The fastball: comparing “stuff” and results

Most people that follow baseball know what it means to have good “stuff.” A triple-digit fastball, a slider with insane cross movement, or a high-spinning curveball. Just take a look @PitchingNinja to build up more of a look. It isn’t 100% correlation, but typically the pitchers with good “stuff” would yield gold results. Makes sense, right? The harder you throw, the more swinging-strikes you get. The more horizontal movement you get, the more ground balls you’ll induce, etc.

But what about the guys that don’t fit this strong correlation? The ones that get crushed while throwing hard or the ones that get away with sub-90 MPH fastball. You can probably think of a couple of guys off the top of your head that fit that description, but there isn’t anything out there that correlates the difference between “stuff” and results to a strong extent. And this is where my research comes to play. I wanted to build up this better understanding between the relationship of the two.

Process

After a few months of slowly and manually churning through pages upon pages on Excel, making a few tweaks along the way, I finally reached my final product. Starting out back in May, I sorted every pitcher since 2015 with a) 20 innings pitched in a season and b) a sample size of 50 four-seam fastballs (I narrowed the study to just one type of fastball) thrown. Each player was separated into their respective season (2015 Clayton Kershaw, 2016 Clayton Kershaw). After all that, the final sample size came down 1,371 different pitcher seasons.

My final goal was to create two calculations, one that defined a pitcher’s “stuff” on his four-seam fastball and one that describe their results. For each calculation, I used a certain array of data that spit out a final number. Each calculation was based off a selection of factors.

When he was at RotoGraphs, Eno Sarris complied a similar calculation to find value in a pitch.

So, to that end, I’ve taken each pitch type and looked at only those pitchers that have thrown 100+ in each of those types. I’ve summed the ground-ball and swinging strike rates for each pitch, and then found the standard deviations. I’ve given each pitcher a z-score for his ground-ball rate and swinging strike rate on each pitch type. Then I’ve summed the z-scores for each pitch type, and then for each pitcher.

Here’s what mine factor in...

• Stuff: velocity, Spin Rate, horizontal movement, vertical movement
• Results: Exit Velocity, Launch Angle, swinging-strike rate

Once all this data was exported and arranged accordingly, I toyed around with it to find a good fit of weights to each metric. All factors were then summed up to create the two respective calculations.

My initial plan on this was to find a good way to rank and quantify a pitcher’s fastball. But then it soon developed into a way to compare the discrepancies between a pitcher’s “stuff” and the results that came with it.

“Stuff”

When you think of a pitcher with good “stuff” in terms of their fastball, who comes to mind? Guys like Aroldis Chapman, Blake Treinen, Max Scherzer, and Corey Kluber probably come to mind. If you know how to scour through data on the internet, you can probably find the best ones through your definition, whether that be velocity, spin rate, or movement. But with everything compared mean, weighted, and summed. Here were the top four-seam “stuff” seasons since 2015.

There aren’t any surprises at the top here with Aroldis Chapman and Justin Verlander filling out the top six spots. Notably, the only starters on here were Verlander and Ariel Miranda (!).

Results

Results are perhaps harder to visually quantify than “stuff” is. With “stuff,” you can see a GIF off a pitcher throwing a nasty 98 MPH fastball with cross movement garner a stupid looking swing from a hitter. But you don’t actually know if his fastball is good in the big picture. For a pitcher that lacks that above-average velocity, he can in some cases get away with prime locating, generating a plethora of weakly hit ground balls. Results kicks all those raw factors out and exclusively cares about what actually happened. Now here are the top four-seamer results seasons.

Like in the “stuff” table, most of these pitchers are relievers. Though we do see 2015 Darren O’Day take the top spot, along with 2018 Jacob deGrom cracking the top 20.

Correlation and Difference

As I mentioned above, you’d expect at least some correlation between “stuff” and results. Placing the results sum on the x-axis and the stuff sum on the y-axis, we find that r = 0.41. Nothing great and super defining, but definitely significant.

This may even be better visualized through some buckets. Separating each stuff z-score sum and averaging it out, we find a perfect slope to fit our narrative.

But just like with every non-great correlation, there are plenty of outliers to be found. And a lot of them are names you’d expect. The pitchers outperforming the “stuff” on their four-seam fastball the most go as: 2016 Darren O’Day, 2015 Darren O’Day, 2018 Matt Albers, 2016 Kyle Hendricks, and 2018 Rick Porcello. Most of the pitchers near the top of this list lack above-average velocity.

The pitchers underperforming their “stuff” the most are: 2015 Blake Treinen, 2016 John Danks, 2016 Ariel Miranda, 2015 Ivan Nova, and 2016 Taylor Rogers. The one that stands out to me here is Treinen, being a guy that couldn’t pitch to the level of his plus-offerings. From 2014-17, his four-seamer induced a 6.1 percent swinging-strike rate. But after firing the pitch in the zone more and improving his command this season, his four-seamer posted a 15.6 percent swinging-strike rate. There was little change in velocity and spin rate.

Conclusion

This isn’t anything groundbreaking in terms of discovering anything. If you want to build up a good idea of how good a certain pitch is, finding its wOBA/xwOBA usually does the job. This just helps build a better understanding of how much raw skills go into a offering, what it takes to make a plus-offering, and how a pitcher’s “stuff” and results intertwine.

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Patrick Brennan loves to research pitchers and minor leaguers with data. You can find additional work of his at Royals Review and Royals Farm Report. You can also find him on Twitter @paintingcorner.