Man, I am having a blast with the Baseball Info Solutions batted ball data that was recently added to the batted ball leaderboards. Sure, there are reasons to complain: the batted ball spray and contact quality statistics lack context, leaving you in the dark about how spray and contact intersect. For example, there’s Hard%, and there’s LD%, but how many of a hitter’s balls in play are hard line drives? (You can actually find this data on individual player pages under the “Splits” tab — just not on the leaderboards.)
Just because the available data aren’t as granular as one might wish they were doesn’t make them worthless or unusable. Yesterday, I demonstrated that we can still achieve small gains in our understanding of batting average on balls in play (BABIP) using the new data.
Similarly, we can further our understanding of other aspects of hitting using batted ball data — namely, in the power department. It’s easy to point to Nelson Cruz, who has hit a home run every other game, and say he’ll regress. We can point to his home runs per fly ball rate (HR/FB) of 30.4 percent and compare it to past years. Or we can look at his monstrous .423 isolated power (ISO) and, again, compare it to past years.
Really, a lot of the time, we’re looking to the past for answers. Because a lot of the time, we can’t tell the future. But sometimes the past has no answers for us, either, in consideration of rookie phenoms such as Joc Pederson or any Chicago Cubs infielder who have no prior Major League experience.
Instead of looking to the past, I want to stay in the present. This batted ball data is pretty good, even as is.
So: what makes for a good power hitter? Well, he should hit the ball hard. He should hit the ball in the air, too, so it goes over infielders’ heads or, better yet, over outfield walls. And he should pull the ball, because the shortest distance for a home run to travel is along the foul line.
Not coincidentally, I am discussing this now because those statistics — Hard%, FB% and Pull% — are available to us via FanGraphs’ leaderboards. And, not coincidentally, ISO is moderately-to-highly correlated to all three:
Hard%: R = .698
FB%: R = .596
Pull%: R = .465
Indeed, all three, when regressed against ISO together, explain more than 60 percent of its variance. And, indeed, the regression thinks Cruz’s ISO should be more than 150 points lower, so that’s encouraging. Using this simple OLS regression, I calculated the “expected ISO” of all 2015 qualified hitters. I’m reluctant to call it that — makes it sound like more than it really is, or that I’m blazing new trails here. (Or maybe I am. I don’t know.)
Before I proceed, however, let’s talk limitations. Not all hard-hit balls are pulled, nor are they all fly balls; not all fly balls are hard-hit nor pulled; and not all pull-side balls in play are elevated nor hit hard. Thus, I’m not capturing strictly hard-hit, pull-side fly balls in this model. Still, I don’t know if such balls in play would help explain ISO as much as it would something else, such as HR/FB.
Moreover, not all of these stats have stabilized, per Derek Carty’s research. So a player’s fly ball rate now may not be the same as it will be in July. Same goes for hard-hit rate and pulled balls in play, although I can’t attest to their stabilization rates just yet.
Lastly, it’s possible — nay, guaranteed — that I am omitting potentially important variables. For example, I know that Oppo% and Soft% would contribute to the model’s goodness of fit. But the gains are so small that I’d rather forego them and keep the model lean. (The inclusion of Soft%, for example, affects the coefficient estimate for Hard% but bears almost no influence on the estimates for Pull% and FB%.)
This is a simple exercise designed to provide some quick but reliable estimates, rather than rock-solid projections, that I hope can sway you on struggling sluggers (you’ll see the term “slugger” is used loosely here). Here are the five National League outfielders underachieving their ISOs by the largest margin, rendering them considerable buy-low candidates. Forgive me, as some are more obvious than others.
Batted ball and ISO statistics below exclude yesterday’s games.
Marcell Ozuna, MIA
Ozuna, he of considerable, cheap power last year, has embarrassed his owners thus far to the tune of no home runs and six RBI (although that inflated batting average sure is nice). Yet his batted ball data — insignificantly different from last year’s, by the way — highlights Ozuna as a ticking time bomb, where the bomb is full of home runs, or at least extra-base hits. His .190 xISO is almost identical to his .186 ISO in 2014, so it’s nice to know last year maybe wasn’t a fluke. He’s probably owned in most leagues for his BABIP luck right now; still, he’s a good player to target for an eruption. (The 5-percentage point bump in walk rate is a welcome addition, too.)
Andrew McCutchen, PIT
Pfft, it’s Cutch. No need to worry. Except maybe somebody is worrying. There are simply too many reasons to not give up on him, with the absurdly low .200 batting average on balls in play (BABIP) sticking out like a sore thumb. A .211 xISO is almost a perfect average of his ISO the last four years. It’s nice to see that his batted ball profile supports any claims for him simply experiencing a rough patch, although it would calm my nerves to see him hit a few more line drives. (Can’t help you with the stolen bases, though.)
Nick Markakis, ATL
Twenty-six singles, three doubles, no triples, no home runs. That’s just impresive. I’m not even mad! He’s hitting balls harder but not elevating them; otherwise, the batted ball spray is consistent. A .127 xISO would be his highest since 2012, so I wouldn’t go that far, but he’s hitting balls hard enough to warrant at least a few more extra-base hits.
Curtis Granderson, NYM
Granderson is enigmatic. He continues to walk and strike out at career-best rates and is using all parts of the field in ways unseen since his days with the Tigers. Like Markakis, a .181 xISO would be a three-year high for the Grandy Man; and, like Markakis, he has become less and less fantasy-relevant in recent years. But Granderson’s new plate approach warrants a few more extra-base hits — when he puts the ball in play, at least, and isn’t walking or striking out. I’m reluctant to assume there will be home runs in tow, though.
Ryan Braun, MIL
It’s hard to consider Braun a buy-low guy for power because he’s on pace for something like 30 home runs. But he has zero — zero! — other extra-base hits. I know it’s easy to dismiss Braun who, after serving a suspension for PED use, trudged through an injury-burdened 2014 campaign, but we have to give him credit where credit is due: he is hitting hard-hit balls and fly balls at career-high rates. Like McCutchen, the BABIP woes are an obvious reason to expect a bounce-back, and, like Markakis and Granderson, his .246 xISO would be a three-year high. I anticipate the ISO surge will be more in the form of doubles than home runs, which still bodes well for counting stats. But the home runs are probably legit, too, and while I hesitate to validate a 30-homer pace, I also hesitate to discount what he has already achieved.
xISO seems to overstate the gains (and maybe understate the losses) it anticipates in isolated power, but it’s encouraging to know that it can reasonably guess a hitter’s level of power strictly using current batted ball data while being completely blind to his track record or career history. As aforementioned, this exercise seems most beneficial in regard to rookie and sophomore hitters. Speaking of rookies, I’ll conclude by cryptically validating Joc Pederson’s entire season thus far. After last night’s two-homer performance, he’s due for a good deal of regression — aren’t we all? — but his xISO still ranks fifth of all qualified hitters based on his batted ball profile. Kid’s a monster.