1 % Lost
Dylan Bundy 2537 2362 6.9%
Justin Dunn 2516 2381 5.4%
Rich Hill 2802 2654 5.3%
Clayton Kershaw 2588 2458 5.0%
Jesus Luzardo 2463 2350 4.6%
Blake Snell 2347 2240 4.6%
Joey Lucchesi 2103 2012 4.3%
Bruce Zimmermann 2629 2522 4.1%
Tyler Glasnow 3018 2899 3.9%
Jake Arrieta 2722 2618 3.8%

Some other interesting names right outside the top ten: Sonny Gray, Jameson Taillon, Zach Eflin, Zack Greinke, Corbin Burnes.

The other pitch I want to look at itself before dumping the full data set on you is the four-seam fastball. It was tied with the slider for second place in the analysis above, but I think it's more impactful just because almost every pitcher throws one, and they make up the majority of pitches thrown.

Here are the top 10 biggest losers in terms of average spin on their four-seamer since June 1.

Pitcher Spin Before 6/1 Spin After 6/1 % Lost
Trevor Bauer 2831 2630 7.1%
James Kaprielian 2164 2012 7.0%
Tyler Anderson 2418 2257 6.7%
Dylan Bundy 2551 2390 6.3%
Tyler Mahle 2474 2325 6.0%
Gerrit Cole 2561 2424 5.3%
Kris Bubic 2073 1967 5.1%
Chi Chi Gonzalez 2420 2305 4.8%
Griffin Canning 2324 2228 4.1%
Anthony DeSclafani 2287 2208 3.5%

Other names just outside of the top ten: Rich Hill, JT Brubaker, Freddy Peralta, Casey Mize, Jameson Taillon

Not good news for Dylan Bundy as he appears on both of these leaderboards, losing major spin since the calendar turned to June and not having much success either (just five strikeouts in eight innings while allowing nine earned runs in two starts). It's also probably uncomfortable for fantasy teams with Gerrit Cole on them to see that name show up. Cole has seen his swinging-strike rate on the four-seamer decline pretty steadily since the middle of May - a big-time red flag.

 

Team Analysis

It is possible that the crackdown on foreign substances will effect certain teams more than others. This would be the case if a certain team as a unit was using substances more often, or stickier substances when using it (it's pretty safe to assume that the majority of the league's pitchers were using something to get a better grip on the ball). I went ahead and looked at the numbers, grouping by team. Here are the notable teams.

Los Angeles Angels

Andrew Heaney, Dylan Bundy, Griffin Canning, Junior Guerra, Mike Mayers, Alex Cobb, and Shohei Ohtani have all seen their spin rate on their fastball drop since June 1. Only Patrick Sandoval and Steve Cishek have not seen a decline in spin on the pitch (the rest of the pitchers on that staff haven't thrown enough four-seamers to qualify).

Bundy and Guerra are two names that appear near the top of the board in most spin lost since June 1. In the words of Ben Affleck in Good Will Hunting, "YA SUSPECT!"

Los Angeles Dodgers

What's going out there in Hollywood? The Dodgers have also seen their big-four all lose spin on their fastballs, with Bauer losing a ton (7%), and the rest of the staff losing only marginal spin (Kershaw  2.6%, Urias 0.5%, Buehler 1.3%).

Tampa Bay Rays

All four of Michael Wacha, Rich Hill, Shane McClanahan, and Tyler Glasnow have lost spin on their fastball since June 1, although it's not a huge percentage (4.2% for Hill is the most, the rest are below 3%).

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The three teams seeing the least spin loss from what I can see are the Mariners, Padres, White Sox.

 

Most Alarming Names

The name that stuck out the most from the data was Dylan Bundy. We have seen his spin fall dramatically, and his swinging-strike rate is suffering alongside it (please note we cannot responsibly infer causation from the correlation here, it's quite possible that these two variables are not related, but there's a pretty darn good chance they are).

You can see the downtrend in swinging-strikes for Bundy happening. Bundy's 2021 season has been horrible, so it's likely that your league has already deposited him to the bench or waiver wire, but this analysis gives us no reason to not leave him there.

The two names that come to mind most when you think of spin rate are fantasy aces Gerrit Cole and Trevor Bauer. Both of those names have come up already in this post, so it's worthwhile to check their recent swinging-strike rates:

 

You can see some bumps in the road for these two when you look at recent performance. Cole is coming off of a start where he struck out just four batters over eight innings, and he hasn't struck out double-digit batters since May 12th (six starts since then). Bauer also hasn't struck out double-digits in his last four starts, and has given up 12 earned runs in those outings.

There is almost never a reason to feel confident about reading into trends when you're looking at just a handful of games, so again, I'm not making any definitive claims here. But the fact remains, both of these pitchers have seen their spin rates decline recently and their recent performance has also not been quite to their usual elite levels.

It's also important to keep in mind that strikeout rates have come down league-wide. Context is the more most important thing. If the league average K% is going down, then your fantasy ace can see his decline as well and he wouldn't lose any of his fantasy value. Everything is relative here.

Now to wrap it up, I'd like to just give you my full data set and let you parse through it. Below you'll see a sortable, searchable, table with 793 rows in it. Each row is an individual pitch type thrown by an individual pitcher. Here's the column breakdown to make more sense of the table:

Pitcher: Self-Explanatory
Team: Self-Explanatory
Pitch: Pitch Type (see the above tables to interpret the pitch types if you don't know them)
Count: Total number of that pitch thrown by the pitcher (as of 6/18)
Before 6/1: Average spin rate on the pitch before June 1
After 6/1: Average spin rate on the pitch on or after June 1
Diff: The difference of the two previous columns
% Diff: The percent difference of the before and after columns

As always, this table is much easier to look at on a PC or tablet, it gets pretty tough to manipulate on a smartphone.

 

If you you want the full data or have any questions, please reach out to me on Twitter and I'll be happy to chat!



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