Early 2018 Hitter Blind Résumés

I’ve done this before — compare similar players, one of whom is “name-brand,” the other “generic-brand,” using blind résumés — as have many others. Ben Kaspick carried the torch a while this year, but he credited Joe Douglas with the idea. So let’s say it’s a group effort to which I’ll contribute once again.

In anticipation of 2018 drafts, I wanted to carry out a “buying generic” style of analysis, borrowing in part from too-early mock draft average draft position (ADP) data. I do not intend to construe the following comparisons as rigorous analysis. I do, however, intend to highlight some potential bargains that, if the too-early mock ADP information is concerned, warrant your attention on draft day.

Comparison #1: Outfielders

2015-17 Stats per 650 PA
#10 98 23 91 23 11.5% 8.2% 0.292 0.350 0.492 0.357 122
#67 106 20 71 35 14.2% 7.8% 0.293 0.351 0.483 0.357 118

The #67 pick in too-early mocks is nearly identical to a 2018 sure-fire first-rounder. Aside from some distinct differences in counting stats — the RBI just weren’t there for the latter, who plays for a worse team, arguably — but they are otherwise perfect clones of each other since 2015. That’s before getting to #67’s significant advantage on the bases.

Under the hood, #10’s peripherals are a bit better, as they should be, given he’s a first-round lock. Although their hard-hit rates (Hard%) are nearly identical, #10 hits fly balls almost 10 percentage points more often than #67, giving him a slight power edge. #10 also makes a little more contact overall and chases a little less frequently, as evidenced by his slightly superior plate discipline metrics (K%, BB%). Conversely, #67 is a line drive guy, affording him an advantage in batting average on balls in play (BABIP), which brings their batting averages almost perfectly in line.

The catch? #67 has seen roughly half as many plate appearances the last two years. You could call him an injury risk, sure; he missed all of 2016 due to what I’d consider a fluky injury, but he also missed 50-odd games to a groin strain last year, and those are no joke. Oh, and he’s almost five years older. So, maybe they’re not quite the same.

But maybe they are.

#10: Mookie Betts
#67: A.J. Pollock

Comparison #2: Outfielders

2017 Stats, Straight Up
#37 635 100 35 109 14 28.3% 11.7% 0.341 0.273 0.361 0.540 0.378
#80 607 88 30 85 15 29.3% 12.0% 0.363 0.278 0.371 0.505 0.372

The #37 pick in too-early mock drafts has floated around this draft slot for the better part of a decade (or at least the last half-decade — I can’t confirm pre-2012). He gave us a scare in 2016 but otherwise has been the same guy as always: lots of home runs, lots of counting stats, lots of strikeouts, a handful of stolen bases. #80 is a new guy, a hot-shot, who had some believers prior to last year but entered the 2017 season with doubts about if he could take the next step. (He did, FYI.)

While their 2017 outcomes were similar, their peripherals deviated dramatically. #37 hit way more fly balls (by 16 percentage points). However, #80 hit way more line drives (by 8 percentage points). Couple that with a microscopic pop-up rate, and it’s no wonder #80 had one of the league’s best BABIPs. It’s easy to say he’ll regress, but I’m not so sure he will — his swing screams high BABIP, although it could sink closer to the .330-to-.340 range (still considerably higher than league average). #37, meanwhile, doesn’t exude those skills and actually leaves a lot of questions marks hanging around if he can pull off another superior BABIP; I’m not convinced he can. However, I’m also not convinced #80 will hit home runs on 31% of fly balls again, either, leaving them both vulnerable to regression. #80 also has a little more swing-and-miss to his game, but not significantly or detrimentally so, especially in this day and age of high-power, high-strikeout hitters.

Mentally applying these adjustments manifests two 2018 hitters who are pretty different from their 2017 selves. #37 might be a 30-homer, 10-steal, .250-average hitter, whereas #80 might be a 20-plus-homer, 12-steal, .260-average hitter. Accordingly, you may draft #80 closer to his ceiling than his mean output, but that quintessential “upside” suggests you may also draft a juggernaut. Such is the case when dealing with a relatively unknown (yet intriguing) quantity.

#37: Justin Upton
#80: Domingo Santana

Comparison #3: First Basemen

2017 Stats per 650 PA
#26 103 46 115 12 26.6% 11.7% 0.315 0.299 0.267 0.352 0.581
#90 99 72 135 0 27.8% 10.2% 0.392 0.238 0.259 0.352 0.651

OK, so 72 home runs seems a little unreasonable. It would be unfair to omit that #26 recorded 548 PA, whereas #90 recorded only 216 PA. Regardless, both hit home runs at absurd rates, clearly — one more absurd than the other, perhaps, given his HR/FB mark stood at an irresponsibly high 41.4%. But that will inevitably regress, as will the BABIP — two forces tugging in opposite directions on his batting average. (It probably won’t move much.) Looking at everything else in their batted ball profiles, there’s reason to favor #90, specifically for his robust pull rate (Pull%), but otherwise the differences in their batted ball profiles are negligible. #26 carries some slight advantages in fly balls (FB%), hard hits (Hard%) and pop-ups (IFFB%), but they’re not enough to swing the pendulum firmly in his direction. Moreover, their plate discipline peripherals paint nearly identical pictures: matching 13.5% swinging strike rates (SwStr%) and almost-matching swings rates in the zone (Z-Swing%) and out of the zone (O-Swing%). At this point, we’d need to consult Baseball Savant/Statcast for the most granular distinctions between the two men: Statcast favors #90 in almost every metric, although such a distinction carries the sample size caveat from earlier. The competing interpretations of Statcast and Baseball Info Solutions (BIS) data (the latter of which informs FanGraphs’ batted ball metrics) make this a bit of a toss-up in a vacuum.

I’m wary to give #90 the edge due to small-sample concerns, I already plan to make a bold prediction that he will out-earn #26 next season despite playing for a significantly worse team. The turbulent offensive profiles for each of these hitters could make for rocky, possibly disappointing, seasons; I won’t be surprised to learn, in October, that each was overrated in the preseason. With such risk in mind, perhaps it’s better to incur it later in the draft with #90. I’m almost confident I won’t change your mind on #26, though, so this is probably futile.

#26: Cody Bellinger
#90: Matt Olson

We hoped you liked reading Early 2018 Hitter Blind Résumés by Alex Chamberlain!

Please support FanGraphs by becoming a member. We publish thousands of articles a year, host multiple podcasts, and have an ever growing database of baseball stats.

FanGraphs does not have a paywall. With your membership, we can continue to offer the content you've come to rely on and add to our unique baseball coverage.

Support FanGraphs

Currently investigating the relationship between pitcher effectiveness and beard density. Two-time FSWA award winner, including 2018 Baseball Writer of the Year, and 5-time award finalist. Featured in Lindy's Sports' Fantasy Baseball magazine (2018, 2019). Tout Wars competitor. Biased toward a nicely rolled baseball pant.

newest oldest most voted

Loved the Bellinger vs. Olson. Bellinger will probably be a headache to value as are most big breakouts. I know Rhys Hoskins probably doesn’t work a well for the blind resume, but what’s your feelings between the three (Hoskins, Bellinger, Olson)


All three of them at least have a solid approaches, will take walks but will also swing and miss. Hoskins should have the lowest K rate of the three giving him the advantage. Since all three will hit in the middle of their respective lineups, I’ll give the slight edge to Hoskins. He has the friendliest park of the three but Bellinger has the better lineup around him.