# What is Too Many Four-Seamers?

The question came up when I examined David Peterson. I wondered if he was getting hit around because he was throwing a ton of subpar fastballs. Today, I’m back-testing the theory.

I had no idea what I was going to find but the results, positive or negative, will help to shape future studies. I examined starters from 2021 and 2022 who threw at least 20 innings (n=201). I limited the time frame to include the STUFFF metrics that have only been around that long. Also, I limited this study to guys who threw their four-seamer more than their sinker. I started with just four-seamers and stayed away from sinkers. The STUFFF metrics are separated based on pitch type so I wanted to stay in one lane.

The narrative behind four-seamers (or any fastball) would be that batters would familiarize themselves with these fastballs. I know that bad fastballs won’t generate as many strikeouts but do they get hit around more, especially if that’s all batters see.

Additionally, I included my pERA values which is only based on if the pitch misses (SwStr%) and the direction it is hit (GB%). These values might seem high but I don’t scale the value based on pitch type and fastballs generate fewer swings-and-misses than non-fastballs. It’s time to start the journey.

First, I grouped the pitchers by how far their ERA estimator was from their actual ERA. Here are the results.

Four-Seamer Fastball Metrics Depending on ERA-FIP
ERA-FIP > 1 Between -1 and 1 < -1
BABIP .322 .286 .241
HR/9 1.5 1.2 1.3
K% 18.7% 21.6% 22.6%
FF% 42.5% 37.8% 34.4%
FF%/(FF%+SI%) 79.1% 78.4% 71.1%
FFv 93.1 93.1 92.9
wFF/C -1.26 -0.21 0.12
Stuff+ 86.4 91.9 94.9
Bot+ 47.6 52.4 50.0
pERA 4.82 4.67 4.68

Four-Seamer Fastball Metrics Depending on ERA-xFIP
ERA-xFIP > 1 Between -1 and 1 < -1
BABIP .310 .287 .254
HR/9 1.8 1.2 1.0
K% 18.9% 21.7% 22.9%
FF% 39.4% 38.2% 35.1%
FF%/(FF%+SI%) 77.9% 78.2% 76.9%
FFv 93.0 93.2 92.9
wFF/C -1.57 -0.19 0.76
Stuff+ 87.2 91.3 99.1
Bot+ 48.8 52.2 53.5
pERA 4.88 4.68 4.50

Four-Seamer Fastball Metrics Depending on ERA-SIERA
ERA-SIERA > 1 Between -1 and 1 < -1
BABIP .307 .287 .264
HR/9 1.9 1.2 0.9
K% 18.9% 21.8% 21.6%
FF% 39.7% 38.0% 36.6%
FF%/(FF%+SI%) 79.6% 77.5% 79.2%
FFv 92.8 93.2 92.7
wFF/C -1.51 -0.21 0.58
Stuff+ 87.4 92.0 93.4
Bot+ 49.2 52.4 51.7
pERA 4.87 4.67 4.58

Four-Seamer Fastball Metrics Depending on ERA-xERA
ERA-xERA > 1 Between -1 and 1 < -1
BABIP .309 .286 .276
HR/9 1.8 1.2 1.3
K% 18.9% 21.9% 19.8%
FF% 41.0% 38.0% 35.7%
FF%/(FF%+SI%) 80.1% 78.8% 70.8%
FFv 92.5 93.2 92.9
wFF/C -1.61 -0.13 -0.39
Stuff+ 85.2 92.6 88.6
Bot+ 47.1 52.7 49.2
pERA 4.83 4.65 4.86

There is a lot to unpack, but the biggest takeaways for me are

• The pitchers with higher than expected ERA threw more fastballs on average.
• The pitchers with higher-than-expected ERA generally had worse STUFFF.
• The pitchers with lower-than-expected ERA mixed in more sinkers.
• Fastball velocity didn’t matter. It still remains linked to strikeouts.

Here are two more groupings by HR/9 and BABIP.

Average Four-Seamer Fastball Metrics Depending on HR/9
HR/9 > 1.7 Between 0.7 and 1.7 < .0.7
BABIP .294 .285 .293
HR/9 2.2 1.2 .6
K% 18.1% 21.8% 23.8%
FF% 39.7% 37.7% 38.8%
FF%/(FF%+SI%) 79.3% 78.2% 73.8%
FFv 92.466 93.156 93.943
wFF/C -1.72 -.09 .40
Stuff+ 85.7 92.5 92.3
Bot+ 49.3 52.1 53.8
pERA 4.99 4.65 4.49

Average Four-Seamer Fastball Metrics Depending on BABIP
BABIP > .317 Between .253 and .317 < .253
BABIP .334 .284 .237
HR/9 1.3 1.3 1.2
K% 20.1% 21.4% 22.9%
FF% 40.5% 37.8% 36.0%
FF%/(FF%+SI%) 75.5% 78.9% 76.9%
pfxvFA 93.212 93.112 92.941
pfxwFA/C -.76 -.32 .50
Stuff+ 85.6 92.1 96.8
Bot+ 51.1 52.1 51.5
pERA 4.75 4.69 4.59

The results are a little messier but the conclusions are close to being the same.

• The batters who got hit around threw a few more fastballs on average.
• The pitchers who got hit around had worse STUFFF.
• Fastball velocity or sinker/four-seam mix didn’t matter to over-or-under-perform batted ball metric.

The two major factors seem to be the usage rate and the STUFFF metrics.

After eyeballing the above tables, it seems like a usage under 40% along with a Stuff+ value under 90 and a Bot Stuff under 50. To see if these benchmarks work, I took the 2023 starters and grouped them.

2023 ERA-ERA Estimators for Starters Throwing Lots of Bad Four Seamers
Four-seam traits FIP xFIP SIERA
Usage >40%, BotStuff <50 -0.10 -0.19 -0.03
Everyone else 0.06 0.07 0.04
Usage >40%, Stuff+ <90 -0.12 0.19 0.17
Everyone else 0.06 0.06 0.04

The pitchers I expected to perform worse actually performed better. That’s suboptimal. I did find out what possibly didn’t work but it would be nice if the values were predictive. I ran one last comparison for future reference, here are the pitchers’ stats for if their ERA is above or below their ERA estimators so far this season.

2023 Stats for Grouped by ERA-ERA Estimator Above or Below Zero
ERA minus estimator FF% wFA/C BABIP HR/9 botStf FF Stf+ FF
ERA-FIP >0 40.2% -0.53 .320 1.4 47.9 93.6
ERA-FIP <0 42.6% 0.17 .268 1.3 49.7 96.6
ERA-FIP >0 41.2% -0.80 .318 1.6 48.0 92.7
ERA-FIP <0 41.5% 0.47 .270 1.0 49.5 97.6
ERA-SIERA <0 40.7% -0.86 .318 1.6 47.3 92.2
ERA-SIERA >0 42.1% 0.53 .270 1.0 50.3 98.2

The usage doesn’t matter this season but the STUFFF values show some signs worth continued investigation.

That’s enough failure for one article. Here is what I see needs to be done next.

• Sinkers will be included by weighting the results by usage. David Peterson mixes in some (bad) sinkers so maybe the combination brings more clarity.
• I’m going to attempt a fastball grade that takes into account the predictive values (STUFFF), pitch results (pERA), and batted ball results (pVAL). From some past work, I wasn’t a huge fan of pVALs but I think they might help show the possible disconnects between shape and results (e.g. ability to hide the ball).

While I didn’t come to any groundbreaking information, I found what not to believe and hopefully, I can improve the future results.

Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR twice, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.

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Nathanmember
6 days ago

Isn’t what you are trying at the end there what Pitcherlist did this offseason with PLV?