Potential 2nd Half Breakouts Using Statcast xwOBA
While Statcast’s xwOBA is not a predictive metric, it’s still useful in validating historical results. Think of xwOBA in the same way you would think of pitcher SIERA — while SIERA isn’t meant to predict future ERA, it does a better job of it than ERA itself, making it backward-looking. So let’s dive into the hitters whose xwOBA marks are significantly higher than their actual wOBA marks.
I filtered the Statcast leaderboard for hitters who have recorded at least 200 plate appearances and removed those who no longer have an every day job. I also calculated ISO and xISO (SLG minus AVG), as that’s far more meaningful than SLG, which could be inflated by more singles, rather than extra-base hits. I included the additional metrics to find out whether the higher xwOBA is due to a higher BABIP (leading to a better xAVG), more power (leading to a higher ISO), or both. Remember that xwOBA is not park-adjusted.
| Name | AVG | xAVG | Diff | ISO | xISO | Diff | wOBA | xwOBA | Diff |
|---|---|---|---|---|---|---|---|---|---|
| Justin Smoak | 0.217 | 0.262 | -0.045 | 0.201 | 0.258 | -0.057 | 0.340 | 0.393 | -0.053 |
| Danny Jansen | 0.211 | 0.259 | -0.048 | 0.169 | 0.182 | -0.013 | 0.286 | 0.332 | -0.046 |
| Jose Ramirez | 0.218 | 0.248 | -0.030 | 0.126 | 0.157 | -0.031 | 0.284 | 0.326 | -0.042 |
| Jason Kipnis | 0.240 | 0.278 | -0.038 | 0.136 | 0.148 | -0.012 | 0.292 | 0.334 | -0.042 |
| Evan Longoria | 0.238 | 0.270 | -0.032 | 0.201 | 0.230 | -0.029 | 0.322 | 0.363 | -0.041 |
| Jose Martinez | 0.285 | 0.300 | -0.015 | 0.141 | 0.208 | -0.067 | 0.333 | 0.372 | -0.039 |
| Justin Turner | 0.294 | 0.305 | -0.011 | 0.152 | 0.212 | -0.060 | 0.356 | 0.392 | -0.036 |
| Jesus Aguilar | 0.225 | 0.247 | -0.022 | 0.160 | 0.205 | -0.045 | 0.312 | 0.348 | -0.036 |
Justin Smoak is as boring a first base option as it gets. He’s already 32, endured years of disappointing performances, and then finally broke out in 2017, before giving up some of those gains last year. This season, he has declined again and now appears to be just another plodding corner option with above average power and nothing else. Sounds like a skill set that’s a dime a dozen. But Statcast is loving him and doesn’t even know he plays in a ballpark that marginally increases homers. Aside from the Statcast optimism, his walk rate has spiked to a career high, while his strikeout rate sits at a career low. Even his fly ball rate barely sits at a career high. Normally that would suggest a career year, but not here. The only thing missing from Statcast’s xwOBA that is particularly relevant here is Smoak’s penchant for pulling his grounders right into the shift. It means his BABIP might not rebound as strongly as Statcast may believe. Still, he’s a buy and will come cheap.
What’s up with Blue Jays?! Credit the organization for sticking with Danny Jansen all season despite weak production, even though they have gone through a rotating cast of characters at some other positions. Since the end of June, Jansen’s bat has come alive, or should I say his results have improved in a hurry, because Statcast thinks his bat has been decent enough all season. Statcast thinks this is a mostly BABIP thing though, as his xISO isn’t much higher than his actual mark. That’s not as exciting for fantasy owners, but given his combination of strikeout and fly ball rates, along with a low double digit HR/FB rate, he should be solid enough in homers.
We’ve discussed Jose Ramirez ad nauseam here and I speculated about three weeks ago that he was suffering from an increased fly ball launch angle that became too high. Since my post, his fly ball launch angle has risen even higher! He was at 39.2 degrees when I posted and has been at 40.1 degrees since. That doesn’t sound good. The thing is, Statcast literally uses launch angle in its xwOBA calculation since it’s their metric and it still thinks Ramirez has deserved much better fate.
So we open our list with two Blue Jays followed by two Indians? That’s weird. Jason Kipnis has long been forgotten as a fantasy asset, though he does have seven homers and five steals. It’s mostly a BABIP thing here, even though his mark sits at a three-season high. But he’s cut both his fly ball and infield fly ball rates, so he does deserve better. He doesn’t need to be on your shallow mixed league radar, but anything deeper, he could be a reasonable power and speed contributor the rest of the way, while not killing your average as much as he has so far.
Man, what happened to Evan Longoria? This year, his power is back up, so you can’t totally blame AT&T Park, but now his BABIP is at the second lowest mark of his career, despite his highest LD% and second lowest IFFB%! Statcast thinks Longoria has deserved better results in both average and power, though remember that xISO is unaware of his home park. Aside from an increased BABIP and resulting batting average, there’s still not much more upside here.
FREE JOSE MARTINEZ! Seriously, he’s the perfect DH and surely some team in the AL could use a good one. Though he’s been decent enough this year, Statcast loves his power potential calculating an xISO just over .200. Marcell Ozuna’s injury has opened the door again for every day playing time and he needs to be owned in all formats.
Speaking of power upside, Statcast is also loving Justin Turner, who has the second highest xISO versus ISO gap in the table. That’s surprising considering his HR/FB rate is right in line with his past two seasons and 2014. He’s a very safe bet for strong production the rest of the way given his pristine batted ball profile.
Hey Milwaukee Brewers, are ya reading this? Jesus Aguilar has been unlucky! After a breakout full season last year, expectations were naturally high, though we acknowledged some bit of regression was likely. Instead, he completely flopped in April and failed to rebound enough in May, en route to losing his starting job. June was no better and only his three homers so far in July have reminded us of what could have been. Statcast thinks his ISO should be sitting above .200, but is it too late to reclaim the starting first base job? The problem is, Eric Thames also deserves a starting job and is wOBA’ing .367. It’s a tough situation for Aguilar hopefuls.
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.
Mike – where can I find fly ball pull percentage?
Go into the Splits Leaderboard, then click Show All to display all the filters. Click Batted Balls and then check off Flyballs. Then you’ll update the data and click on Batted Balls. The Pull% displayed is going to be your Fly Ball Pull%. Below is the link directly to it.
https://www.fangraphs.com/leaders/splits-leaderboards?splitArr=12&splitArrPitch=&position=B&autoPt=true&splitTeams=false&statType=player&statgroup=3&startDate=2019-03-01&endDate=2019-11-01&players=&filter=&sort=17,1
It’s also insightful switching Flyballs to Groundballs, as the guys on the top are probably being shifted a ton and seeing their BABIPs decline as a result.
A lot of Smoak’s decline is his performance from the right side (ie, against lefties). He’s actually been pretty consistent from the left side the last 3 years but 2017 was by far the best year of his career from the right side. The last 2 years he’s gone back to being a well below average hitter against lefties. Frankly, he looks a lot like a platoon hitter when you look at his career platoon splits and not just the phenomenal 2017.
A healthy portion of the xWOBA under-performance can be attributed to Smoak being one of the slowest runners in baseball (sprint speed 468 out of 472). Slow runners like him, Pujols and Morales routinely under-perform their xWOBA’s by 30-40 points
Jose Martinez under-performs his xWOBA because he hits more more central FB instead of pulled FB and AFAIK statcast doesn’t yet distinguish between those. He has a career xWOBA under-performance of .035 (.358 WOBA vs .393 xWOBA) in over 1180 PA’s. He’s also a slow runner although not to the same extent as Smoak.
I have a comment about this elsewhere in the comments. The shorts is that speed particularly HP-1B times explains only about 12% of the difference between woba and xwoba but at a very high >99.9% confidence level.
Mike – how do you feel about xwOBACON?
No differently than xwOBA. xwOBA isn’t recalculating what walk and strikeout rates should be, so if the actuals are being used, then xwOBACON isn’t going to give us any additional insight into performance than xwOBA.
Mike, I don’t know if you are aware of the speed penalty for wOBA-xwOBA, but I did a research project on my own over the off season and I discovered that HP-1B explains approximately 12% (R Squared of .1199) of the wOBA-xwOBA variance with a very small p-value of 0.0000000001. This is more much more than sprint speed alone, which explains approximately ~7.5% (R Squared of ,0766) of the variance which has a lower but still significant p-value 0.000001. Based on my regression Aguilar who is significantly slower in his HP–1B time this year (4.97 this year vs 4.68 last year) can expect a wOBA penalty of approximately -0.0153 off his xwOBA. Justin Smoak likewise can expect a wOBA penalty of approximately -0.0132 off his xwOBA (Smoak’s penalty is based on 2018’s HP-1B time because he does not have a 2019 HP-1B number listed on Statcast.
I wasn’t aware of the specific penalty, but I definitely know the missing variables in xwOBA. They are similar to the variables I made sure to include in my xBABIP formula — speed and pulled grounders into the shift. I would imagine if a study was done, guys who frequently pull their grounders underperform their xwOBA and those who pull their grounders least outperform.
I haven’t had time to do a study for grounders pulled into the shift but I think it might actually explain a fair amount of the variation as well. I went for speed because it seemed like the lowest hanging fruit. What amazed me the most about the speed penalty was just how small the p-value was despite only explaining only 10 – 15 % of the variance.
It should be pretty easy to incorporate both pulled grounders and speed into xwOBA. Wonder if they have plans to do so in the future. Would significantly improve it so we don’t always have to mention that caveats and ignore the slow-footed catchers annually underperforming.
I would think that speed would be the harder of the two because it relies on getting a meaningful sample from each player which could take a half a season or more. This might cause their individual xwOBA formula to fluctuate with in the season and from season to season. Whereas grounders pulled into the shift can be calculated a league lever or across all of MLB.
I definitely don’t think it would take very long for speed to stabilize. They already have the Sprint Speed leaderboard, I’m sure using that would boost xwOBA’s usefulness immensely.
What about straight flyballs? I read an article somewhere, possibly here on fangraphs, about how more valuable pulled fly balls are then straight ones. It broke up the pull/opp fields into five territories and the middle flies were by far the least effective in both average and power
Not sure the exact components of xwOBA, don’t know if it’s just launch angle and exit velocity or they consider horizontal angle as well.
They don’t consider the horizontal angle. The aforementioned article pointed out that Castellanos hit the majority of his flyballs to the middle quintant and that explains why he underperformed his xStats last year.
I wonder if Jose Martinez does the same as he is another perennial underperformer with a high cent%
@Morbo This is excellent info. Thanks for sharing.
I had to choose between cutting Smoak and Jose Martinez right before the all-star break and I chose JM. The tiebreaker was that I realized Jose perenially falls short of his statcast numbers. His xSLG is actually at its lowest of his career (besides his very brief rookie season).
I would be very interested to see an article on what he is doing that xStats is failing to capture.
This is a really slow group outside of Ramirez. Actually Ramirez isn’t exactly fast either just a good base stealer. That’s usually the trend among xwOBA laggards so probably not just bad luck on most of these guys
Speed shouldn’t have much of an effect on xISO though, only batting average.
Speed allows for a lot of doubles to become triples and singles to become doubles.
For this reason, xwOBA not only overestimates xBABIP for slow players, but also xISO.
In my comment elsewhere I singled out Aguilar and Smoak for speed based penalties. The remaining names on the list either have 0 expected speed penalty or should have positive expected speed based adjustment to their xwOBA. Justin Turner is actually faster this year in his HP-1B metric than last year.
Question: assuming identical speed, which hitter has more raw power?
a) 0.200 BA, 0.400 SLG
b) 0.300 BA, 0.500 SLG