Power Leaderboards, With the Old Ball

Are you nostalgic for 2015? There are a few reasons to be, not the least of which is Fetty Wap’s Billboard dominance. Or, you know, when home runs were more rare. Back when the ball’s seams were probably higher and balls didn’t travel as far. Back when hitting 20 homers by the half was tough to do, and there weren’t 24 players that had done so by the All-Star Game.

What if we could go back?

Well, thanks to the help of Andrew Perpetua, we might be able to. We know, for example, that exit velocity is up for the entire league since May 2015 (1.02 mph). We also know that the league is hitting the ball about 5.6 feet further on average, probably reduced to less drag on a ball with lower seams. The ball is going farther at the same exit velocities.

In order to put these two effects together, we can express that extra distance in exit velocity again, just for the purposes of this exercise. In other words, what sort of exit velo would produce 5.6 extra feet? Turns out it’s another 1.11 mph. Then we can take the summed exit velocity changes (2.13 mph) and remove those from 2017’s balls in plays. Then we take those de-juiced numbers and run them through the same rubric that powers Perpetua’s xStats metrics to get an expected line. An expected line for this year given those adjusted inputs.

All of this is to answer the question: how would the league’s bats look if they were hitting the 2015 ball?

Here are the top 20 in expected home runs given the same kind of exit velos and batted-ball distances we saw back in the halcyon days of May 2015.

De-Juiced Ball Home-Run Leaderboard
Aaron Judge 366 0.272 0.405 0.570 0.421 29.9 19.0
George Springer 378 0.299 0.375 0.603 0.408 26.3 18.9
Joey Votto 384 0.313 0.428 0.615 0.443 28.4 18.5
Khris Davis 368 0.235 0.329 0.497 0.352 23.9 16.9
Joey Gallo 291 0.184 0.309 0.472 0.346 23.6 16.2
Miguel Sano 345 0.245 0.343 0.510 0.362 21.3 16.2
Paul Goldschmidt 381 0.310 0.428 0.577 0.428 23.7 15.8
Giancarlo Stanton 369 0.262 0.348 0.512 0.372 21.4 15.6
Cody Bellinger 292 0.257 0.340 0.558 0.385 22.6 15.3
Salvador Perez 315 0.279 0.308 0.550 0.358 19.9 14.9
Justin Smoak 333 0.282 0.350 0.547 0.383 23.7 14.8
Eric Thames 329 0.233 0.365 0.502 0.374 21.9 14.5
Adam Duvall 355 0.260 0.305 0.522 0.351 21.2 14.5
Manny Machado 365 0.269 0.331 0.516 0.366 21.0 14.2
Matt Davidson 257 0.215 0.255 0.488 0.306 18.6 14.2
Nolan Arenado 388 0.307 0.356 0.570 0.386 20.4 14.1
SOURCE: xStats.org
xStats here are with batted ball stats adjusted to early 2015 levels
xHR = expected homers with the early 2015 adjustment to the batted ball stats
yHR = expected homers with current-batted ball stats

There’s quite a bit of data here, but it’s actually not that bad. All the metrics preceded by an -x- represent an estimate of the relevant player’s numbers were he facing the old ball. The one metric preceded by a -y- (yHR) represents the player’s expected home-run total for the current season (under 2017 conditions, that is).

According to the numbers, last night’s Derby winner has earned every one of his home runs this year. Aaron Judge leads the majors with 30 long balls. The batted-ball data suggests he “should have” hit 29.9 so far. Basically identical.

Applying the 2015 adjustment, however, we see that Judge would have “only” put up 19 homers by this point. That’s still impressive for a rookie, obviously. But he likely wouldn’t be on pace for 60 homers. And he’d have ranked only 13th in 2015’s actual results.

Aaron Judge would have “only” hit 19 homers with the old ball. (Photo: Arturo Pardavila III)

This is probably also where the caveats should come in. We left park factors out of this, because they were impossible. We’re applying a blanket adjustment even though Judge seems to have shown us that he hits the ball harder than anyone ever born. This is not science.

But it is a way to try and imagine today’s game, which now sees at least 25% more home runs per fly ball than it did in early 2015, with yesterday’s ball. Imagine a season in which Joey Votto might just hit 30 or so home runs, it’s easy if you try. Imagine a season in which Miguel Sano is beasting, but has 16 homers right now.

Jeff Sullivan showed us that this batted-ball boost has most benefited the “middle class of would-be power hitters.” Can we look at the players with the biggest deltas between their current expected homers and their early 2015 version, and put some faces and names to that class of player? Yes, we can.

The Biggest Gainers With the New Ball
Name PA xAVG xOBP xSLG xOBA yHR xHR Delta Delta/yHR
Andrelton Simmons 369 0.283 0.335 0.410 0.327 8.2 3.8 4.4 0.54
Nick Markakis 376 0.278 0.360 0.389 0.335 5.0 2.4 2.6 0.52
Gerardo Parra 172 0.336 0.359 0.529 0.384 6.5 3.2 3.3 0.51
Orlando Arcia 317 0.257 0.297 0.376 0.295 5.7 2.9 2.8 0.49
David Freese 262 0.243 0.377 0.376 0.346 6.2 3.2 3.0 0.48
Michael Brantley 278 0.317 0.378 0.475 0.370 5.9 3.1 2.8 0.47
Scooter Gennett 226 0.277 0.330 0.473 0.347 11.2 5.9 5.3 0.47
Jason Kipnis 283 0.250 0.308 0.412 0.310 8.1 4.3 3.8 0.47
Josh Harrison 368 0.269 0.351 0.409 0.337 9.6 5.1 4.5 0.47
Yunel Escobar 303 0.297 0.354 0.428 0.340 6.2 3.3 2.9 0.47
Victor Martinez 286 0.264 0.332 0.386 0.320 6.5 3.5 3.0 0.46
Asdrubal Cabrera 256 0.278 0.358 0.434 0.348 7.4 4.0 3.4 0.46
Yangervis Solarte 289 0.275 0.356 0.392 0.337 6.1 3.3 2.8 0.46
Yasmani Grandal 280 0.254 0.307 0.412 0.313 9.6 5.2 4.4 0.46
Derek Norris 198 0.208 0.264 0.351 0.267 5.9 3.2 2.7 0.46
Stephen Vogt 199 0.230 0.298 0.392 0.305 7.0 3.8 3.2 0.46
Maikel Franco 347 0.272 0.324 0.419 0.324 9.7 5.3 4.4 0.45
Alex Bregman 329 0.275 0.357 0.445 0.352 9.3 5.1 4.2 0.45
Tyler Flowers 224 0.274 0.372 0.393 0.341 5.1 2.8 2.3 0.45
Jordy Mercer 332 0.267 0.347 0.398 0.338 6.0 3.3 2.7 0.45
SOURCE: xStats.org
Delta/yHR = Difference between expected homers divided by 2017 xHR

Here, we divided the difference between the two expected homer totals by 2017’s expected homers. We did this because we wanted to adjust for sheer quantity. Hit tons of hard fly balls and you’ll gain more from the ball, but who’s really gaining the most per opportunity?

Here, the names really pop out at you. When you hear an announcer bemoaning the fact that middle infielders are hitting 20 homers a year now, left and right and willy nilly, they’re talking about Andrelton Simmons and Scooter Gennett. And Yunel Escobar. And Jordy Mercer. Even if those guys don’t all hit the 20-homer threshold this year, they’ll end up closer to it than seems right to some.

There might be better ways to do this, but then again, maybe it shouldn’t be done better. After all, these things don’t happen in a vacuum. For example, the average league-wide launch angle is up about a degree, perhaps as a response to the shift, or because the ball started flying. Who knows. You see the ball fly better, you adjust to take advantage of it.

In other words, we can’t go back. This is where we are now. But we can wonder what things would be like, if the ball acted like it used to.

We hoped you liked reading Power Leaderboards, With the Old Ball by Eno Sarris!

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With a phone full of pictures of pitchers' fingers, strange beers, and his two toddler sons, Eno Sarris can be found at the ballpark or a brewery most days. Read him here, writing about the A's or Giants at The Athletic, or about beer at October. Follow him on Twitter @enosarris if you can handle the sandwiches and inanity.

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If the ball has lower seams, then shouldn’t that be resulting in less break on breaking balls? idk if it is or not – but any change in the ball should also change the way it moves when pitched – not just the way it moves when hit.

Aaron Judge's Gavel
Aaron Judge's Gavel

“As for how the changes to the ball might affect pitches, we still don’t know. Hurlers generate movement on breaking pitches by orienting the seams to spin the ball. If those seams are lower, as Lindbergh and Lichtman found, pitchers could be less able to grip the way they’re used to. It’s difficult to see any effect on pitch spin in the data, however, and according to Nathan, for a given amount of spin, small changes in seam height aren’t likely to change the aerodynamic properties of breaking balls very much.”


However further up in the article it’s also mentioned: “…the average pitch loses about 7.4 mph on its way to the plate. The exact amount of velocity lost depends on the thickness of the air (which varies with temperature, weather and elevation), but a high-drag ball tends to lose about 8.7 mph on the way to the plate, while a slicker ball would lose only 6.5 mph.”

So the juiced ball would lose less velo on the way to the plate. I wonder if this would have increased SwStr% on certain pitch types, helping to lead to more K’s?