Expected Home Run Rate, 2020 Edition
Of course, some caveats apply. I’m only looking at batted balls between 15 and 45 degrees, and the sample size is still small. But for the most part, and excluding the vagaries of that small sample, the conclusion makes sense. Hit the ball harder, and you’ll find more home runs.
Wait — hit it at the wrong angle? That seems like something in a batter’s control. It partially is, but I’ve chosen to exclude it for two reasons. First, I’ll point again to this excellent Alex Chamberlain article. You should really read it, but the conclusion is basically this: batters control exit velocity and pitchers control launch angle. That’s not exclusively true, and there are obviously fly ball and groundball hitters, but if you start giving batters credit for the exact angle of their batted balls instead of just generally saying “in the air” or “not,” you might be going too far.
Second, this way is simpler! Simplicity has value. Overspecify a model, and you can get very precise results that are also hard to interpret, or that depend heavily on small fluctuations in initial conditions. That’s not to say that such a model is a bad idea — merely that it’s not strictly upside to add more and more gadgets and whizbangs to it. You also risk losing the signal you’re looking for, which in this case is the ability to absolutely hit the snot out of the ball, sending it skyward at stupid speeds.
Let’s say, for example, that we could add spray angle (horizontal launch angle, essentially), wind conditions, humidity, and count on the pitch in question to our model. It would, in theory, be more precise. All of those things affect home runs. But none of them are under the direct control of the batter, which makes them questionably useful. Does the humidity on August 28, say, have anything to do with whether Aaron Judge hits the ball hard enough to deserve a home run? I think not.
Don’t think of this, then, as me being some arbiter of who deserves and doesn’t deserve their home runs. This is definitely not that! The home runs that have already happened are banked, and nothing can take them away. It is, however, a useful way of seeing who’s hitting the ball hard and in the air this year. It’s a better metric than average exit velocity, in my opinion at least, because it handles distribution; a 70 mph fly ball and a 110 mph fly ball suggests more home run talent than two 95 mph fly balls.
Plus, the expected home run leaders are all really good power hitters. That’s a good sign:
| Name | xHR | xHR/Air% | Air Balls |
|---|---|---|---|
| Fernando Tatis Jr. | 12.9 | 28.0% | 46 |
| Corey Seager | 11.7 | 22.6% | 52 |
| Jose Abreu | 11.3 | 19.8% | 57 |
| Mike Trout | 11.2 | 21.5% | 52 |
| Brandon Lowe | 10.1 | 21.0% | 48 |
| Trevor Story | 9.2 | 15.1% | 61 |
| Teoscar Hernandez | 9.0 | 20.5% | 44 |
| Marcell Ozuna | 9.0 | 21.4% | 42 |
| Miguel Sano | 8.9 | 33.0% | 27 |
| Luis Robert | 8.8 | 18.8% | 47 |
There’s interesting information in both the totals and the expected rate. Trevor Story, for example, should expect half as many home runs per fly ball as Fernando Tatis Jr. He still makes the list, though, because he gives himself more chances: his 0.57 GB/FB ratio is one of the lowest 10 marks in the majors this year. His presence on the list is all the more remarkable because I’m not making any corrections for Coors Field. He’s on the list for his pop, not for where he plays.
If you’re looking for pure thump instead of some combination of opportunities and muscle, you could instead look through the list of batters with the highest expected home runs per ball in the air:
| Name | xHR/Air% | Air Balls |
|---|---|---|
| Miguel Sano | 33.0% | 27 |
| Fernando Tatis Jr. | 28.0% | 46 |
| Juan Soto | 27.7% | 26 |
| Colin Moran | 24.8% | 23 |
| Matt Chapman | 24.1% | 36 |
| Jorge Soler | 23.4% | 30 |
| Ronald Acuna Jr. | 23.3% | 32 |
| Corey Seager | 22.6% | 52 |
| Eloy Jimenez | 22.2% | 39 |
| Garrett Cooper | 22.2% | 16 |
There’s an old analyst saw that the best metrics tell you 80% what you already knew and 20% things you didn’t expect, and seeing Colin Moran and Garrett Cooper on this list certainly qualifies as that. They’ve both done it in small samples, but still, mashing the ball isn’t something you can fake. You can’t hit a seeing-eye 105 mph laser. That’s not to say they’re two of the top 10 power hitters in baseball, but they’re playing like it at the moment.
What about the flip side of the coin, hitters who maybe shouldn’t be putting the ball in the air so much? Here are the 11 batters with the lowest expected home runs per air ball this year:
| Name | xHR/Air% | Air Balls |
|---|---|---|
| Dee Strange-Gordon | 0.2% | 17 |
| Andrelton Simmons | 0.8% | 16 |
| Eric Sogard | 1.8% | 30 |
| Ender Inciarte | 1.9% | 31 |
| David Fletcher | 1.9% | 37 |
| Tony Kemp | 2.4% | 33 |
| Myles Straw | 3.5% | 18 |
| Cole Tucker | 3.7% | 21 |
| Kurt Suzuki | 3.7% | 36 |
| Isiah Kiner-Falefa | 3.8% | 31 |
| Mike Tauchman | 3.8% | 24 |
I extended it to 11 to point out how weird Mike Tauchman’s season is, but truthfully, this list makes a ton of sense. Eric Sogard lucked into a pile of homers last year, but did it despite poor exit velocity numbers — he was one of the biggest home run overachievers in last year’s version of this list. It’s no surprise to see him crash back down to earth this year. The rest of the list is a bunch of slap hitters. No surprises here.
Another quick list: the batters who have hit the ball hardest with nothing to show for it. Here are the 10 hitters whose xHR most outstrips their actual home run total. One quick note: all the home run totals I’m using here are home runs hit on the balls I’m considering, hit between 15 and 45 degrees. There have been exactly four home runs outside those bands this year, including one off the bat of Ian Happ, who will appear later on. For the most part, though: bad luck, friends:
| Name | xHR | HR | HR-xHR |
|---|---|---|---|
| Justin Turner | 5.6 | 2 | -3.6 |
| Nick Solak | 5.4 | 2 | -3.4 |
| J.D. Martinez | 7.3 | 4 | -3.3 |
| Nomar Mazara | 3.0 | 0 | -3.0 |
| Jose Peraza | 3.9 | 1 | -2.9 |
| Evan Longoria | 7.7 | 5 | -2.7 |
| Ketel Marte | 4.5 | 2 | -2.5 |
| Luis Arraez | 2.3 | 0 | -2.3 |
| Kyle Farmer | 2.2 | 0 | -2.2 |
| Christian Walker | 8.2 | 6 | -2.2 |
Shocked to see Jose Peraza on there? I was too! But he’s hit a ton of air balls this year — 49 so far. His expected home run per air ball rate is only 8%, which puts him toward the bottom of the league. It’s just — one home run! That’s unlucky! The rest of the list is good hitters having down years statistically — not every single hitter having a down year is on here, but this group, at least, is still hitting the ball hard.
To round out this trip around batted ball data, let’s take a look at the players who have most outperformed their expected home run totals. This isn’t to say they’re getting lucky, or that they’re headed for a decline, or anything of the sort. It simply means that based on how hard they’ve hit the ball in the air so far, and keeping everything else equal, you would have expected them to have fewer homers. If they keep hitting the ball like they have, you should expect them to run a lower home run rate going forward:
| Name | xHR | HR | HR-xHR |
|---|---|---|---|
| Nelson Cruz | 8.0 | 15 | 7.0 |
| Luke Voit | 7.8 | 14 | 6.2 |
| Teoscar Hernandez | 9.0 | 14 | 5.0 |
| Ian Happ | 7.3 | 12 | 4.7 |
| Mookie Betts | 7.7 | 12 | 4.3 |
| Aaron Judge | 4.7 | 9 | 4.3 |
| Pete Alonso | 5.7 | 10 | 4.3 |
| Eddie Rosario | 4.9 | 9 | 4.1 |
| Marcell Ozuna | 9.0 | 13 | 4.0 |
| Kyle Schwarber | 6.0 | 10 | 4.0 |
To be clear, these are all good hitters. Only Mookie Betts isn’t a stereotypical power hitter, and he gets it done with quickness and volume. All of them, however, are running home run rates in excess of what you’d expect if they don’t start hitting the ball harder. Cruz has 16 air balls over 100 mph this year — 15 of them comprise his 15 home runs. That’s efficiency! Tatis, tied with Cruz for the second-most home runs in the majors, has 26 air balls above 100 mph. He’s crushing the ball in bulk.
How predictive is this metric? I don’t know yet. I’ve got more work to do there, and it’s likely more of a split-half thing, because the dang ball keeps changing year-to-year. Trying to get one year’s expected home run rate to predict next year’s actual home run rate, with both players and the ball changing, might be optimistic. There are park effects to handle, too: Story has nine dingers and nine expected dingers, but with the added knowledge that he’s playing in Coors, he’s probably getting unlucky.
For now, you’ll simply have to get the data in list form. Fernando Tatis Jr. is a deserving home run leader! Dee Strange-Gordon isn’t powerless on accident! Justin Turner can still rake! Aaron Judge might have lost a little bit of pop under the hood! If those aren’t fun conclusions, I don’t know what to tell you.
All data is through games of 9/9/2020.
