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.

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.

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.