11 Hitter BABIP Surgers For 2018
A year ago, I introduced the latest and greatest version of my hitter xBABIP equation, this time incorporating shift data. Even though it was leaps ahead of any previous iterations and attempts at an xBABIP equation, it still only resulted in an adjusted R-squared of 0.5377. There’s still a whole lot more work to be done here! I would have liked to spend some time doing more research in the hopes of unveiling a further improved equation before the season begins, but alas, I haven’t had the time.
So we’ll stick to the current equation and begin by identifying 11 fantasy relevant hitters whose xBABIP marks sat significantly above their actual BABIP marks. Assuming similar skills driving my BABIP equation, these are the guys who should enjoy a spike. Be careful not to confuse this with a batting average spike, as there’s more to a batting average than just BABIP. Both strikeout and home run rate will affect batting average, so this is just one of the drivers.
| Name | LD% | TFB%* | TIFFB%** | Hard% | Spd | PGBWS%*** | % BIP Shifted | BABIP | xBABIP | BABIP-xBABIP |
|---|---|---|---|---|---|---|---|---|---|---|
| Rhys Hoskins | 23.8% | 41.2% | 4.0% | 46.0% | 3.4 | 1.4% | 9.3% | 0.241 | 0.335 | -0.094 |
| Miguel Cabrera | 27.3% | 32.1% | 0.8% | 42.5% | 1.1 | 1.8% | 8.4% | 0.292 | 0.354 | -0.062 |
| Ian Kinsler | 20.6% | 39.8% | 6.7% | 37.0% | 5.6 | 1.8% | 12.6% | 0.244 | 0.301 | -0.057 |
| A.J. Pollock | 23.3% | 28.1% | 4.0% | 35.0% | 7.5 | 2.7% | 7.3% | 0.291 | 0.343 | -0.052 |
| James McCann | 28.2% | 31.2% | 3.0% | 38.2% | 3.3 | 2.3% | 10.7% | 0.300 | 0.349 | -0.049 |
| Russell Martin | 23.7% | 24.5% | 3.6% | 30.2% | 2.2 | 5.0% | 20.7% | 0.261 | 0.310 | -0.049 |
| Gregory Bird | 17.9% | 46.2% | 5.7% | 36.5% | 1.3 | 16.7% | 72.4% | 0.194 | 0.241 | -0.047 |
| Brad Miller | 16.5% | 33.9% | 2.2% | 38.4% | 4.6 | 5.3% | 36.1% | 0.265 | 0.307 | -0.042 |
| Yasmany Tomas | 20.5% | 31.7% | 0.8% | 41.9% | 2.3 | 2.4% | 9.3% | 0.294 | 0.332 | -0.038 |
| Ryan Braun | 18.9% | 29.0% | 2.9% | 39.0% | 5.3 | 1.5% | 5.2% | 0.292 | 0.330 | -0.038 |
| Nick Castellanos | 24.5% | 37.6% | 0.6% | 43.4% | 4.6 | 4.9% | 21.3% | 0.313 | 0.351 | -0.038 |
| Unweighted Avg**** | 20.3% | 32.3% | 3.4% | 31.9% | 3.8 | 4.9% | 22.3% |
**True IFFB%
***Pull GB While Shifted%
****Averages not weighted by PA and only from my population set of 435
And now you have more context for why I selected Rhys Hoskins 37th overall during last week’s LABR Mixed draft. I noted in my recap that my batting average projection is higher than the rest of the systems, and this is why. While Hoskins is a fly ball hitter, he hit a high rate of line drives, didn’t pop up too frequently, hit the ball ridiculously hard, and rarely got shifted. Perhaps my favorite part of Hoskins’ profile is his contact ability. For a power hitter, a 7.1% SwStk% is fantastic. Sure, it came in a small sample, but he posted single digit SwStk% marks at Double-A and Triple-A as well.
Another day, another “bad luck” list that Miguel Cabrera appears on. What’s important to note is that he has not been a consistent xBABIP underperformer. Since 2012, his BABIP marks have remained rather close to his xBABIP, with the lone exception coming in 2015, when he vastly outperformed his xBABIP. But at this point, I’m not questioning an offensive rebound, I’m just wondering about his health. That’s going to be the determinant of whether he ends up a bargain at his current ADP (94.5).
Ian Kinsler appears to be a bargain this draft season. I’m not entirely sure why, though I’m guessing it has a little something to do with him entering his age 35 season. He has underperformed his xBABIP mark a couple of times, but never anywhere close to this degree. Figure he’ll return to the .280-.300 range.
A.J. Pollock did almost everything right, between hitting line drives, hitting it hard, rarely grounding into a shift, and showcasing his speed. Yet, his BABIP fell below the league average for no reason whatsoever. In fact, his xBABIP was marginally above his 2015 mark when he posted a .338 BABIP and above both his 2013 and 2014 marks when his BABIP was comfortably above .300. He’s a near lock to push that BABIP back over .300.
It’s rare that you see an xBABIP of .349 from a catcher, but that’s exactly what James McCann posted. Don’t get so excited though as it was largely due to an unsustainable 28.2% LD%. How’d he do that and post just a league average BABIP?!
This was Russell Martin’s highest xBABIP since I have been calculating it from 2012. His LD% spiked to the highest mark of his career, and that’s not going to be sustainable. So while his BABIP should rebound off .261, it’s not going to rise all that much, and certainly not to the level of his xBABIP.
Okay, so even Gregory Bird’s xBABIP is terrible. He knocked a ton of fly balls, wasn’t a fan of the line drive, and grounded into the shift often. Of course, literally anyone could predict his BABIP will jump from .194. I figured that he’ll be a bit less extreme this year and hopefully health won’t be holding him back this time.
Brad Miller is one of those that breaks xBABIP. He has underperformed that mark every season since 2013, though this was easily the most dramatic underperformance. I’m more fascinated by the fact that he suddenly discovered home run power in 2016 and then gave it all back, and yet his Hard% actually went up!
Who would have though that slow-footed, power hitting Yasmany Tomas was worthy of a .332 BABIP?! Amazingly, his BABIP skills have remained remarkably consistent, as he has posted xBABIP marks between .332 and .336 each season since his 2015 debut. Oddly, his actual BABIP has been all over the place and in free fall, dropping from .354 to .310 to.294. The humidor certainly won’t help him rebound, but he should still get back over .300.
Strangely, Ryan Braun significantly outperformed his xBABIP marks back in 2012 and 2013, but has now underperformed for four straight seasons. His 2017 marked the most dramatic underperformance, though. His age, injury history, and outfield logjam in Milwaukee scare me enough though that even assuming a BABIP rebound, I’m not too excited.
Man, if you thought I’ve gotten tired talking about Miggy Cabrera on these poor fortune lists, how about the poster boy, Nick Castellanos? But unlike HR/FB rate, Castellanos hasn’t been a consistent underperformer — he actually outperformed in both 2015 and 2016. He owns a near elite batted ball profile with lots of line drives and few pop-ups, so he should remain well above .300 in BABIP.
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.
Hey Mike, there was a lot of talk about Detroit’s tracking software not working correctly last year. Does this impact your thoughts on the two Tigers on this list?
Four Tigers, don’t forget Kinsler’s data for 2017 is from when he was a Tiger. I would think this has to have an impact on the above list.
Saw somewhere it was mostly right field that is off, which makes sense given that Miggy and to a lesser extent Castellanos go that way quite a bit for righties.
Really? Every time I got the Statcast guys involved in the conversation about something off in Comerica, they told me every time that there’s no issue. Where did you read definitively that there was a problem?
Everyone keeps bringing this up because of some Community Research piece that noted the discrepancy between the Tigers’ road and home hard hit %. That’s the only thing but some have accepted it as gospel.
Here is the article, for what it’s worth. Didn’t have time to really read and understand it all yet: https://www.fangraphs.com/community/detroits-batted-ball-readings-are-hot/
Right, that’s not definitive proof of anything. The Statcast guys have read the article and refuted it as weak evidence. Hard% has nothing to do with Statcast and Exit Velocity is a different measurement.
All it proves is that Tigers batters hit a higher percentage of balls classified as “Hard” at home than away. That could just be proof that Comerica is a hitter friendly venue, not that there’s any measurement error.
The article uses FG Hard %, which is not calculated using Statcast data or exit velocity. According to the FG glossary, quality of contact statistics are calculated via a BIS proprietary algorithm using a variety of inputs (landing spot, hangtime, etc.).
That being said, I’m going to stand by the data presented in the piece, as it was all taken from this website. I’d also tend to think that the BIS inputs are park agnostic, so I’m not sure if Comerica being hitter friendly would impact it. To be clear again, not sure why the Statcast team would call it “weak evidence”, as it doesn’t use their data.
I’m sure you use many inputs in your calculations (which I really enjoy), but I hope this helps clarify to anyone wondering about the article.
We’re mixing things up with my various metrics — before xBABIP, I would generally be talking about Brls/BBE with Tigers hitters and that’s when the Comerica stuff and Hard% article came out. Brls/BBE is Statcast, Hard% is BIS.
I’m still not convinced that a large gap between home and away Home% by itself means there’s an error in the calculation.
Detroit may have a favorable visual background for hitters even if other elements don’t make it a hitter’s park over all. I would think this (if true) could bump up contact quality even if this does not necessarily translate to, for example, a home run friendly park.
Two tigers? Try 4…
Shouldn’t there be a pull penalty and maybe also an extreme low gb penalty (you put in pop ups but non popped up high flies also hurt babip)? I’m especially thinking about Hoskins. I don’t think he ever will be a 330 babip guy, and probably not a 300 babip guy either. He obviously isn’t going to babip 230 but his pull and fly Ball approach will probably suppress his babip just like with Bautista and Dozier. I once did an article about guys with similar pull, fb and iso as Hoskins and the group had a 276 or so lifetime babip. Hoskins babips steadily went down as he rose the minors (A ball over 300, AA 297, AAA 281, majors lower), probably because as you get higher the effect of exit velo gets a little lower and predictable hit patterns hurt more.
Statcasts xBA (unfortunately they don’t have xbabip) also has him at 250 which is lower than his actual ba so they don’t seem to see his babip as that bad luck influenced.
Also I searched last years leader board for th rate under 40 and pull rate over 45 and that group also had a 277 babip (only 12 such guys though)
Please don’t see that post as a disrespect, I’m a huge fan of your posts and especially that fb pull rate is a really nice tool.
Or maybe I’m missing something.
Btw I love Hoskins but I think he will be more a Bautista type with good k-bb rates, great power but babips around 260-270 especially when teams start to realize he needs to be shifted more (72% gb pull rate which is among the highest in the league). If he is a 260 hitter with a 360 on base and 500+ Slg(35 hr) that is a really good outcome.
I think Hoskins has a bit more BABIP potential than Bautista given how hard he hits the ball and how infrequently he pops it up.
Take a look at Hoskins BABIP by batted ball type from last year:
GB: .128
LD: .667
FB: .026
The lowest BABIP on groundballs (min. 300) among hitters in 2016-17 was Edwin Encarnacion at .175. So fair to say Hoskins has upside there. The lowest on flyballs (min. 300) was Todd Frazier at .063, so again strong upside there.
Thanks for this added data!
Hoskins never posted popup rates that low or ld rates that high in the minors, I think both are due to regression.
Also not only his pulled fb rate is high but also his pulled grounder rate is very high and warrants more shifting. Teams will shift him more.
Don’t get me wrong, I do think hoskins babip will improve but at the same time pop up rate, ld rate and hr/fb all likely will regress some.
Hoskins is very good but people just regress his babip to 300 and leave all the good stuff untouched.
In AA and AAA he was 18% liners, 15% pop ups and 20% hr/fb, in mlb he was 23/9/30.
Maybe the hr/fb should get a little juiced ball bump but I think overall 19% liners, 12-14 pop ups and 25 hr/fb is more realistic and he will get shifted more. Will also see different pitch mix.
I don’t think steamer will be all that wrong on him.
You need to halve the minor league IFFB% marks:
https://www.fangraphs.com/fantasy/a-late-primer-on-milb-infield-fly-ball-rate-iffb/
Hoskins always ran respectable marks after doing that. And yes, you’re right about the LD%, which is why I’m projecting some regression there.
His HR/FB rate has nothing to do with his BABIP since homers are excluded. You might say that will reduce his batting average, but it won’t affect his BABIP, unless you want to argue those previous homers are now going to find gloves.
Also I don’t put much stock in minor league LD% because I’m not sure how accurate those are either.
Im aware that homers don’t affect babip but they affect his average. His non in play average last year actually was 290 18hr/18+44ks.
Interesting info about the pop ups, what is the reason?
Still I wonder why your projection is so different from statcast.
Typo meant gb rate under 40
Only pulled grounders matter because they get shifted. A pulled fly ball is actually the best type for BABIP, check my original article, as I posted all the BABIPs.
Non-popped up high flies sounds like bringing in launch angle. That would require a complete overhaul of the equation to use Statcast, which is something I haven’t gotten around to yet. Andrew Perpetua’s formula does incorporate Statcast data, but overall the equation is worse than mine since it’s missing some other necessary components.
I did my own personal analysis on this using Baseball Savant Data about halfway through 2017. I should re-do now that the season is over, but here’s what I came away with:
Detroit Hitter Brl/BBE Home: 11.3%
Detroit Hitter Brl/BBE Away: 8.6%
Tigers hitters did way better at home. Now that could just be a sample size or fluky type thing. So I looked at all hitters:
MLB Hitter Brl/BBE Comerica: 10.5%
MLB HItter Brl/BBE Everywhere Else: 6.3%
Crazy difference. Now some of that could be due to Detroit’s pitchers sucking…
Detroit Pitchers Brl/BBE Home: 9.9%
Detroit Pitchers Brl/BBE Away: 8.9%
Then I thought that oh that might be related to homefield advantage, but it turned out that MLB pitchers as a whole don’t seem to allow more barrels at home than on the road.
All that leads me to believe there is definitely something going on. On top of the anecdotal evidence of seeing guys like John Hicks atop the hard hit leader board.
Comerica seems to inflate both exit velocity and hard%. The two metrics are unrelated as far as I can tell. So there’s little reason to think this is due to measurement error, as others have suggested.
In my view, the more likely explanation is that Comerica simply inflates the actual average exit velocity of a ball off a player’s bat. FanGraphs park factors have always shown Comerica to reduce strikeouts substantially, and the batter’s eye has been described as ideal for the hitter. Perhaps batters see the ball better at Comerica, and hit the ball harder as a result.
Also, I seriously doubt that a single half-season is enough data to come close to any conclusions. Three or more years would probably be ideal.
Hmmm, more Detroit Tigers on these lists. Although it would hurt the sample size, curious if any of these guys make it using away stats only.
Braun’s an interesting target. He actually hit pretty well last year despite the injuries.
Does he have everyday at bats in Milwaukee?
It’s baffling what’s going on there. How could Domingo Santana become their 4th OFer?! I keep assuming a trade is imminent, and yet nothing happens. Yelich and Cain should obviously be safe, but Braun, Santana, and maybe Thames, could be hurt if they don’t make a trade. Then again, I can’t imagine then sitting Braun for a rotation.
Isn’t Braun supposed to play 1st? There is also talk of him backing up at 2nd, which is unbelievably lol.
No, “supposed to” isn’t the correct term. He has apparently been taking grounders at first, but that’s it. The hope would simply be to increase his versatility as they juggle 4 OFers for 3 spots. He’s not just going to take over as the every day first baseman. They still have Thames, who was plenty good enough offensively to play every day against righties.
30 minutes ago Craig Counsell said Braun will play primarily LF and 1st. I’d venture everyone gets regular rest. Something like Cain/Santana/Yelich playing 4 out of 5 to 5 out of 6 games, with Braun spelling them and also playing first base vs lefties and Aguilar out of a lineup spot.
Every time I see a BABIP under-performers list, I find it to be largely peopled by players who are old, slow, and/or were injured. This may just be some sort of observational bias on my part, but is it possible that there is something more in a player’s foot speed (or acceleration) that isn’t being captured adequately?
That’s not what this list is composed of though. 6 of the 11 have below average Spd scores, which is basically a split. And only 4 of them one might consider old!
how did Mookie Betts’ 2017 fare per this analysis? It’s tough to believe he’s anything close to the .268 figure he posted last year true-talent wise.
Perhaps surprisingly, his xBABIP was just .289, the first time it has dipped below .300 since debuting. It was due to a career low LD% and career high True IFFB%. Those are fluky though, so I’d figure a rebound back to .300+