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.
| 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 |
| 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 |
| 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 |
| 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.
| 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 |
| 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.
| 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.
| 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.
