Fly Ball Launch Angle Matters

On Tuesday, I tried to figure out why Jose Ramirez‘ home run power has disappeared. After much research that led to multiple dead ends, I discovered that his fly ball launch angle (LA) had increased significantly this season to a its highest mark in the Statcast era. I opined that perhaps his fly balls had become overly elevated and were too close to a pop-up than a line drive, driving down his HR/FB rate. This discovery piqued my interest in fly ball LA, so I decided to embark on a not-too-scientific study.

I downloaded average fly ball LA for the past two seasons and then added each season’s HR/FB rates. The hypothesis — the largest LA surgers would suffer HR/FB rate declines, while the biggest reductions might enjoy HR/FB rate spikes. Let’s see if I was right.

My population set was comprised of 271 players. Let’s begin by looking at all hitters who have seen their average fly ball LA increase by at least three degrees.

Fly Ball LA Surgers
Player 2018 HR/FB 2019 HR/FB Diff 2018 FB LA 2019 FB LA Diff
Brandon Nimmo 17.5% 10.0% -7.5% 35.4 41.9 6.5
David Bote 19.4% 20.0% 0.6% 32.7 38.4 5.7
Kendrys Morales 17.9% 4.9% -13.0% 33.7 38.9 5.2
Maikel Franco 17.5% 11.8% -5.7% 32.8 37.3 4.5
Willy Adames 16.9% 13.0% -3.9% 34.8 38.9 4.1
Ji-Man Choi 20.8% 17.0% -3.8% 35.0 38.9 3.9
Christian Walker 21.4% 18.2% -3.2% 32.7 36.6 3.9
Justin Bour 18.3% 17.1% -1.2% 33.3 37.0 3.7
Rowdy Tellez 22.2% 20.0% -2.2% 32.7 36.4 3.7
Luke Voit 40.5% 27.0% -13.5% 33.9 37.5 3.6
Yan Gomes 13.6% 5.0% -8.6% 36.5 40.0 3.5
A.J. Pollock 17.1% 6.9% -10.2% 35.5 39.0 3.5
Jake Bauers 13.8% 14.8% 1.0% 35.5 39.0 3.5
Niko Goodrum 15.5% 10.2% -5.3% 34.6 38.1 3.5
C.J. Cron 21.4% 22.1% 0.7% 34.8 38.1 3.3
Billy McKinney 17.6% 7.3% -10.3% 34.8 38.1 3.3
Yolmer Sanchez 6.0% 2.0% -4.0% 34.2 37.5 3.3
Jeff Mathis 2.0% 3.0% 1.0% 37.8 40.9 3.1
Jose Ramirez 16.9% 4.7% -12.2% 35.8 38.9 3.1
Adam Eaton 7.7% 7.4% -0.3% 34.5 37.6 3.1
Christian Yelich 35.0% 34.7% -0.3% 32.6 35.7 3.1
Unweighted Avg 18.0% 13.2% -4.9% 34.5 38.3 3.9

Would you look at that! As a group, the unweighted average fly ball LA surged by 3.9 degrees. Perhaps not coincidentally, the group’s unweighted average HR/FB rate has plummeted by 4.9%. What’s amazing is that only four of the 21 hitters on this list have increased their HR/FB rates! That’s pretty incredible. And of those four, not one of them increased their HR/FB rate by more than 1%. Seriously, this alone looks like pretty damning evidence that fly ball LA must be accounted for when explaining historical HR/FB rates or projecting them moving forward.

Next up are the fly ball LA decliners. Did they enjoy a HR/FB rate spike, moving in the opposite direction of the fly ball LA surgers? I’ll once again use a three degree cut off.

Fly Ball LA Decliners
Player 2018 HR/FB 2019 HR/FB Diff 2018 FB LA 2019 FB LA Diff
Ben Gamel 1.8% 14.3% 12.5% 39.3 33.1 -6.2
Brandon Drury 5.0% 12.8% 7.8% 38.0 32.1 -5.9
Yandy Diaz 4.8% 19.3% 14.5% 36.9 31.8 -5.1
Willson Contreras 9.3% 26.0% 16.7% 37.6 32.6 -5.0
Billy Hamilton 3.1% 0.0% -3.1% 38.2 33.5 -4.7
Trea Turner 11.0% 13.2% 2.2% 36.7 32.1 -4.6
Lourdes Gurriel Jr. 17.5% 20.0% 2.5% 37.6 33.1 -4.5
Brian Anderson 8.3% 13.1% 4.8% 39.2 34.9 -4.3
Brandon Dixon 19.2% 16.7% -2.5% 38.8 34.7 -4.1
Derek Dietrich 12.3% 30.0% 17.7% 38.6 34.8 -3.8
Carlos Correa 13.9% 23.4% 9.5% 36.2 32.4 -3.8
Danny Jansen 9.7% 4.7% -5.0% 39.4 35.6 -3.8
Pedro Severino 3.7% 20.5% 16.8% 38.9 35.1 -3.8
Eric Thames 22.9% 25.0% 2.1% 39.2 35.6 -3.6
Tyler White 17.4% 6.3% -11.1% 38.2 34.6 -3.6
Byron Buxton 0.0% 11.5% 11.5% 42.6 39.1 -3.5
Harrison Bader 14.1% 13.6% -0.5% 37.7 34.2 -3.5
Kolten Wong 10.2% 9.1% -1.1% 39.2 35.9 -3.3
Matt Adams 20.2% 25.0% 4.8% 38.0 34.7 -3.3
Tim Anderson 14.2% 16.7% 2.5% 36.9 33.6 -3.3
Andrew Benintendi 9.4% 7.9% -1.5% 37.9 34.7 -3.2
Unweighted Avg 10.9% 15.7% 4.8% 38.3 34.2 -4.1

Holy guacamole, this group represents another point for the “fly ball launch angle matters” camp! This group’s unweighted average fly ball LA declined by 4.1 degrees, which is a very similar absolute change to the surger group’s increase. This seemingly has helped lead to this group’s unweighted average HR/FB rate jumping by 4.8%, which was almost identical to the change experience by the surger group, but in the opposite direction.

Once again, there were 21 hitters in this group, but seven hitters suffered from a decline in HR/FB rate. Aside from the greater number of hitters performing opposite of the rest of the group, the declines were much more significant than the surgers in the first table. Some of this has to do with the fact than in general, hitters suffer HR/FB rate declines each season on average as they age. So this is not surprising or unexpected.

Though the effects here aren’t as validating as the first group, it does assist in the storytelling. It would appear that an increase in fly ball LA is more damning than a decrease is positive, but the latter is still meaningful data to be aware of.

I also calculated the correlation between the year over year difference in fly ball LA and HR/FB rate, which was -0.361. This is not the strongest of correlations, but it’s high enough that we can be pretty confident it’s a factor in HR/FB rate. Furthermore, it’s negative, which is precisely the picture the tables were painting.

I went a step further, calculating the correlation between HR/FB rate and fly ball LA, which was -0.245. The correlation was a bit weaker than the directional correlation, but again, confirms that fly ball LA should not be ignored.

While this was admittedly not an exhaustive scientific study, it was all I needed to prove that my initial hypothesis was correct. More work should absolutely be done on the affect of launch angle changes on HR/FB.

This discovery has a real impact on me as I heavily use my xHR/FB rate equation to develop my annual Pod Projections. The current form of the equation includes Statcast’s barrels, which is a combination of LA and exit velocity, but it doesn’t explicitly incorporate fly ball LA. I already knew I wanted to go back to the drawing board and work on xHR/FB Rate Version 3.0, and this discovery is all the more reason to do so.





Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.

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ChapelHeel66
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ChapelHeel66

Think there might be an extra zero in that first correlation number?