Poll 2022: Which Group of Hitters Performs Better?

Yesterday, I asked you to vote on which group of pitchers you expect to post a better ERA over the rest of the season. One group was composed of the 10 biggest SIERA overperformers, while the other comprised the underperformers. For just the second year, I’m going to take the same polling idea and use it for hitters. So let’s follow the same concept and compare two groups of hitters based on xwOBA overperformance and underperformance. We know that xwOBA isn’t perfect. Neither is SIERA. In fact, no estimated/expected/forecasted equation is going to be perfect, because there will always be players that do something we have a difficult time quantifying. Furthermore, there will always be players each year that fall into either end of the extremes for no other reason than complete randomness. So let’s keep that in mind when reviewing these two groups.

My initial population group consisted of 157 qualified hitters. Group A is composed of the 10 largest xwOBA overperformers, while Group B is composed of the 10 largest xwOBA underperformers.

Group A – The xwOBA Overperformers
Player BA     xBA     SLG xSLG wOBA xwOBA Diff
Jose Ramirez 0.288 0.271 0.576 0.462 0.396 0.346 0.050
Paul Goldschmidt 0.330 0.278 0.590 0.536 0.429 0.381 0.048
Jose Iglesias 0.301 0.270 0.404 0.345 0.327 0.288 0.039
C.J. Cron 0.298 0.264 0.552 0.502 0.383 0.352 0.031
Xander Bogaerts 0.316 0.273 0.453 0.424 0.368 0.337 0.031
Nolan Arenado 0.293 0.274 0.526 0.476 0.379 0.350 0.029
Jeff McNeil 0.300 0.270 0.418 0.387 0.344 0.317 0.027
Brandon Drury 0.278 0.265 0.528 0.495 0.370 0.348 0.022
Chris Taylor 0.238 0.213 0.409 0.390 0.316 0.296 0.020
Jose Altuve 0.275 0.272 0.518 0.475 0.383 0.365 0.018
Group Average 0.294 0.267 0.504 0.453 0.373 0.340 0.033
League Average 0.242 0.255 0.395 0.438 0.310 0.328 -0.018

Group A – The xwOBA Underperformers
Player BA     xBA     SLG xSLG wOBA xwOBA Diff
Marcell Ozuna 0.221 0.272 0.407 0.549 0.298 0.367 -0.069
Corey Seager 0.251 0.309 0.480 0.609 0.341 0.409 -0.068
Alex Verdugo 0.262 0.308 0.372 0.503 0.295 0.358 -0.063
Christian Walker 0.204 0.267 0.460 0.576 0.337 0.397 -0.060
Max Muncy 0.160 0.203 0.315 0.432 0.291 0.349 -0.058
Seth Brown 0.216 0.272 0.396 0.486 0.288 0.342 -0.054
Shohei Ohtani 0.258 0.292 0.486 0.622 0.356 0.410 -0.054
Ryan Mountcastle 0.270 0.312 0.473 0.579 0.334 0.388 -0.054
Kyle Schwarber 0.208 0.248 0.503 0.631 0.351 0.404 -0.053
Max Kepler 0.245 0.298 0.394 0.499 0.329 0.382 -0.053
Group Average 0.233 0.282 0.431 0.552 0.323 0.382 -0.059
League Average 0.242 0.255 0.395 0.438 0.310 0.328 -0.018

Group Averages Comparison
Group BA     xBA     SLG xSLG wOBA xwOBA Diff
A 0.294 0.267 0.504 0.453 0.373 0.340 0.033
B 0.233 0.282 0.431 0.552 0.323 0.382 -0.059
League Average 0.242 0.255 0.395 0.438 0.310 0.328 -0.018

First, let’s confront the elephant in the room. If you look at the league average lines in any of the tables, you would have noticed that the Statcast expected metrics are significantly higher than the actual marks. This isn’t normal! Obviously, over a large enough sample, the actual and expected marks should be close to identical. The only explanation I can think of is the change to the ball this year that has resulted in a drop in HR/FB rate. BABIP is down too, so clearly results on balls in play are worse than they have been in past years, and Statcast uses actual results from past years to calculate an expected result for each batted ball. So that means that in aggregate, players will look like they are underperforming more than they are. Since all fantasy analysis is relative, that doesn’t really matter when looking at multiple players. But keep that in mind if picking a particular hitter and noting he has underperformed his xwOBA. Your hitter might actually be underperforming by the same degree the league as a whole is, which means he’s likely not underperforming at all.

That said, let’s get on with comparing Group A, the xwOBA overperformers, with Group B, the underperformers. Group A’s overperformance is smaller in both xBA and xSLG than Group B’s, which aligns with the above that in aggregate, the league is underperforming. A pair of veterans are leading all of baseball in xwOBA overperformance, but they are doing it in a different way. Jose Ramirez has primarily overperformed his xSLG, while Paul Goldschmidt has overperformed his xBA, undoubtedly thanks to his inflated .388 BABIP. It’s really surprising to find Ramirez atop the list, considering his HR/FB rate is at its lowest since 2016. Though, he’s made up for it by already hitting 30 doubles (just two short of all last season), which is tied for third in baseball. To think that even with a disappointing HR/FB rate, he’s still overperforming is a scary thought for his fantasy owners, which includes me! Goldschmidt has not been a consistent xwOBA overperformer, so there’s no reason to think he’ll avoid the regression monster.

In the past, I’ve noticed that the speedier players were lumped into Group A more than in B, as speed doesn’t seem to be properly accounted for in the xwOBA equation. There isn’t a whole lot of speed in either groups, though perhaps with Chris Taylor and Jose Altuve, there’s a touch more in Group A.

The most obvious characteristic driving the gap between these two groups is batted handedness and facing the shift. Check out how many left-handed sluggers are in Group B — Max Muncy, Seth Brown, Kyle Schwarber, Max Kepler. These guys all hit a high rate of grounders into the shift, which kills their BABIP marks. Unfortunately, shifts are ignored by xwOBA, so the calculation consistently overestimates these types of hitters. That said, they still are probably underperforming as Statcast suggests they should be hitting for significantly more power and hitting into the shift is only going to take away singles.

Overall, it’s interesting to see Group A’s actual wOBA a bit lower than Group B’s xwOBA. So not only should the two groups move in opposite directions the rest of the way, Statcast thinks Group B should have actually performed better than Group A performed, even with the good fortune they have apparently benefited from.

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. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.

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cjsarponmember
19 days ago

My assumption is that A trends down, B trends up, but the handedness issue as Mike points out (Group B a bunch of shiftable LHB) ultimately proves to be the main driver here and Group A still outperforms Group B ROS.

Joe Wilkeymember
18 days ago
Reply to  cjsarpon

I mean, you’re partially right. If you break it down by batted ball types, Group B does perform worse relative to expected than Group A on grounders. Group A has a .285 actual wOBA on grounders compared to .226 expected, while Group B has a .176 actual compared to a .238 expected.

For everything except GB, however, Group A is still outperforming expectations, and Group B is still underperforming. For all non-GB league-wide, wOBA is .468, compared to .471 expected. This in itself is interesting, as maybe the stark difference in overall wOBA may be more due to more aggressive shifting rather than a difference in the ball. For what it’s worth, the Statcast era started with 9.6% of pitches being thrown in front of a shift in 2015. It crept up slowly over the next three years to 17.7% in 2018, then exploded. 2019 saw 26.2% of pitches thrown in front of a shift, 2020 was 34.7%, 2021 dropped to 30.9%, and so far in 2022 it’s 35.8%. In total, 20.7% of pitches in the StatCast era have been thrown in front of a shift, way lower than this year’s rate.

That aside, Group A has a wOBA of .519 on FB/LD/PU compared to a .447 expected wOBA. Group B has a wOBA of .482 on FB/LD/PU compared to a .536 expected. Ground balls account for roughly 40% of each group’s balls in play, so ground balls account for less than half of the difference from expectations on contact for each group.

So yes, while GB do have an effect, it’s not even half of the difference. If you split the difference between actual and expected for each group, they’re roughly the same. I still expect Group B to be better over the remainder of the year, I don’t believe than entire difference in grounders is due to the shift.

cjsarponmember
18 days ago
Reply to  Joe Wilkey

Good context. Though would ask do we know non-GB batted ball types to not be materially affected by the shift or is this simply an assumption you’re making here?

If its immaterial its immaterial but I certainly feel as if I’ve seen plenty of LDs/FBs that the dreaded shallow RF shift has turned into outs.