New Pod Hitter xBABIP vs Statcast xBABIP — The Overcalculated

Last week, I introduced the latest iteration of my ever-improving hitter xBABIP equation, by starting with Statcast’s implied xBABIP (SxBABIP) calculation and adding additional variables to my regression. As you could imagine, it has resulted in a Pod xBABIP (PxBABIP) that sometimes varies widely from SxBABIP. So yesterday, I shared a large group of hitters that PxBABIP was significantly higher for vs SxBABIP. The pattern was a speedy group who avoided pulling grounders into the shift and hit their grounders to the opposite field more frequently than the league. Today, let’s now check out the group of hitters whose PxBABIP is well below SxBABIP.

Statcast xBABIP Overcalculated
Player BABIP Statcast xBABIP xBABIP Diff Sprint Speed PSIFAGB R% PSIFAGB L% Opposite GB%
Luke Voit 0.313 0.333 0.308 -0.026 24.9 22.7% 0.0% 2.3%
Zack Collins 0.303 0.317 0.297 -0.021 25.2 0.0% 20.5% 2.5%
Yasmani Grandal 0.246 0.274 0.256 -0.018 23.5 0.0% 20.7% 5.0%
Kole Calhoun 0.281 0.262 0.244 -0.018 24.7 0.0% 24.8% 6.6%
Jake Lamb 0.239 0.274 0.256 -0.018 26.0 0.0% 21.6% 2.3%
Matt Carpenter 0.250 0.340 0.323 -0.018 25.7 0.0% 18.8% 3.9%
Tommy La Stella 0.255 0.290 0.273 -0.017 24.9 0.0% 18.6% 2.7%
Mitch Moreland 0.256 0.255 0.239 -0.016 24.4 0.0% 23.2% 6.7%
Joey Votto 0.287 0.300 0.285 -0.015 25.1 0.0% 15.2% 1.0%
Corey Seager 0.336 0.322 0.306 -0.015 26.5 0.0% 18.6% 3.6%
Carlos Santana 0.227 0.263 0.248 -0.015 24.9 2.2% 16.9% 3.3%
Albert Pujols 0.223 0.248 0.234 -0.015 22.4 16.3% 0.0% 4.2%
Max Muncy 0.257 0.287 0.273 -0.014 26.7 0.0% 19.3% 2.9%
Rougned Odor 0.242 0.239 0.225 -0.014 26.7 0.0% 23.2% 4.3%
Jorge Soler 0.250 0.288 0.275 -0.014 26.9 19.6% 0.0% 4.0%
Max Kepler 0.225 0.285 0.271 -0.013 27.4 0.0% 21.0% 4.1%
Freddie Freeman 0.321 0.346 0.333 -0.013 27.0 0.0% 16.4% 3.9%
Brian Goodwin 0.259 0.296 0.283 -0.013 27.3 0.0% 18.8% 2.9%
Ji-Man Choi 0.300 0.313 0.300 -0.013 25.4 0.0% 16.9% 5.0%
Mike Zunino 0.231 0.236 0.223 -0.013 26.0 18.9% 0.0% 2.4%
Lewin Diaz 0.210 0.234 0.222 -0.012 25.5 0.0% 17.3% 1.2%
Kyle Schwarber 0.306 0.303 0.291 -0.012 26.8 0.0% 18.2% 4.1%
Shohei Ohtani 0.303 0.320 0.308 -0.012 28.8 0.0% 20.1% 3.9%
Anthony Rizzo 0.258 0.273 0.261 -0.012 25.2 0.0% 17.3% 4.1%
Cal Raleigh 0.267 0.266 0.254 -0.012 27.0 0.0% 18.6% 2.3%
Kyle Seager 0.226 0.239 0.227 -0.012 25.5 0.0% 19.5% 4.1%
Yordan Alvarez 0.320 0.323 0.311 -0.012 26.3 0.0% 17.1% 5.5%
Ryan O’Hearn 0.277 0.291 0.279 -0.012 27.8 0.0% 20.1% 4.4%
Trevor Larnach 0.338 0.314 0.302 -0.011 26.5 0.0% 15.9% 4.0%
Mitch Garver 0.320 0.318 0.307 -0.011 25.6 11.2% 0.0% 1.6%
Dom Nunez 0.258 0.225 0.214 -0.011 25.3 0.0% 17.2% 2.3%
Dominic Smith 0.298 0.320 0.310 -0.011 26.2 0.0% 14.6% 4.0%
Bobby Bradley 0.263 0.292 0.282 -0.011 27.0 0.0% 20.3% 6.8%
Bryce Harper 0.359 0.346 0.336 -0.011 27.8 0.0% 16.1% 4.6%
Gregory Polanco 0.258 0.291 0.280 -0.010 27.2 0.0% 16.2% 2.6%
Michael Conforto 0.276 0.305 0.295 -0.010 27.0 0.0% 16.6% 4.5%
Jason Castro 0.310 0.345 0.335 -0.010 26.6 0.0% 17.2% 8.0%
Joey Gallo 0.246 0.250 0.241 -0.010 27.4 0.0% 21.0% 5.2%
Group Avg 0.274 0.292 0.279 26.1 16.6% 18.7% 3.9%
Full Dataset Avg 0.293 0.291 0.295 27.0 2.2% 4.4% 6.1%

As a reminder, I used a minimum balls in play of 75 for my list, as that equates to roughly a month of at-bats. I chose to cut this list off to hitters whose PxBABIP was at least .010 lower than SxBABIP, which means a .300 SxBABIP, but a .290 or lower PxBABIP.

Let’s once again focus on the bottom two rows of the table, which is the group average, or the average for the hitters in the table weighted by balls in play, and my 2021 dataset of hitters weighted by balls in play. The group’s actual BABIP sat at just .274, well below my dataset average of .293. This time both xBABIP equations overshot the group’s BABIP, but my .279 mark was significantly closer than the SxBABIP of .292. In fact, the group’s SxBABIP was actually slightly higher than the SxBABIP of my dataset! That’s shocking.

Now let’s discuss the four variables I added to my PxBABIP equation. The full dataset avg for Sprint Speed is 27.0 ft/sec. However, this group was far slower than that, clocking in at just 26.1 ft/sec. That’s a sizeable difference and big reason why the group’s PxBABIP is so much lower than SxBABIP, even though the latter is supposed to account for speed.

Moving along to the Pull Shift IF Alignment GB As R% and Pull Shift IF Alignment GB As L% variables, you might notice something. Of the 38 hitters in the table, only five of them hit exclusively from the right side, while one of them is a switch-hitter! Counting the switch-hitter as a lefty since he records most of his PAs from that side of the plate, that means that 33 of the 38 batters are left-handed! Hmmmmmmmm, ding, ding ding. More proof that SxBABIP is missing a significantly important factor.

Both the right-handers and left-handers pulled their grounders into the shift at extraordinarily high rates compared to the dataset avg of their handedness. This is essentially your who’s who of slower, left-handed power hitters. In past years, you would have seen names like Chris Davis, David Ortiz, and Ryan Howard. If it wasn’t enough to pull such a high rate of grounders into the shift, the group also hits their grounders the opposite way less frequently than the dataset average. What happened to using the whole field?!

Let’s now discuss some of those guys at the top of this list.

Luke Voit sits head and shoulders above the rest. Would you ever believe that he was deserving of a .333 BABIP?! He’s your prototypical slow-footed left-handed power hitter and his metrics all back that up. My equation undershot his actual BABIP slightly, but man was it closer than the SxBABIP. What’s interesting about Voit is that is actually the first season in which the PxBABIP was closer than SxBABIP to his actual BABIP. His SxBABIP was almost spot on in 2020, while PxBABIP was too low, while he demolished both equations in 2018 and 2019. The sample size still seems a little too small to make any confident claims, but perhaps he’ll prove to be a consistent outlier.

This is the first season since 2016 that Yasmani Grandal did not outperform both xBABIP equations. His pulled grounders into the shift rate has been creeping up and has rocketed from 3.1% in 2017 to its current 20.7%, rising each season. It’s really had to continue to outperform SxBABIP while pulling so many grounders into the shift with his bottom tier speed. He remains an OBP monster, but if his BABIP remains low, it’s going to bring down the OBP as well, of course.

Kole Calhoun posted the highest Pull Shift IF Alignment GB As L% on the list, all the while his Sprint Speed dropped to the lowest mark of the Statcast era, and probably lowest of his career, but no way to confirm. This time SxBABIP ended up closer than PxBABIP, but five of the previous six seasons, PxBABIP was closer. Calhoun has not had a history of consistently underperforming or overperforming PxBABIP, so he’s a major BABIP risk for his first year with the Rangers.





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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bcpkid
2 years ago

Voit’s a righty, no?