Poll 2021: 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 included the underperformers. For the first time, 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 a player or multiples that figure out how to do something we have a difficult time quantifying or there’s simply bound to be players each year that fall into either end of the extremes for no reason at all except for randomness. So let’s keep that in mind when reviewing these two groups.

My initial population group consisted of 115 hitters who have put at least 200 balls into play according to Statcast. 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
Cedric Mullins 0.314 0.275 0.541 0.453 0.391 0.350 0.041
Adam Frazier 0.330 0.296 0.463 0.393 0.375 0.336 0.039
David Fletcher 0.309 0.275 0.381 0.321 0.314 0.277 0.037
Marcus Semien 0.277 0.246 0.528 0.460 0.372 0.336 0.036
Jared Walsh 0.278 0.266 0.556 0.479 0.374 0.340 0.034
Yuli Gurriel 0.313 0.274 0.472 0.392 0.364 0.332 0.032
Randy Arozarena 0.251 0.213 0.400 0.352 0.321 0.290 0.031
Xander Bogaerts 0.321 0.287 0.545 0.492 0.396 0.366 0.030
Raimel Tapia 0.283 0.259 0.387 0.333 0.316 0.289 0.027
Garrett Hampson 0.253 0.224 0.415 0.377 0.308 0.282 0.026
Group Average 0.296 0.264 0.468 0.403 0.354 0.320 0.034
League Average 0.240 0.243 0.403 0.413 0.313 0.319 -0.006

Group B – The xwOBA Underperformers
Player BA     xBA     SLG xSLG wOBA xwOBA Diff
Kyle Tucker 0.271 0.319 0.503 0.602 0.351 0.409 -0.058
Aaron Judge 0.282 0.327 0.526 0.639 0.386 0.441 -0.055
Juan Soto 0.283 0.315 0.445 0.543 0.369 0.418 -0.049
Paul Goldschmidt 0.265 0.288 0.432 0.546 0.335 0.383 -0.048
Elvis Andrus 0.235 0.276 0.322 0.396 0.261 0.308 -0.047
Alec Bohm 0.243 0.273 0.343 0.415 0.283 0.325 -0.042
Freddie Freeman 0.274 0.305 0.489 0.582 0.371 0.413 -0.042
Eugenio Suárez 0.175 0.211 0.372 0.438 0.275 0.314 -0.039
Charlie Blackmon 0.261 0.288 0.364 0.436 0.324 0.360 -0.036
Kevin Newman 0.210 0.246 0.273 0.316 0.233 0.268 -0.035
Group Average 0.250 0.285 0.405 0.489 0.318 0.362 -0.045
League Average 0.240 0.243 0.403 0.413 0.313 0.319 -0.006

Group Averages Comparison
Group BA     xBA     SLG xSLG wOBA xwOBA Diff
A 0.296 0.264 0.468 0.403 0.354 0.320 0.034
B 0.250 0.285 0.405 0.489 0.318 0.362 -0.045

These are some interesting groups and unlikely to be the names you might expect to see on each of these lists. It certainly appears that Group A has more speed, but it also has its share of slowies in Walsh and Gurriel, while B features just one name in Newman who clearly owns above average speed. Supposedly, xBA has factored in a batter’s Sprint Speed since 2019, but I’ve been skeptical that that’s the case, or at least it hasn’t been factored in enough. That was from my own subjective feeling after working on an updated xBABIP equation, but Alex Chamberlain confirms this as well. So this is a weakness to be aware of, but it doesn’t mean xwOBA in its current form is useless as a batter could still overperform or underperform their mark ignoring the speed issue.

Group A has posted a significantly stronger batting average than B, but has surprisingly posted an xBA 0.021 points lower. I’m guessing that a lot of that gap could be explained by the speed issue mentioned above, and perhaps once adjusting for speed, the two xBA marks might be quite close to each other. That said, it’s hard to imagine that speed alone would explain the gaps between each group’s BA and xBA.

Next is SLG, in which speed has less of an impact, but still an effect nonetheless. While speed has the greatest impact on infield ground balls that end up as singles, speedier hitters could also turn singles into doubles and doubles into triples, raising their SLG that xSLG isn’t fully reflecting. Again though, speed is likely to play a more impactful role in BA than SLG, so the gaps here shouldn’t be explained as much by it and therefore be more actionable. It’s interesting that Group A’s xSLG is actually all the way down at Group B’s actual SLG, while Group B’s xSLG is actually higher than Group A’s actual SLG.

Overall, we find that Statcast calculates Group A should be posting a wOBA well below Group B, which is not what we found during yesterday’s pitcher SIERA groups, where the underperformers still deserved a higher ERA, with the gap being significantly narrower by SIERA. Here, Group B actually looks like the significantly better group of hitters. And by simply scanning the names, it surely looks like a far better group as well.

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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HauckPark
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HauckPark

How does first half vs. second half wOBA look historically league-wide? With warmer weather, pitchers tiring, etc., I imagine it tends to be higher. (There’s probably an effect from September callups, too, which aren’t really a thing anymore, so I suppose you could leave Sep. numbers out of it.)

With sticky stuff enforcement, any upward trends might be more pronounced as well.