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Velocity, Spin Rate, and Pitcher Expectations

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Which Cardinal pitchers over or underperformed their expectations based on velocity and spin?

Cleveland Indians v St Louis Cardinals
John Gant was one of the Cardinals kings of spin this season
Photo by Dilip Vishwanat/Getty Images

The Cardinals have their work cut out for them this off-season with regards to the pitching staff. The bullpen’s performance was dreadful all season long. The rotation performed very well, but is bursting at the seams with options entering 2019. Adam Wainwright will be back with an undefined role, while Alex Reyes is a wild card. Tyson Ross and Bud Norris, each mostly effective out of the bullpen in 2018, are not likely to return. Tyler Lyons was very quietly granted free agency a few weeks ago. Thirty players took the mound for the Cardinals last season, and 21 of them are still on the table as 2019 options. That assumes Ross and Norris are gone. There’s a lot to work through.

There will certainly be some new additions, and some of the 21 remaining will be stashed in AAA, traded, or granted free agency. However, it’s a good idea to know what you have before making those types of moves. You need to know if a pitcher got better or worse results than you can reasonably expect. I want to take a different approach today in making that determination. Let’s play around with velocity and spin rate to see how Cardinal pitchers fared compared to their league-wide counterparts.

Methodology

I’ve collected the average velocity, average spin rate, and wOBA (weighted on-base average) allowed against each specific pitch for each Cardinal pitcher in 2018. These pieces are readily available on Baseball Savant.

From there, I determined the wOBA allowed by the average pitcher from 2016 to 2018 on similar pitches. Then, that number was compared to each respective Cardinal, who was given a league-relative wOBA (a wOBA+, as it were) comparing their performance to the rest of the league. Note that I did not perform a ballpark adjustment, so keep that in mind as you view the results.

Here’s a real world example. Luke Weaver’s average four-seam fastball this season was 93.7 miles per hour, with a spin rate of 2,324 RPM (revolutions per minute). He allowed a .342 wOBA on fastballs this season. Since 2016, when all pitchers have thrown four-seam fastballs between 92 and 93.9 mph with a spin rate between 2,300 and 2,399, they’ve allowed a .349 wOBA. We then take Weaver’s .342, divide it by the league-wide .349, and multiply it by 100. The resulting figure- 98.0- means that Weaver’s four-seam fastball was 2% more effective than league average, with 100 representing league average. Put another way, in the absence of other factors beyond velocity and spin, you would have expected Weaver to allow a wOBA that was 2% worse against his fastball.

I performed this exercise for four-seam fastballs, sinkers, sliders, and curveballs. I also combined two-seam fastballs and cutters into their own category. I did not research change-ups because so much of the effectiveness of a change-up is inherently wrapped up in other factors. The effectiveness of a change-up says a lot more about the rest of a pitcher’s approach and repertoire compared to the quality of the change-up itself. As such, I excluded it.

I’ve also excluded pitchers who are already gone. You won’t see Sam Tuivailala, Greg Holland, Preston Guilmet, or Ryan Sherriff in here. You also won’t see Jedd Gyorko or Greg Garcia because, come on, why would I do that? Lastly, I’ve excluded Alex Reyes and Giovany Gallegos on the basis of their minuscule sample sizes. However, I’m leaving Lyons, Norris, and Ross in place because there may be something instructive regarding their performances in the underlying data.

Results

Four-Seam Fastballs

We’ll start with four-seam fastballs. I’ve opted to leave the number of results- the number of plate appearances that ended on this particular pitch- in these tables in the interest of transparency. You’ll see why as you dig through the data.

4-Seam Fastballs vs. Lg. Average

Player Spin Velo Results wOBA lgwOBA wOBA+
Player Spin Velo Results wOBA lgwOBA wOBA+
Matt Bowman 2117 93.3 11 0.160 0.359 44.6
Dominic Leone 2273 93.9 34 0.175 0.350 50.0
Jordan Hicks 2106 100.9 16 0.165 0.306 53.9
Tyson Ross 2499 91.5 53 0.245 0.370 66.2
Daniel Poncedeleon 2216 93.2 73 0.244 0.350 69.7
John Gant 2442 93.2 62 0.244 0.341 71.6
John Brebbia 2389 94.6 117 0.246 0.334 73.7
Carlos Martinez 2209 95.1 107 0.254 0.335 75.8
Dakota Hudson 2165 96.7 5 0.250 0.318 78.6
Jack Flaherty 2200 93.2 237 0.286 0.350 81.7
Miles Mikolas 2309 94.1 221 0.276 0.334 82.6
Tyler Webb 2186 90.2 35 0.338 0.381 88.7
Michael Wacha 2069 93.6 162 0.342 0.371 92.2
Luke Gregerson 2349 87.6 16 0.378 0.389 97.2
Chasen Shreve 2439 91.9 41 0.361 0.370 97.6
Luke Weaver 2324 93.7 330 0.342 0.349 98.0
Austin Gomber 2087 92.5 151 0.382 0.371 103.0
Mike Mayers 2351 96.1 140 0.351 0.300 117.0
Bud Norris 2450 94.4 71 0.401 0.329 121.9
Brett Cecil 2086 90.0 31 0.473 0.387 122.2
Tyler Lyons 2150 89.7 16 0.528 0.389 135.7
Adam Wainwright 2174 89.4 20 0.571 0.389 146.8

It’s easy to dismiss Bowman, Hicks, Hudson, Gregerson, Lyons, and Wainwright, whose results are built on 20 plate appearances or less. Leone, Cecil, and Webb are also teetering on the brink. The biggest outliers here are Norris in the negative and Ross in the positive. Pitchers with Norris’ four-seamer typically see much better results, whereas pitchers with Ross’ four-seamer typically fare much worse. Something in their make-up led to an inversion in results.

Michael Wacha, Miles Mikolas, John Gant, Carlos Martinez, and Jack Flaherty- a group that combined for 112 of the team’s 162 this season- all well exceeded the league averages for similar fastballs.

Two-Seam Fastballs and Cutters

Moving on to the other kinds of fastballs, here are the results for two-seamers and cutters.

Two-Seamers and Cutters vs. League Average

Player Avg Spin Velo Results wOBA lgwOBA wOBA+
Player Avg Spin Velo Results wOBA lgwOBA wOBA+
Luke Weaver 2278 87.1 MPH 24 0.249 0.344 72.4
John Gant 2424 93.2 MPH 192 0.283 0.355 79.7
Michael Wacha 2129 89.4 MPH 59 0.269 0.337 79.8
Daniel Poncedeleon 2354 89.5 MPH 31 0.273 0.331 82.5
Bud Norris 2508 91.9 MPH 141 0.262 0.305 85.9
Jack Flaherty 1989 91.1 MPH 97 0.329 0.368 89.4
Carlos Martinez 2142 91.8 MPH 208 0.355 0.365 97.3
Dominic Leone 2424 90.8 MPH 62 0.330 0.333 99.1
Miles Mikolas 2215 93.5 MPH 173 0.347 0.345 100.6
Brett Cecil 2196 86.1 MPH 20 0.344 0.337 102.1
Adam Wainwright 2326 83.9 MPH 33 0.344 0.331 103.9
Matt Bowman 2011 91.2 MPH 61 0.389 0.370 105.1
John Brebbia 2158 93.5 MPH 5 0.407 0.367 110.9
Tyson Ross 2496 90.8 MPH 9 0.400 0.333 120.1
Luke Gregerson 2320 87.8 MPH 23 0.452 0.331 136.6

Small samples more or less eliminate Weaver, Brebbia, Ross, Gregerson, Poncedeleon, Cecil, and Wainwright here. As we’ll see, this is the only pitch where Mikolas had worse results than league average. Gant, Wacha, Norris, and Flaherty stand out as overperformers on two-seamers and cutters. For the first three in that list, they amassed impressive results on these pitches. Flaherty, on the other hand, was still better than the overall league average on two-seamers and cutters, but his velocity and spin typically yield worse results.

Sinkers

It’s a limited group throwing enough sinkers to register, but there are some doozies in there.

Sinkers vs. League Average

Player Avg Spin Velo Results wOBA lgwOBA Pitch+
Player Avg Spin Velo Results wOBA lgwOBA Pitch+
Adam Wainwright 2146 89.5 48 0.371 0.403 92.1
Jordan Hicks 2080 100.4 246 0.316 0.341 92.7
Dakota Hudson 2191 95.9 68 0.308 0.318 96.9
Tyler Lyons 2065 88.8 22 0.419 0.381 110.0
Brett Cecil 2025 89.7 33 0.493 0.381 129.4

It’s tough to deduce much from Lyons and Cecil, though it’s worth noting that they’re showing up with worse results compared to the league average on almost every single pitch. Something beyond velocity and spin prevented them from having better seasons.

Wainwright exceeded expectations, but it has more to do with the league-wide ineffectiveness of sinkers thrown with his spin and velocity. The real beasts here are Hicks and Hudson. The average wOBA against all sinkers, regardless of velocity and spin, is .350. With that in mind, even if Hudson regressed to league average for his spin and velocity class, it would still be an effective pitch. It’s possible that Hicks is due for some regression but I suspect other factors pushed his sinker wOBA down below league average.

Sliders

Now we start seeing the limitations of sample size for all of our relievers.

Sliders vs. League Average

Player Avg Spin Velo Results wOBA lgwOBA Pitch+
Player Avg Spin Velo Results wOBA lgwOBA Pitch+
Jordan Hicks 2425 86.2 75 0.162 0.270 60.0
Dakota Hudson 2467 91.3 31 0.185 0.259 71.4
Tyler Webb 2049 80.1 21 0.229 0.317 72.2
Miles Mikolas 2361 88 205 0.202 0.256 78.9
Carlos Martinez 2156 83.5 129 0.256 0.295 86.8
Luke Gregerson 2588 80.6 17 0.245 0.244 100.4
Jack Flaherty 2234 83.6 215 0.282 0.276 102.2
Matt Bowman 2298 84.6 16 0.317 0.276 114.9
Austin Gomber 2307 88.7 74 0.295 0.256 115.2
Tyler Lyons 2376 79 41 0.328 0.280 117.1
Mike Mayers 2191 86.7 85 0.344 0.288 119.4
Bud Norris 2715 84.1 27 0.279 0.228 122.4
Tyson Ross 2633 85.5 41 0.279 0.227 122.9
Chasen Shreve 2289 85.7 13 0.359 0.276 130.1
John Gant 2511 83.4 19 0.340 0.261 130.3
John Brebbia 2622 82.8 83 0.322 0.246 130.9
Dominic Leone 2490 83.7 7 0.707 0.275 257.1

Seven of our 17 pitchers fail to cross the 30 result threshold. If it seems like these are impressive wOBAs allowed by Cardinal pitchers, bear in mind that the league average on all sliders- regardless of velocity and spin- is .266 over the last three years. Still, we can see some encouraging signs here. Gregorson’s mix of velocity and spin is typically lethal. The same is true for Brebbia, who underperformed on the pitch. If velocity and spin is any indication, he probably has better times coming his way. Mikolas was absolutely devastating with his slider, and the spin and velocity combo suggests he should have been above average. The same is true for Hudson.

Curveballs

Finally, here are curveballs, which seem to be an all or nothing pitch. Bowman, Martinez, Poncedeleon, and Hudson rarely end at-bats with the pitch, rending their results meaningless.

Curveballs vs. League Average

Player Avg Spin Velo Results wOBA lgwOBA Pitch+
Player Avg Spin Velo Results wOBA lgwOBA Pitch+
Adam Wainwright 2706 73.6 73 0.196 0.323 60.7
Jack Flaherty 2410 77.2 48 0.190 0.270 70.4
Michael Wacha 2235 75.8 38 0.264 0.289 91.3
Miles Mikolas 2613 78.5 170 0.257 0.280 91.8
Dakota Hudson 2426 86.4 14 0.241 0.259 93.1
Austin Gomber 2498 77.9 75 0.255 0.270 94.4
Brett Cecil 2353 81.4 57 0.286 0.262 109.2
Luke Weaver 2219 80.8 77 0.387 0.269 143.9
John Gant 2575 76.1 38 0.384 0.256 150.0
Carlos Martinez 2037 78.7 8 0.474 0.307 154.4
Daniel Poncedeleon 2621 77 3 0.587 0.257 228.4
Matt Bowman 2677 76.1 3 0.678 0.257 263.8

For context, the league-wide wOBA against all curveballs is .266. Flaherty and Wainwright performed exceptionally well on the pitch, and Gomber, Mikolas, and Wacha did fairly well. Cecil continued his trend of underperforming expectations. For both Gant and Weaver, their worst pitch was the curveball. If not for how much damage he yielded on curveballs, Weaver would have had a fairly solid season.

Putting it all together

I’d like to do one more thing here, if you’ll bear with me through one last table. I’ve taken each pitcher’s results and weighted it by the percentage of results they received. In doing so, I’ve accumulated a total expected wOBA for each pitcher based on spin and velocity. The name “xwOBA” is taken, so I’m calling it “spin-velOBA.” I know it’s a weird, cumbersome name, but this is far from official. I compared their spinvelOBA to the actual wOBA they allowed and created yet another league-relative number. The lower the numbers here, the more a pitcher overperformed compared to his expected level based on spin and velocity, spread across all pitches. The higher, the more they underperformed.

The positive is that this should scrub out some of the small sample issues we had with individual pitches, and get us more overall results for some of the outliers.

wOBA vs. Expectation

Pitcher Results spin-velOBA+
Pitcher Results spin-velOBA+
Daniel Poncedeleon 107 76.6
Tyler Webb 56 83.5
Jordan Hicks 337 85
John Gant 311 87
Tyson Ross 103 87.8
Miles Mikolas 769 88.2
Adam Wainwright 174 88.7
Michael Wacha 259 89.4
Dakota Hudson 118 90
Carlos Martinez 452 90.2
Dominic Leone 103 90.8
Jack Flaherty 597 91.3
John Brebbia 205 93.9
Bud Norris 239 100.5
Matt Bowman 91 102.5
Luke Weaver 431 103.2
Chasen Shreve 54 103.9
Austin Gomber 300 111.2
Luke Gregerson 56 114.6
Mike Mayers 225 118.1
Tyler Lyons 79 119.3
Brett Cecil 141 137.2

It’s almost comical how sharply it divides between relievers and starters. Gant, Mikolas, Wainwright, Wacha, Carlos Martinez, Flaherty, and Daniel Poncedeleon accounted for 124 of the Cardinals’ 162 starts, and each allowed a wOBA that was 8% or lower than their expected level based on spin and velocity. Weaver and Gomber are the biggest outliers in the rotation, though Weaver was fairly close to what you would expect within our parameters.

On the other hand, Cecil, Lyons, Mayers, and Gregerson have velocity and spin that suggests they should have received much better results. Given how far Cecil, Lyons, and Gregerson fell from their 2017 results, it’s easy to see how the season spun out of control in high leverage situations. For the three that are returning, it offers a ray of hope that 2019 won’t be as disastrous.

What’s Missing?

I’d love to draw some firm conclusions here, but it would be intellectually dishonest. This has been a fun exercise and it might shed some light on these pitchers. However, there are several big puzzle pieces missing when evaluating the data above.

Location/Command: We have no idea how well these pitchers are hitting their spots with these pitches.

Life (horizontal and vertical movement): Throwing a high RPM pitch at a good velocity sound great in a vacuum, but not if the pitch is straight. A lot of ground can be made up with the life on a pitch. Granted, velocity and spin can generate horizontal and vertical movement in many cases, but it’s not guaranteed.

Tunneling and/or sequencing: Throwing different back to back pitches through the same tunnel, even if the pitch is lacking in velocity or spin, can very effectively disrupt a hitter’s timing. That’s only indirectly accounted for in the results above.

Similarly, sequencing pitches can disrupt a hitter- changing their eye level, coaxing them to back off the plate or dive out over the plate, or simply speeding up and/or slowing down their bat with off-speed pitches.

Quality of opponent: Austin Gomber’s fastball was a tick worse than league average for similar pitchers, registering a 103 wOBA+. However, we don’t know if he faced an inordinate amount of good hitters, or had to swim upstream against the platoon advantage more than normal. Ultimately, that’s true for all of these pitchers. That piece of information would help fill in some gaps for us.

Defense: There’s nothing in the data above that accounts for the quality, or lack thereof, of the defense behind each pitcher during these events.

Dumb luck: Even if we account for every other piece listed above, there will always be dumb luck that can completely wreck a pitcher’s statistic in these type of categories. It happens all the time in baseball, and this type of research is no different. Hitters get two degrees more loft on a flyball, they swing a millisecond sooner, outfielders get a bad jump and suddenly a flyball becomes a double, pitchers don’t quite get the grip they want on a curveball, a sharp slider turns into a cement mixer, and on and on. Not all of those things are “luck” in the purest sense of the word but they’re random events that can add noise to the data above.

This all leaves us with a fun thought experiment about velocity and spin, and an educational tool for which types of spin and velocity combinations yield better results. They’re building blocks to understanding where these pitchers over or underperformed. What this data doesn’t do is answer why they over or underperformed their expected wOBA. We can save that for another day.

If you’re looking for similar, much more detailed work, I recommend QOP (quality of pitch).