Poll 2021: Which Group of Pitchers Performs Better?
Since 2013, I have polled you dashingly attractive 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 learn 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 vanish? And 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 105 pitchers who have thrown at least 70 innings, which included some that are no longer in a rotation and/or are injured (I excluded these pitchers from the tables). Group A is composed of the 10 largest SIERA overperformers, while Group B is composed of the 10 largest SIERA underperformers.
| Name | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Kyle Gibson | 19.8% | 50.9% | 29.3% | 7.2% | 9.6% | 0.254 | 84.3% | 21.7% | 7.6% | 2.29 | 4.15 | -1.86 |
| Lance Lynn | 20.5% | 34.5% | 45.0% | 12.6% | 8.7% | 0.243 | 86.2% | 28.5% | 8.4% | 1.99 | 3.75 | -1.76 |
| Kevin Gausman | 15.7% | 44.2% | 40.1% | 5.6% | 7.5% | 0.212 | 84.7% | 30.5% | 6.9% | 1.73 | 3.31 | -1.58 |
| Taijuan Walker | 24.1% | 39.7% | 36.2% | 15.5% | 7.1% | 0.249 | 77.6% | 24.9% | 8.4% | 2.50 | 4.08 | -1.58 |
| John Means | 21.7% | 30.4% | 47.8% | 20.5% | 14.8% | 0.192 | 100.0% | 25.7% | 4.9% | 2.28 | 3.78 | -1.50 |
| Kwang-hyun Kim | 19.6% | 47.5% | 32.9% | 15.3% | 8.3% | 0.280 | 77.1% | 19.2% | 8.1% | 3.11 | 4.59 | -1.48 |
| Wade Miley | 21.6% | 53.0% | 25.4% | 8.2% | 8.2% | 0.279 | 78.4% | 19.2% | 6.6% | 2.80 | 4.16 | -1.36 |
| Lance McCullers Jr. | 15.8% | 52.5% | 31.7% | 7.8% | 9.4% | 0.260 | 80.3% | 26.4% | 12.2% | 2.94 | 4.29 | -1.35 |
| Walker Buehler | 19.0% | 41.7% | 39.3% | 8.8% | 11.4% | 0.231 | 83.7% | 25.7% | 5.9% | 2.36 | 3.71 | -1.35 |
| Anthony DeSclafani | 18.1% | 45.5% | 36.5% | 9.5% | 10.5% | 0.238 | 81.0% | 23.5% | 6.9% | 2.68 | 4.02 | -1.34 |
| Group Average | 19.5% | 44.5% | 36.0% | 11.0% | 9.6% | 0.245 | 82.4% | 24.6% | 7.5% | 2.44 | 3.95 | -1.52 |
| League Average* | 21.2% | 43.3% | 35.5% | 9.6% | 14.1% | 0.288 | 72.8% | 23.2% | 8.0% | 4.19 | 4.20 | -0.01 |
| Name | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Matt Harvey | 25.8% | 41.8% | 32.4% | 9.0% | 13.5% | 0.362 | 54.5% | 16.7% | 7.1% | 7.70 | 4.83 | 2.87 |
| Eduardo Rodriguez | 22.3% | 43.0% | 34.8% | 7.9% | 15.7% | 0.361 | 64.5% | 27.0% | 5.5% | 5.52 | 3.49 | 2.03 |
| Andrew Heaney | 24.8% | 31.6% | 43.7% | 5.6% | 15.6% | 0.328 | 67.9% | 28.5% | 7.9% | 5.38 | 3.71 | 1.67 |
| Chris Paddack | 21.4% | 44.5% | 34.1% | 2.6% | 15.4% | 0.332 | 60.1% | 24.2% | 5.4% | 5.38 | 3.75 | 1.63 |
| Mike Minor | 20.6% | 38.4% | 41.0% | 11.0% | 13.4% | 0.305 | 63.2% | 22.8% | 7.3% | 5.67 | 4.23 | 1.44 |
| Jorge Lopez | 24.8% | 47.7% | 27.5% | 5.6% | 19.7% | 0.350 | 69.9% | 21.0% | 9.9% | 5.95 | 4.54 | 1.41 |
| Justus Sheffield | 23.2% | 45.5% | 31.3% | 5.2% | 18.2% | 0.347 | 65.3% | 16.9% | 10.0% | 6.48 | 5.12 | 1.36 |
| Jake Arrieta | 24.5% | 41.5% | 34.0% | 4.9% | 20.7% | 0.308 | 63.6% | 19.0% | 10.4% | 6.30 | 4.99 | 1.31 |
| Aaron Nola | 21.5% | 40.0% | 38.5% | 9.6% | 14.4% | 0.331 | 71.3% | 29.5% | 5.4% | 4.53 | 3.26 | 1.27 |
| J.A. Happ | 19.9% | 34.2% | 45.9% | 8.2% | 13.9% | 0.319 | 68.2% | 18.5% | 6.9% | 5.90 | 4.81 | 1.09 |
| Group Average | 22.8% | 40.9% | 36.3% | 7.3% | 15.7% | 0.334 | 64.8% | 22.5% | 7.5% | 5.82 | 4.23 | 1.59 |
| League Average* | 21.2% | 43.3% | 35.5% | 9.6% | 14.1% | 0.288 | 72.8% | 23.2% | 8.0% | 4.19 | 4.20 | -0.01 |
| Group | LD% | GB% | FB% | IFFB% | HR/FB | BABIP | LOB% | K% | BB% | ERA | SIERA | Diff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A | 19.5% | 44.5% | 36.0% | 11.0% | 9.6% | 0.245 | 82.4% | 24.6% | 7.5% | 2.44 | 3.95 | -1.52 |
| B | 22.8% | 40.9% | 36.3% | 7.3% | 15.7% | 0.334 | 64.8% | 22.5% | 7.5% | 5.82 | 4.23 | 1.59 |
| League Average* | 21.2% | 43.3% | 35.5% | 9.6% | 14.1% | 0.288 | 72.8% | 23.2% | 8.0% | 4.19 | 4.20 | -0.01 |
Surprisingly, these groups aren’t significantly different from an underlying skill metrics basis. Group A (the overperformers) has suppressed line drives and those liners have become ground balls, while Group B (the underperformers) have allowed a higher rate of liners at the expense of grounders. Obviously, you would prefer a lower LD% and higher GB% like Group A has posted, but are those sustainable skills, or mostly just randomness over a relatively small sample size?
We find a bigger difference on the IFFB% side, or pop-up rate. Group A has been significantly more adept at inducing them, while Group B has struggled to do so. There is typically more skill here than on LD%, so I’d chalk this rate gap up as being more sustainable.
Moving along to the luck metric trio, we find our first clue as to why these pitchers find themselves in each particular group. Unsurprisingly, Group A has posted a significantly lower HR/FB rate than both the league average and Group B, which has posted a higher than league average mark. How much of this is home ballpark vs pitcher skill vs luck? Moving along to BABIP, we find a massive difference between the two groups. Sure, Statcast metrics aren’t included here for simplicity sake, but just looking at the batted ball type distribution rates, do you really think the BABIP gap between these two groups deserves to be this large?! Perhaps defensive support differences play a role, which shouldn’t dramatically change, but I would argue this gap is more luck than anything else.
Finally, we round out the luck metric trio discussion with LOB%, the metric that gets the least amount of attention. There’s definitely some skill here that goes above and beyond the obvious drivers (strikeout/walk rates, HR rate, etc), like stolen base attempt and success rates, and underlying skills with runners on vs bases empty. But there’s no way that would explain the entirety of the huge disparity between these two groups.
Last, we move on to strikeout and walk rates. Group A has struck out a higher rate of batters than both the league average and Group B, while both groups have posted identical walk rates.
Combining all the underlying skill metrics, we do find that Group A is a slightly better aggregate of starting pitchers, but it’s actually pretty darn close. It’s interesting to see that Group A overperforms its SIERA by nearly the same amount as Group B underperforms its. And actually, Group B’s pitchers are essentially league average on the whole, as its SIERA is barely worse.
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 founder of ProjectingX IQ, an advanced fantasy baseball analytics platform that transforms projection data and in-season performance signals into actionable intelligence. He 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.
Do you have an overall ranking over where the two groups have finished historically? Which group has been the better bets?
I haven’t tracked it, but if you google “podhorzer fangraphs poll review” with the year, you should find all of them. I believe the underperformer group did win at least once.
You had me at Matt Harvey
Stretch vs Windup
An underperforming pitcher can be elite in the windup and fall to pieces in the stretch. Lots of unknowns as to the precise reasons as to the disparity, at least in terms that fantasy can track. With all the motion data, there could be an expansion in this field.
Sure, that could lead to big differences in LOB%, but that’s backwards looking. Unless it’s an actual skill we could prove, or a lack of skill we could prove, who’s to say a pitcher’s low LOB% or high LOB% won’t revert back toward a mark that better matches their other skills (K%, BB%, batted ball distribution profile)?
Since I have 2 in the first group and none in the second group, I really hope those guys continue to do well.
If one has watched a few of those „underperforming“ pitchers- one can deduct that their underperforming numbers might b deserved bc they are relatively not good/bad/broken pitchers. I’ve watched a few Arrieta/Harvey/Happ/Sheffield starts. Their ERAs feel deserved as one is surprised if they ever get a lucky lineout on days when they „don’t have it“. Which are plentiful.