Aaron Judge’s Odd, Not-Quite-Damning Feat
On Monday, CBS Sports’ Chris Towers took to Twitter to put a bit of a scare into Aaron Judge’s hype-men and -women, posting the following set of images:
*whispers* less than most people are pretending pic.twitter.com/fUsNuMfRTn
— ADP Boy (@CTowersCBS) March 12, 2018
Frankly, I loved it. This word is overused, but, alas: it certainly “triggered” some of his followers. Folks were quick to defend Judge, shielding him from the likenesses he shares with Chris Davis that might, in some version of the near future, manifest in a similar player trajectory. Granted, much of praise for Judge was warranted; an additional 8 percentage points of walks elevates his floor a bit higher than Davis’. Fact of the matter, though, is Judge’s power has few modern comps — namely, Davis, Giancarlo Stanton, Ryan Howard. And I’ve compared Stanton to Howard more than once, so the four of them share the same curiously constructed boat.
What caught my attention, though, aside from the similarities between Judge and Davis that are far more striking than most are willing to admit, is, perhaps surprisingly, the doubles column. In Davis’ monster 2014 campaign, he hit 17 more extra-base hits than Judge did in 2017. Seventeen! That’s no small number. And it got me thinking: a ratio of 52 home runs to 24 doubles is actually kind of alarming. They (whoever “they” are) say a season with lots of doubles but fewer home runs than expected portends more power in the following season. Testing that wisdom, conventional or not, is outside the scope of this post. What I’d rather test is the inverse: does a season with lots of home runs but few doubles (or, generally, other extra-base hits) portend less power?
I looked at all (1,455) qualified hitter-seasons of the last decade and calculated the ratio of home runs to extra-base hits (HR/XBH, henceforth). The top and bottom of this list looks as such:
Season | Name | HR/XBH |
---|---|---|
2013 | Adam Dunn | 69% |
2012 | Adam Dunn | 68% |
2012 | Curtis Granderson | 66% |
2017 | Joey Gallo | 66% |
2017 | Aaron Judge | 66% |
2015 | Nelson Cruz | 66% |
2016 | Todd Frazier | 66% |
2017 | Giancarlo Stanton | 65% |
2015 | Albert Pujols | 65% |
2017 | Edwin Encarnacion | 64% |
2011 | Michael Bourn | 4% |
2008 | Ryan Theriot | 4% |
2010 | Chone Figgins | 4% |
2014 | Nori Aoki | 3% |
2014 | Adeiny Hechavarria | 3% |
2014 | Adam Eaton | 3% |
2012 | Ben Revere | 0% |
2011 | Jamey Carroll | 0% |
2010 | Elvis Andrus | 0% |
2010 | Nyjer Morgan | 0% |
The trend is easily identifiable because it’s inherent to the calculation: powerful dudes top the list whereas powerless dudes bring up the rear. What’s less easily identifiable — but I won’t doubt you started to think about it, because you’re smart, you — is the column I deliberately omitted from the table that brings me here in the first place:
Season | Name | HR/XBH | BABIP |
---|---|---|---|
2013 | Adam Dunn | 69% | 0.266 |
2012 | Adam Dunn | 68% | 0.246 |
2012 | Curtis Granderson | 66% | 0.260 |
2017 | Joey Gallo | 66% | 0.250 |
2017 | Aaron Judge | 66% | 0.357 |
2015 | Nelson Cruz | 66% | 0.350 |
2016 | Todd Frazier | 66% | 0.236 |
2017 | Giancarlo Stanton | 65% | 0.288 |
2015 | Albert Pujols | 65% | 0.217 |
2017 | Edwin Encarnacion | 64% | 0.271 |
2011 | Michael Bourn | 4% | 0.369 |
2008 | Ryan Theriot | 4% | 0.339 |
2010 | Chone Figgins | 4% | 0.314 |
2014 | Nori Aoki | 3% | 0.314 |
2014 | Adeiny Hechavarria | 3% | 0.323 |
2014 | Adam Eaton | 3% | 0.359 |
2012 | Ben Revere | 0% | 0.325 |
2011 | Jamey Carroll | 0% | 0.332 |
2010 | Elvis Andrus | 0% | 0.317 |
2010 | Nyjer Morgan | 0% | 0.304 |
Note the batting average on balls in play (BABIP) marks. The company Judge shares is not comprised of slouches. Some have their flaws, but all were, and some still are, wonderfully productive hitters. And those 10 seasons with the highest HR/XBH marks combine for a .272 BABIP. Meanwhile, the 10 lowest HR/XBH marks combine for a .330 BABIP. It’s almost a perfect inverse, centered around the league-average. The top- and bottom-30, respectively: .277 and .319. The top- and bottom-50, respectively: .278 and .320. The trends are entrenched at the extremes where Judge resides.
There’s more going on here than I’m letting on. The guys with the lowest HR/XBH percentages are fast dudes. They routinely outperform the league-average BABIP because of their speed and batted ball profile. Likewise, powerful guys routinely underperform because of their respective batted ball profiles, which often involves a few too many pop-ups and easily fieldable fly balls. And then there’s Judge in the middle with a stable line drive rate (LD%) and relatively few pop-ups, indicating a fairly shallow launch angle for a hitter like him. He should be exempt from wherever I’m going with this, no?
Indeed, it might be possible Judge isn’t a true-talent .357 BABIP hitter. Who knew! “His hard contact should buoy his BABIP!” Well, it could, except Davis’ didn’t. After a .336 BABIP in 2013 — not to mention a career .335 BABIP through his first 2,317 plate appearances, prior to 2014 — he has since generated a .287 BABIP. It’s nothing catastrophic (although it kind of has been), but it’s nowhere near a commendable BABIP, let alone an elite one.
Let’s not limit the comparison to Davis, though. There’s three possible paths that Judge can take, ranked in order of the probability that I think each will transpire:
1) He sustains his HR/XBH.
The hitters who sustain(ed) insane HR/XBH include most of the guys named in the tables above: early-career Howard, Davis, Adam Dunn, late-career Albert Pujols, late-career Dan Uggla, Edwin Encarnacion, Mark Reynolds, Nelson Cruz. (There are more, but not those who did it as consistently at such an extreme.) The early- and late-career descriptors here are critical, and you know exactly why. The 2008 season was Howard’s last as an extreme HR/XBH guy; I don’t know if it’s a leading indicator, but the 2009 season was Howard’s last that mattered. Late-career Pujols is a shell of his former self. Encarnacion and Cruz are fantastic hitters, and Dunn, Uggla, and Reynolds had their moments prior to the juiced ball era. There are some really nice BABIPs scattered in there — a .350 from 2015 Cruz, that .336 from 2013 Davis, the surprising .343 from Reynolds last year. But out of 55 player-seasons shared among them, only 16 of 55 rank certifiably above-average, and they combined for a .288 BABIP.
Also, is Judge’s batted ball profile really that much better than those of his contemporaries? Have you looked at Dunn’s batted ball distribution recently?
Name | BABIP | LD% | GB% | FB% | IFFB% | HR/FB | Pull% | Hard% |
---|---|---|---|---|---|---|---|---|
Aaron Judge | 0.348 | 21.0% | 34.9% | 44.1% | 5.4% | 33.3% | 42.0% | 45.7% |
Adam Dunn | 0.281 | 20.1% | 34.1% | 45.8% | 7.0% | 21.6% | 47.2% | 37.8% |
(Note: Upon further scanning of the data, Carlos Pena and Chris Carter popped out as hitters who would compare well by sustained HR/XBH. Not that that should make you feel better. But Khris Davis was there, too, so, that helps?)
2) His HR/XBH slips because he hits fewer home runs.
While I think Judge’s profile is unique enough that he’ll continue to resemble the game’s premier sluggers of new and old, it’s also likely he doesn’t hit 40, let alone 50, home runs next year. (Technically, paths #1 and #2 are not mutually exclusive — he could sustain his HR/XBH while also hitting fewer home runs.) So, let’s assume Judge’s 66% HR/XBH was an abberation. Who else had random spikes in HR/XBH recently? Alex Rodriguez, Carlos Gonzalez, Curtis Granderson, Stanton (kinda), Josh Hamilton (hold on, trying to keep this more modern), Mark Trumbo, Miguel Cabrera, Mike Moustakas, Todd Frazier, Yonder Alonso.
A-Rod’s career tanked the year after his HR/XBH spike. So did CarGo’s after 2015. And Miggy’s, too, for all intents and purposes, after 2013. The Grandy Man has yet to replicate his peak 2012 season, and both Trumbo and Frazier failed monumentally to replicate their 2016 seasons. The jury remains out on Moustakas and Alonso, but folks have generally bet against them this preseason, and it seems like it might be wise to do so.
(It’s weird. I didn’t expect to draw a separate conclusion from this — that HR/XBH spikes could serve as leading indicators of decline. But it’s something I’m noticing with alarming frequency, albeit the conclusion is merely anecdotal at this point. I’d like to pursue it further soon.)
3) His HR/XBH slips because he hits more doubles and triples.
I’ll be real with you: this seems unlikely. But if he does accomplish this feat — hitting more doubles and triples without sacrificing home runs — he may quickly earn the title of “best hitter in baseball.” I’m reluctant to buy of this particular stock.
Conclusion
Well, I don’t know. I never know. But I do think it’s unwise to expect an elite BABIP from Judge simply because he hits the ball stupid hard. History tells us not even the game’s best sluggers routinely mustered above-average BABIPs — in fact, they routinely mustered below-average BABIPs. And Judge’s batted ball profile isn’t so special as to exempt him from history’s lessons. As of last night, FanGraphs FANS project Judge for a .329 BABIP. The projection systems are more bearish, settling between .314 and .318. I think the chance it falls below .300 is much greater than the projections let on. If everything breaks the wrong way — if his strikeout rate (K%) climbs, his power craters (even slightly), and his BABIP plunges — we’re legitimately talking about a 2016 Chris Davis redux: 38 home runs, a 33% strikeout rate, and a .221 batting average suppressed by a .279 BABIP. That’s still a top-100 player, but that’s not who you’re paying for, and the batting average drought would feel harder to endure than it should be; it always does.
Psssst: that’s better than Judge’s 20th-percentile Pecota projection — maybe better than his 30th-percentile projection, too. For comparison, he only cracks the 40-homer threshold in his 80th- and 90th-percentile projections. If you think those are exceedingly likely, then you should be just as prepared for what might be a remarkably low floor.
you are basically nah uh ing statcast based metrics by looking at…non statcast metrics. all this says is that judge would be a significant outlier using less granular data.
How predictive is statcast data really, though? i.e. he might have “deserved” his .350 BABIP this year but what are the realistic chances he hits the ball that hard and that well again? I’d guess pretty low.
Do you think at his age he just all of a sudden quits hitting the ball harder than any person alive? Why? I am not someone who is reaching for Judge, at all. But this part I just don’t agree with. He hits the ball harder than anyone ever. Does that mean he will get continued babip success? Maybe not…but that is more based upon shifts, pitches he sees, etc. But to say all of a sudden he won’t hit the ball harder than all other major leaguers seems to be a big jump.
This is pretty dramatic regarding your use of “ever” here. Top average exit velocities in 2015: Stanton (95.9), Sano (94.0), Miggy (93.8). 2016: Cruz (94.5), Stanton (93.9), Miggy (93.6). Judge in 2017: 94.5. To say he hits the ball harder than anyone is false, unless you live exclusively in the vacuum of 2017. And if he did hit the ball harder than anyone else, it wouldn’t be by a substantial margin. Period. Cruz, Miggy, and Stanton — routinely among the highest EVs — combined for a .302 BABIP the last three years. Hard contact does not a BABIP make.
I guess you must like worm burners and weak popups in your averages. Judge averaged 100 mph on all flyballs and line drives last season (1st in MLB). In 2016, Cruz averaged 99.2.
The bottom line: Steamer is projecting a .318 BABIP and an .886 OPS for 2017. Unless you are projecting something substantially different to occur, you’re really not bringing anything to the table.
What are you refuting? We are still talking about a fraction of a mile per hour.
I’m not here to try to convince or beat Steamer. I’m here to try to sway the fans who have projected a .329 BABIP and .966 OPS, which is substantially different than Steamer.
I was talking peak speed.
If Statcast data was perfectly predictive I wouldn’t have bothered. My gripe about living and dying by the Statcast sword is it assumes he’ll have the same batted ball distribution and quality again. It rates the quality of those particular batted balls, but it should not be a foregone conclusion the chips will all fall the same way the next season. He will not hit at the exact same launch angles (both laterally and vertically) nor at the exact same exit velocities. And those minor differences, plus the very nature of random variance not only in outcomes but also in the inputs to those outcomes is enough to not accept Statcast as gospel and to harbor some skepticism.
Besides, if you accept outliers at face value, you will invariably be disappointed more often than not. It is a cognitive bias that the brain has significant trouble overcoming; it fails to properly anticipate regression and significantly overweights low-probability events… e.g., Judge’s 2017.
edit: BFR44 nailed it, by the way, and much more concisely.
What is this straw man argument that he will forever be a .357 BABIP guy? No one really believes that. Yet even if he can put up a .325 BABIP, he’ll likely be a total stud.