Poll 2022: Which Group of Pitchers Performs Better?
Since 2013, I have polled you wonderful readers on which group of pitchers you think will post the better aggregate ERA post all-star break. The two groups were determined based on ERA-SIERA disparity, pitting the overperformers versus the underperformers during the pre-all-star break period.
I came up with this idea given my faith in using SIERA over smaller samples, rather than ERA, as I generally ignore ERA completely as late as the middle of the season and it’s interesting to see how everyone else thinks. Will the SIERA overperformers continue to outperform, perhaps due to continued strong defensive support and/or more pitcher friendly ballparks, or will the magic disappear? Is it just bad luck that is due to reverse course for the SIERA underperformers or are they being hampered by one of the aforementioned factors that should continue to play a role the rest of the way?
My initial population group consisted of 110 pitchers who have thrown at least 70 innings. Group A is composed of the 10 largest SIERA overperformers, while Group B is composed of the 10 largest SIERA underperformers. Let’s compare.
| Name | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Michael Wacha | 22.3% | 41.2% | 36.5% | 14.3% | 9.1% | 0.240 | 80.9% | 17.6% | 7.7% | 2.69 | 4.55 | -1.86 |
| Tony Gonsolin | 16.9% | 42.1% | 40.9% | 4.0% | 9.1% | 0.197 | 86.5% | 24.2% | 6.7% | 2.02 | 3.68 | -1.66 |
| Marco Gonzales | 19.7% | 45.9% | 34.4% | 12.0% | 12.0% | 0.272 | 75.8% | 12.3% | 7.9% | 3.50 | 5.15 | -1.65 |
| Sandy Alcantara | 14.2% | 56.4% | 29.4% | 15.1% | 5.7% | 0.241 | 78.9% | 23.5% | 6.3% | 1.76 | 3.40 | -1.64 |
| Michael Kopech | 16.6% | 30.5% | 52.9% | 12.7% | 8.5% | 0.215 | 76.7% | 21.6% | 12.3% | 3.36 | 4.88 | -1.52 |
| Alek Manoah | 19.9% | 38.8% | 41.3% | 12.4% | 7.8% | 0.244 | 81.0% | 22.6% | 5.5% | 2.28 | 3.75 | -1.47 |
| Justin Verlander | 21.1% | 39.1% | 39.8% | 19.1% | 9.6% | 0.236 | 81.6% | 25.6% | 4.5% | 1.89 | 3.30 | -1.41 |
| Taijuan Walker | 20.2% | 49.6% | 30.2% | 15.2% | 5.1% | 0.274 | 77.8% | 20.1% | 6.9% | 2.55 | 3.95 | -1.40 |
| Joe Ryan | 15.2% | 28.6% | 56.2% | 16.9% | 7.6% | 0.240 | 81.5% | 21.6% | 7.2% | 2.99 | 4.38 | -1.39 |
| Johnny Cueto | 17.4% | 42.9% | 39.7% | 4.6% | 10.3% | 0.276 | 84.9% | 19.5% | 6.7% | 2.80 | 4.19 | -1.39 |
| Group Average | 18.3% | 42.5% | 39.2% | 12.8% | 8.5% | 0.245 | 80.1% | 21.0% | 7.0% | 2.51 | 4.05 | -1.54 |
| League Average* | 20.2% | 42.6% | 37.2% | 9.9% | 11.8% | 0.290 | 72.5% | 21.4% | 7.7% | 4.09 | 4.08 | 0.01 |
| Name | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Hunter Greene | 21.0% | 28.3% | 50.6% | 9.3% | 19.5% | 0.280 | 71.7% | 28.8% | 10.2% | 5.78 | 3.72 | 2.06 |
| Patrick Corbin | 22.0% | 45.5% | 32.4% | 8.3% | 15.6% | 0.363 | 64.8% | 19.4% | 7.5% | 5.87 | 4.20 | 1.67 |
| Austin Gomber | 22.1% | 41.4% | 36.5% | 5.8% | 13.5% | 0.324 | 61.9% | 17.4% | 6.8% | 5.97 | 4.43 | 1.54 |
| Jose Berrios | 21.7% | 38.2% | 40.1% | 8.7% | 15.9% | 0.316 | 73.3% | 21.1% | 5.5% | 5.22 | 3.92 | 1.30 |
| German Marquez | 20.9% | 48.5% | 30.7% | 6.0% | 18.0% | 0.303 | 65.2% | 18.8% | 8.2% | 5.47 | 4.29 | 1.18 |
| Lucas Giolito | 23.4% | 33.5% | 43.1% | 4.9% | 15.5% | 0.338 | 73.1% | 27.1% | 8.5% | 4.69 | 3.61 | 1.08 |
| Trevor Rogers | 22.3% | 41.0% | 36.7% | 4.3% | 11.7% | 0.324 | 66.9% | 20.5% | 10.3% | 5.46 | 4.52 | 0.94 |
| Alex Cobb | 17.1% | 62.9% | 20.0% | 2.4% | 12.2% | 0.324 | 58.8% | 22.8% | 6.7% | 4.09 | 3.17 | 0.92 |
| Alex Wood | 21.8% | 51.0% | 27.2% | 4.2% | 11.3% | 0.327 | 66.8% | 23.9% | 5.6% | 4.20 | 3.33 | 0.87 |
| Charlie Morton | 24.4% | 37.6% | 38.0% | 8.2% | 15.3% | 0.295 | 74.3% | 27.1% | 8.8% | 4.45 | 3.60 | 0.85 |
| Group Average | 21.7% | 42.7% | 35.5% | 6.6% | 15.2% | 0.321 | 67.7% | 22.6% | 7.8% | 5.15 | 3.90 | 1.25 |
| League Average* | 20.2% | 42.6% | 37.2% | 9.9% | 11.8% | 0.290 | 72.5% | 21.4% | 7.7% | 4.09 | 4.08 | 0.01 |
| Group | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A | 18.3% | 42.5% | 39.2% | 12.8% | 8.5% | 0.245 | 80.1% | 21.0% | 7.0% | 2.51 | 4.05 | -1.54 |
| B | 21.7% | 42.7% | 35.5% | 6.6% | 15.2% | 0.321 | 67.7% | 22.6% | 7.8% | 5.15 | 3.90 | 1.25 |
| League Average* | 20.2% | 42.6% | 37.2% | 9.9% | 11.8% | 0.290 | 72.5% | 21.4% | 7.7% | 4.09 | 4.08 | 0.01 |
Finally, a really interesting list of names in both groups! I have found that typically the underperformer group seems worse from a name perspective, but this time it seems to be one of the strongest groups in terms of perceived value since I’ve started this exercise.
As usual, the underlying skills posted by the two groups are fairly similar. One of the primary differences is in batted ball profile. Group A (the overperformers) has allowed a lower LD% and induced a higher rate of pop-ups (IFFB%) than Group B (the underperformers). Those missing line drives by A pitchers have turned into fly balls. That’s going to result in a lower BABIP, a potentially lower HR/FB rate, but perhaps more home runs given the higher rate of fly balls. I’m not sure exactly what effect that would have on ERA, as I would need to use an ERA estimator and plug in the numbers to determine if a slightly lower LD% and higher FB% and IFFB% is better, but I would guess it is, especially as HR/FB rate has declined versus recent seasons.
Moving along to the three luck metrics, and we unsurprisingly find the drivers of the massive discrepancies between ERA and SIERA for each group. Group A has posted a HR/FB rate just more than half of Group B’s, which is a massive difference. There are a number of pitchers who play their home games in home run friendly parks on both lists, so it’s highly doubtful park effects play a significant role in the HR/FB rate gap. It’s more likely that Group A has simply allowed weaker fly ball contact than Group B, but the more important question is whether that trend will continue. In past exercises, the HR/FB rate gap has continued, but it has dramatically narrowed, so regression toward the mean ended up playing much more of a role over the rest of the season.
Holy smokes, look at that BABIP gap! As mentioned earlier, clearly Group A is deserving of a lower BABIP given the lower LD%, along with the higher FB% and IFFB%. But there’s absolutely no way they deserve a BABIP this far below Group B’s! Group A’s batted ball profile really isn’t that much different than the league average, yet they have posted a .245 BABIP versus a .290 league BABIP. That can’t possibly last. Meanwhile, it’s unlikely Group B posts such an inflated BABIP the rest of the way as well. Though they haven’t generated many pop-ups, and have allowed a higher line drive rate than the league, the odds are both improve at least a bit, and the group’s BABIP improves.
Finally, the HR/FB rate and BABIP discrepancies have driven a divergence in LOB%. Obviously, pitchers allowing fewer home runs on their fly balls and hits on balls in play are going to end up stranding more baserunners, which we see here. Once the groups’ HR/FB and BABIP marks move closer to league average the rest of the way, LOB% is going to move in the opposite direction toward league average as well.
I think what’s most interesting here is that Group A has actually posted a lower strikeout rate than Group B, as well as a lower walk rate. So in terms of potential rest of season fantasy value, I definitely want the higher strikeout rate group on my team! We find that Group A’s SIERA overperformance is actually bigger than Group B’s underperformance. Furthermore, Group A has actually posted a marginally higher SIERA than Group B, which hasn’t always been the case in past exercises.
So which group performs better over the rest of the season? Let’s get to the poll questions. Feel free to share your poll answers and why you voted the way you did.
Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year and three-time Tout Wars champion. He is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. Follow Mike on X@MikePodhorzer and contact him via email.
I’m curious about the results from the previous polls, and how well they aligned with subsequent performance each year.
Have the underperformers and overperformers typically regressed such that their actual ERA gaps have narrowed, or have their first-half trends continued into the second half?
The gaps have always narrowed and I do remember one time in the past few years, the SIERA underperformer group posted a lower ERA than the overperformer group over the second half.