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Generic fastballs just don’t cut it

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Or why individual pitches are so hard to evaluate.

Texas Rangers v Seattle Mariners Photo by Stephen Brashear/Getty Images

Do you remember way back in October when Lance McCullers threw 28 straight curveballs to close out Game Seven of the American League Championship Series? In the aftermath, I wondered if we were witnessing the death of the fastball. There may be a little hyperbole in that claim—pitchers will always throw fastballs—but we’re also seeing hitters do more damage off fastballs than ever before. Teams like the Astros or the Yankees are pushing their pitchers to throw more breaking balls and offspeed pitches and this change in approach came to a head during the playoffs.

You might also remember that the Mariners have increased their fastball usage as a team year-over-year since Jerry Dipoto became GM in 2015. At the end of that piece linked above, I talked about finding pitchers who could adjust their approach to hide their fastball with a more effective pitch. I still have that project in mind—I’ve been working on the initial research for it—but I wanted to take a closer look at why fastballs are so ineffective.

The other day, I saw a really interesting graph created by Andrew Perpetua, creator of xStats.org and writer for RotoGraphs.

via Andrew Perpetua

This graph shows the relationship between pitch velocity, spin rate, and swinging strike rate. There’s a lot to dissect in this graph but I want to focus on that large blue area towards the bottom of the graph. This blob represents fastballs thrown between 88-94 MPH, with a spin rate between 1800-2400 RPM. This group of pitches includes four-seamers, two-seamers, and sinkers. We know that fastballs produce the lowest swinging strike rates when compared to breaking balls or offspeed pitches. But this graph reveals something even more interesting. Unless you throw your fastball with incredible velocity or generate a spin rate much higher than normal, you’re probably going to end up with a swinging strike rate well below league average. Conceptually, this makes sense. We know there’s a direct relationship between higher velocity and higher spin rates. But what about those pitchers who don’t possess the outlier characteristics that produce really effective fastballs?

The Mariners pitching staff is filled with pitchers with pretty generic fastballs. Erasmo Ramirez, Marco Gonzales, and Andrew Moore each possess a fastball that’s basically league average when it comes to velocity and spin rate. There are a few outliers, like Ariel Miranda’s spin rate or Edwin Diaz’s velocity, but it’s a pretty underwhelming group as a whole. I pulled a bunch of data from Statcast to see what the expected swinging strike rate for each Mariner pitcher might be based on their average velocity and spin rate.

Expected Swinging Strikes

Player Name Avg Velocity Spin Rate xSwStr% SwStr% ▲Diff
Player Name Avg Velocity Spin Rate xSwStr% SwStr% ▲Diff
Nick Vincent 90.1 2379 6.5% 13.1% 6.6%
Juan Nicasio 95.3 2070 7.4% 12.5% 5.1%
James Pazos 95.6 2042 7.4% 11.3% 3.9%
Shawn Armstrong 93.1 2375 9.1% 12.4% 3.3%
Ryan Garton 92.6 2140 6.1% 8.9% 2.8%
James Paxton 95.5 2262 9.1% 11.5% 2.4%
Marco Gonzales 91.7 2205 6.6% 9.0% 2.4%
Andrew Moore 91.0 2209 6.6% 8.3% 1.7%
Marc Rzepczynski 92.5 2107 5.9% 7.6% 1.7%
David Phelps 94.3 2316 8.5% 10.2% 1.7%
Edwin Diaz 97.4 2289 11.8% 13.5% 1.7%
Hisashi Iwakuma 85.4 2079 4.6% 6.0% 1.4%
Mike Morin 90.4 2106 5.5% 6.8% 1.3%
Christian Bergman 88.5 2064 4.4% 5.6% 1.2%
Ariel Miranda 92.1 2412 8.0% 8.5% 0.5%
Felix Hernandez 90.7 2097 5.3% 5.3% 0.0%
Erasmo Ramirez 91.7 2266 7.1% 7.0% -0.1%
Mike Leake 90.3 2026 5.1% 4.7% -0.4%
Dan Altavilla 96.8 2391 10.9% 10.2% -0.7%
Casey Lawrence 91.2 2119 5.9% 5.2% -0.7%
Chase De Jong 90.1 2220 6.0% 5.1% -0.9%
Tony Zych 94.5 2038 6.9% 6.0% -0.9%
Sam Moll 92.4 2412 8.2% 4.7% -3.5%
League Average 92.8 2217 6.7% 8.2% 1.5%
The data includes four-seam fastballs, two-seam fastballs, and sinkers.

Nick Vincent, again, shows us the value of ignoring these basic pitch characteristics. His fastball outperforms his expected swinging strike rate by a wide margin and a lot of that is due to effective velocity—a concept I discussed last week when taking a look at Juan Nicasio. And look, Nicasio is sitting right under Vincent.

What might be surprising is the league average outperforming its expected swinging strike rate. That’s because velocity and spin rate are just small pieces the puzzle. By limiting our analysis to these two variables, we’re only getting a limited view of what makes a pitch effective. Things like movement profiles, effective velocity, pitch sequencing, and pitch tunneling all have an effect on swinging strike rates so it shouldn’t be a surprise that major league pitchers are able to outperform their limited pitch characteristics.

But what’s really interesting is the lower half of that table. A pitcher like Dan Altavilla, who possesses plus velocity on his fastball, clearly isn’t maximizing this pitch. Ariel Miranda and Sam Moll both possess above average spin rates on their fastballs but aren’t able to leverage that characteristic into an effective pitch. These are the pitchers who might be candidates to adjust their repertoire or learn how to use effective velocity to their benefit.

Evaluating a single pitch is so hard because there are so many variables to consider. Velocity, movement, and now spin rate all get the most attention because they’re easily measurable. But we also need to consider these other, more hidden factors too. This is as much a reminder for me as it is for you.