The Z Files: The Relationship Between HR and Flyball Average Exit Velocity

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Published on January 30, 2020

Lesson learned: less sausage, more meat. Understanding how the sausage is made is certainly a worthwhile endeavor but there's only about eight weeks before drafts pick up in earnest, so there will be less bandwidth on theory and more on application.

This week's discussion will continue the notion of breaking average exit velocity (AEV) into components, with flyballs the focus. By means of reminder, here is the 2019 league average velocity component data:

Batted Ball	mph
Overall	89
Flyball	92
Line Drive	93.3
Groundball	86.3

Pitches travel with a downward trajectory. In order to transfer the most energy to a batted ball, the swing path should match the trajectory, hence an uppercut. This explains the difference between grounders and flies. Neither are centered, but the upward trajectory of the swing transfers more energy to anything lofted.

The bulk of today's discussion will revolve around a concept Mike Podhorzer from Fangraphs introduced several years ago. Home run rate correlates very well with average fly ball distance (FBD). So much so, in fact, an expected home run total can be determined for every hitter and compared to their actual performance. Just like comparing any stat estimator, outliers can be identified and evaluated.

Batted Ball	mph
Overall	89
Flyball	92
Line Drive	93.3
Groundball	86.3

Parsing out flyball homers and comparing HR/FB (home run per fly) to average FBD, the correlation coefficient is .77. Intuitively, flyball distance should correlate with AEV on flies. Launch angle matters, but there should be some relationship. Sure enough, the correlation coefficient is a strong .90. Flyball AEV correlates to HR/FB at virtually the same level, .77. It's this final correlation, average flyball velocity to HR/FB that drives the rest of the discussion.

Before diving into some intriguing players, while a .77 correlation is solid, it's not perfect. A possible reason for the discrepancy could be park dimensions. As such, I adjusted the flyball home runs according to the player's home venue. The resulting correlation is unchanged. Following up on this is a worthwhile project, but… less sausage, more meat.

Let's start with something simple. Here are the top 20 batters in terms of AEV (minimum of 10 homers).

Rank	Player	AEV	HR	FB DIST	#FB	HR/FB
1	Joey Gallo	100.9	19	359	47	40.4%
2	Miguel Sano	100	32	367	72	44.4%
3	Nelson Cruz	99.9	36	359	81	44.4%
4	Franmil Reyes	99	35	351	92	38.0%
5	Adam Duvall	98.1	10	353	34	29.4%
6	Aaron Judge	98	24	347	63	38.1%
7	Jorge Soler	97.4	43	349	115	37.4%
8	Josh Donaldson	97.1	32	341	93	34.4%
9	Bryce Harper	97	30	340	114	26.3%
10	Pete Alonso	96.8	45	350	118	38.1%
11	Eloy Jimenez	96.8	29	348	79	36.7%
12	Matt Adams	96.8	18	346	58	31.0%
13	Jorge Alfaro	96.7	15	346	57	26.3%
14	Kyle Schwarber	96.7	35	342	109	32.1%
15	Mitch Garver	96.7	29	336	80	36.3%
16	Christian Yelich	96.5	38	347	105	36.2%
17	C.J. Cron	96.5	18	338	84	21.4%
18	Matt Olson	96.5	33	337	105	31.4%
19	Brad Miller	96.4	12	338	37	32.4%
20	Juan Soto	96.3	31	340	112	27.7%

Most of the names are expected, with a couple of surprises with the pair of catchers catching my eye, especially Alfaro. The Marlins backstop doesn't take advantage of one of the highest flyball AEV. His home park doesn't help, but remember the fence in right field will be closer this season and Alfaro is adept at going the other way. More importantly, Alfaro fanned 33 percent of the time last season. Further, his ground ball rate was 52.7 percent, far too high for someone able to drive flyballs with authority. Because Alfaro's cost of acquisition is so cheap, it's worth the speculation his contact improves and/or he learns to elevate the ball more. Of course, this assumes doing either doesn't take away from his flyball AEV.

Let's look at some outlying players via various comparisons to see how the delta affected their 2019 home run total, then decide how it influences 2020 expectations.

The following table displays the 20 biggest differences between flyball AEV and AEV on all batted balls.

Rank	Player	AEV FB	AEV All	HR	FB DIST	#FB	HR/FB
1	Ronny Rodriguez	95.1	87.3	13	339	54	24.1%
2	Matt Adams	96.8	88.4	18	346	58	31.0%
3	Roberto Perez	96	88.3	21	343	68	30.9%
4	Corey Dickerson	94.1	87.1	11	333	39	28.2%
5	Aristides Aquino	94.2	87.9	19	339	46	41.3%
6	Jose Altuve	93.3	86.1	28	336	92	30.4%
7	Tim Beckham	93.9	87.5	14	326	55	25.5%
8	Jonathan Schoop	93.8	87.5	18	337	62	29.0%
9	Starling Marte	93.7	87.4	21	334	89	23.6%
10	Willson Contreras	94	88.3	21	338	64	32.8%
11	Jose Martinez	94.3	88.6	10	341	42	23.8%
12	Carlos Correa	95	89	17	343	54	31.5%
13	Harrison Bader	92.9	86.6	11	332	62	17.7%
14	Mike Yastrzemski	94.2	88.7	20	340	78	25.6%
15	Wil Myers	94.7	88.9	13	328	56	23.2%
16	Rougned Odor	95.9	89.4	25	343	100	25.0%
17	Maikel Franco	94.7	89	16	328	72	22.2%
18	David Dahl	93.6	88.2	14	347	63	22.2%
19	Paul DeJong	92.8	87	26	333	123	21.1%
20	Robinson Chirinos	92.5	86.2	16	326	66	24.2%

Note, the overall AEV for these players is below league average, but their flyball AEV is above average. A casual look at overall AEV in a vacuum may point towards some of these guys having inflated, or lucky, home run totals. However, digging deeper reveals the power output is more deserved.

Let's look at some of the more interesting batters from this table. There isn't a one-size-fits-all approach to the analysis. Each player should be considered in context. All the data required to choose a player on your own and look under the hood is available via searching the Statcast portion of Baseball Savant. Please feel free to post other players in the comments and I'll be happy to put them under the microscope.

Ronny Rodriguez

There's nothing especially fantasy relevant about Rodriguez, but he's a good example of someone with an extreme delta between component AEV.

Overall	87.3
Flyball	95.1
Line Drive	92.1
Groundball	82.3

The only takeaway is overall AEV may not tell the whole story. Before whipping out the luck card, it's worth looking at the component AEV.

Roberto Perez

Heading into the 2019 campaign, Perez blasted 21 homers in 963 career plate appearances. Last season, he crushed 24 long balls in 449 PA, what gives? His flyball AEV last season was 96 mph, compared to 91 mph in 2018 and 92.1 mph in 2017. This doesn't guarantee Perez will maintain the significant gains, but there's more to his power surge than luck.

Rougned Odor

Odor's batting average is already a deterrent despite his knack for bullying up counting stats. With the Rangers moving into a new park that's assumed to be less hitter-friendly, there's even more of a reason to shy away. A league average AEV doesn't help his cause. However, Odor mashes flyballs with authority, which is reflected in his average flyball distance. If I already felt comfortable in batting average, I wouldn't hesitate to grab Odor, especially since his cost is essentially free.

Here's another list of perceived lucky players. The following is the 20 biggest discrepancies between their average FBD and HR/FB:

Rank	Player	FB DIST	HR/FB	AEV FB
1	Brett Gardner	315	27.5%	90.6
2	Jesse Winker	320	27.3%	91
3	Will Smith	326	30.4%	93
4	Pablo Sandoval	319	25.6%	92
5	Derek Dietrich	330	34.7%	92.6
6	Kurt Suzuki	315	23.9%	90.3
7	Keston Hiura	326	29.3%	92.9
8	A.J. Pollock	317	24.2%	92.5
9	Matt Wieters	322	26.2%	91
10	Tom Murphy	329	31.6%	94.4
11	Yuli Gurriel	318	24.1%	90
12	Anthony Rizzo	325	27.5%	93
13	Clint Frazier	320	24.5%	91.3
14	Gleyber Torres	330	29.9%	93.1
15	Tucker Barnhart	303	19.6%	88.5
16	Jake Marisnick	317	22.7%	92.1
17	Mitch Haniger	329	28.8%	93.8
18	Alex Bregman	323	24.5%	91.3
19	Eugenio Suarez	335	35.5%	93
20	Kris Bryant	325	25.4%	92.8

A lot of these differences are in part due to favorable venues. There are four Reds, three Astros and three Yankees, encompassing half the spots. It makes sense they're not all park related. If they were, the park factor adjustment would have resulted in a greater correlation.

Something to note with these players is not only are they subject to possible regression in HR/FB based on a short FBD, most of the distances are in the sweet spot with respect to benefiting from the reduced drag on last season's baseball. If the five to 10 feet of added travel is lost, they're all in danger of giving back a disproportionately large amount of their power gains from last season.

Looking at the other end of this spectrum should unearth perceived unlucky batters as the following HR/FB are low compared to the FBD.

Rank	Player	FB DIST	HR/FB	AEV FB
1	Howie Kendrick	342	20.9%	94.8
2	David Dahl	347	22.2%	93.6
3	Alex Gordon	329	9.0%	91.8
4	Brandon Dixon	340	21.2%	94.4
5	Yadier Molina	328	12.3%	91.4
6	Nick Castellanos	334	18.4%	94
7	Brandon Drury	335	19.0%	92.3
8	C.J. Cron	338	21.4%	96.5
9	Tommy Pham	338	21.4%	94.9
10	Harrison Bader	332	17.7%	92.9
11	Evan Longoria	333	18.5%	93.9
12	Danny Jansen	332	17.8%	91.8
13	Charlie Blackmon	341	23.3%	92.7
14	Yandy Diaz	341	23.3%	94.5
15	Gio Urshela	332	18.1%	93.4
16	Mookie Betts	330	16.9%	93.9
17	Nick Ahmed	330	17.3%	91.2
18	Jose Martinez	341	23.8%	94.3
19	Jose Osuna	333	20.0%	92.6
20	Eduardo Escobar	329	17.3%	91.3

Without a definitive reason for the deltas, there's a chance some of these players were indeed snake bit and are candidates to regress to the good. In addition, if they lose some distance, they're not as likely to have it cost them in terms of homers.

Castellanos is most interesting, seeing as though he's moving to a great power park. Urshela is also nice to see since the narrative is his power came out of nowhere, thus he could revert to previous levels when in fact, he should have hit even more homers last year.

Cron has spent a lot of his career in power-suppressing venues, so not only is it a plus Comerica Park is slightly favorable to righty swingers, he's due some positive HR/FB regression.

The final set of comparisons features flyball AEV and FBD. Remember, the correlation was .90 so the outliers can be telling. First, the longer than expected FBD:

Rank	Player	FB DIST	AEV FB	HR/FB
1	Nolan Arenado	339	92.1	26.5%
2	Charlie Blackmon	341	92.7	23.3%
3	Trevor Story	342	92.9	28.2%
4	Willie Calhoun	335	91.8	29.8%
5	David Dahl	347	93.6	22.2%
6	Ian Desmond	339	92.8	24.7%
7	Brandon Drury	335	92.3	19.0%
8	Danny Jansen	332	91.8	17.8%
9	Nick Ahmed	330	91.2	17.3%
10	Bryan Reynolds	326	90.2	16.7%
11	Eduardo Escobar	329	91.3	17.3%
12	Garrett Cooper	340	93.6	30.4%
13	Jose Osuna	333	92.6	20.0%
14	Jose Altuve	336	93.3	30.4%
15	Alex Gordon	329	91.8	9.0%
16	Hunter Pence	338	93.5	32.0%
17	Yadier Molina	328	91.4	12.3%
18	Eugenio Suarez	335	93	35.5%
19	Chris Davis	339	93.8	23.5%
20	Justin Upton	332	92.6	29.7%

It's no surprise five of the top six are Rockies, since the thin Denver air reduces resistance, lengthening flight. Another possible explanation is the backspin incurred when each batter hits a flyball. The science behind this goes beyond the scope of this discussion. However, something like backspin is a repeatable skill, it could explain a batter continually launching flyballs deeper than portended by the AEV. That said, perusing the non-Colorado players, no one has maintained a higher than expected homer total for consecutive seasons.

The last table shows the hitters with the shortest distances relative to their AEV:

Rank	Player	FB DIST	AEV FB	HR/FB
1	Matt Chapman	326	95	22.8%
2	Byron Buxton	317	93.5	17.9%
3	Luke Voit	326	94.6	24.4%
4	Ryan O'Hearn	326	94.4	17.2%
5	Brandon Lowe	331	95.8	27.6%
6	Marcell Ozuna	332	96.2	24.2%
7	Maikel Franco	328	94.7	22.2%
8	Wil Myers	328	94.7	23.2%
9	Tim Beckham	326	93.9	25.5%
10	A.J. Pollock	317	92.5	24.2%
11	Kole Calhoun	332	95.3	29.7%
12	Mitch Garver	336	96.7	36.3%
13	Tom Murphy	329	94.4	31.6%
14	Jackie Bradley Jr.	329	94.2	22.6%
15	Yan Gomes	315	92	15.7%
16	Edwin Encarnacion	333	95.2	27.3%
17	Wilson Ramos	328	93.9	21.4%
18	Yasiel Puig	324	93.3	19.0%
19	Jake Marisnick	317	92.1	22.7%
20	Jose Abreu	335	95.6	28.8%

Non-ideal backspin could be again be a cause for these results as can atmospheric conditions. If cold weather, or wind, or the marine layer were a tangible reason, though, more players from the associated teams would populate this list. Ergo, if it's random, the above are all candidates to improve in the long ball department next season.

The beauty of studies of this nature is the data is open to individual interpretation. With that in mind, here's a list of players I'm noting as possible power gainers this season, followed by some I'm worried could fall back.

Gainers

Fallers

Thus concludes a look at the relationship between fly ball average exit velocity and power. To reiterate, please post players you'd like examined closer, even if they don't appear on any of these lists.