Getting to Know Barrels Per True Fly Ball Rate (Brls/True FB)
Since it was first introduced about a year and a half ago and published on the Statcast Leaderboard, Barrels quickly became one of my favorite metrics. Combining exit velocity with launch angle made it the perfect statistic to reference when investigating a hitter’s power potential. Since we like ratios better than counting stats for projection purposes, Barrels per Batted Ball Event (Brls/BBE) was my metric of choice. But as informative as it remains, I discovered that it actually wasn’t the best ratio when it came to forecasting HR/FB rates.
Yesterday, I unveiled the newest version of my batter xHR/FB rate, which looks a bit different than my original Statcast-charged version. One of the changes made to the equation was a switch from using Brls/BBE to Brls/True FB. That is, instead of using all batted balls as the denominator, I realized that we really only care about fly balls, since that’s the denominator of the equation we’re trying to update. Duh. But rather than use all fly balls, I removed pop-ups, to get a truer sense of a batter’s ability to hit his fly balls over the fence. I dubbed this statistic True FB.
Since Brls/True FB (Brls/TFB from now on) is a new metric I just introduced yesterday, let’s get to know it, shall we? To put it into context, I will do a lot of comparing to Brls/BBE. Note that all statistics are solely from the population I used to create my equation, which are batters with at least 60 BBE from 2015 to 2017 (1,347 total over three seasons).
Season | Population Avg Brls/TFB | Population Avg Brls/BBE |
---|---|---|
2015 | 17.99% | 5.48% |
2016 | 20.45% | 6.37% |
2017 | 19.94% | 6.41% |
While my population’s average Brls/BBE spiked in 2016 and rose again ever so slightly in 2017, Brls/TFB actually regressed marginally in 2017 after surging in 2016. That’s probably a direct result of more fly balls hit in 2017, providing more chances for batted balls to be classified as a barrel. But when we’re isolating fly balls, we find that hitters aren’t barreling them up more often, which might come as a surprise given the league HR/FB rate trend.
2015-2017 | Brls/TFB | Brls/BBE |
---|---|---|
Max | 63.5% | 25.7% |
Average | 19.5% | 6.1% |
Median | 18.2% | 5.9% |
Since TFB is a much smaller subset of BBE, Brls/TFB is of course going to be a larger rate than Brls/BBE. Because of the bigger maximum, the range is a lot wider, increasing the skill differences between players. Can you guess who easily led baseball over the last three seasons with the max in both metrics? Aaron Judge, obviously.
This table helps us determine what’s a strong, average, and weak Brls/TFB mark, something that’s important to remember when learning a new metric. My population average and median Brls/TFB rates are about triple the marks of Brls/BBE. Though, the max Brls/TFB isn’t quite triple that of Brls/BBE (c’mon Judge, get on that!).
Now let’s compare where a hitter ranked in 2017 in Brls/BBE to where he ranks in the new Brls/TFB. I subtracted the Brls/BBE rank from the Brls/TFB rank, so a positive number mean a worse Brls/TFB ranking, while a negative number means better. Let’s start with those who look less powerful using Brls/TFB.
Player | Brls/True FB | Brls/True FB Rk | Brls/BBE | Brls/BBE Rk | Rk Diff |
---|---|---|---|---|---|
Kyle Seager | 17.8% | 219 | 8.6% | 117 | 102 |
Matt Carpenter | 17.2% | 229 | 8.2% | 131 | 98 |
Tyler Moore | 17.7% | 222 | 8.1% | 133 | 89 |
Justin Turner | 18.5% | 203 | 8.6% | 116 | 87 |
Gregory Bird | 18.4% | 206 | 8.4% | 127 | 79 |
Pat Valaika | 15.3% | 255 | 6.7% | 176 | 79 |
Lucas Duda | 26.3% | 109 | 12.1% | 33 | 76 |
Anthony Rendon | 15.0% | 262 | 6.5% | 186 | 76 |
John Jaso | 18.1% | 211 | 7.9% | 138 | 73 |
Josh Phegley | 12.7% | 293 | 5.6% | 222 | 71 |
Jay Bruce | 23.8% | 132 | 10.5% | 62 | 70 |
Derek Norris | 12.3% | 306 | 5.2% | 236 | 70 |
These hitters were really crushed with the switch to Brls/TFB. Why? Because as a group, they averaged a whopping 45.8% True FB% versus just a 31.6% population average mark from 2015-2017! Their fly ball happiness boosted their Brls/BBE marks thanks to all the extra opportunities they had to hit a barrel.
Now let’s check on the winners of this metric. You could probably guess that it’s the low True FB% crew.
Player | Brls/True FB | Brls/True FB Rk | Brls/BBE | Brls/BBE Rk | Rk Diff |
---|---|---|---|---|---|
Derek Fisher | 31.3% | 62 | 5.4% | 228 | -166 |
Jonathan Villar | 30.0% | 72 | 5.5% | 223 | -151 |
Yandy Diaz | 19.0% | 195 | 3.3% | 321 | -126 |
Howie Kendrick | 40.4% | 12 | 7.9% | 135 | -123 |
Eric Hosmer | 34.0% | 41 | 7.0% | 162 | -121 |
David Freese | 29.4% | 77 | 6.4% | 187 | -110 |
Christian Arroyo | 16.7% | 236 | 3.2% | 326 | -90 |
Erik Gonzalez | 46.7% | 5 | 9.3% | 90 | -85 |
Ian Desmond | 14.6% | 267 | 2.7% | 352 | -85 |
Craig Gentry | 21.1% | 167 | 4.9% | 247 | -80 |
Ryan Rua | 36.8% | 24 | 9.0% | 103 | -79 |
Russell Martin | 29.1% | 80 | 7.1% | 157 | -77 |
Christian Yelich | 28.7% | 86 | 7.0% | 163 | -77 |
Yunel Escobar | 21.4% | 164 | 5.0% | 241 | -77 |
Joe Mauer | 19.8% | 188 | 4.5% | 263 | -75 |
Leury Garcia | 18.9% | 200 | 4.2% | 275 | -75 |
Tommy Pham | 38.8% | 19 | 9.3% | 91 | -72 |
We were right! This group’s True FB% was a measly 22%, which gave them scarce opportunities to hit a barrel. So all this time, the Brls/BBE underrated their home run power potential. This is mostly a list of scrubs, certainly a less impressive group of hitters, but there are a couple of intriguing names.
Derek Fisher is a great name to tuck away if he ever managed to fall into playing time on that ridiculously good Astros team. Or hey, perhaps they trade him. Check out Howie Kendrick! From a non-descript single digit Brls/BBE to a monstrous 12th ranked Brls/TFB! Eric Hosmer’s name is no surprise to find, as he is the king of hitting too many grounders for a guy we know has good power. Ummm, Erik Gonzalez fifth? WHO?! Pretty small sample size, but the power breakout first began at Triple-A, suggesting that this was at least somewhat for real.
I purposely lengthened the list so I could grab Tommy Pham at the end of it. It’s not often that a 29-year-old career minor leaguer records his first season with more than 200 MLB plate appearances and posts a .398 wOBA, .214 ISO, and 26.7% HR/FB rate. With just a 9.3% Brls/BBE mark that ranked 91st, it was impossible to believe that mid-20% HR/FB rate was anything close to sustainable. But a 38.8% Brls/TFB rate that ranked 19th? Yeah, that offers a heck of a lot more reason to be optimistic that this wasn’t just a dream fluke for Pham. And since I know you’ll ask, his updated xHR/FB rate was 22.5%, significantly higher than the 15.2% xHR/FB rate calculated from the previous equation.
More Brls/TFB rate talk to come!
Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year and three-time Tout Wars champion. He is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. Follow Mike on X@MikePodhorzer and contact him via email.
Okay, interesting. So would you say both of these statements are equally accurate/fantasy-relevant?
1) “The bottom list is populated by guys who have shown the potential for a power breakout if they start lifting the ball more.”
2) “The bottom list is populated by guys who are already turning their relatively few fly balls into barrels at an unusually high rate – if they don’t/can’t adjust to hit more fly balls, they have likely maxed out their possible power production.”
Thanks!
Kind of to number 1. The bottom list is a group of guys my original equation underrated for HR/FB forecasts. That doesn’t mean they have greater breakout potential. However, because their FB% marks are low, it seemingly gives them more room for growth, which would indeed lead to a breakout. That said, any hitter who increases their FB%, with all else remaining equal, will enjoy a homer spike.
If you phrase it in such a way to state that Giancarlo Stanton and Aaron Judge have maxed out their possible power production, then 2 is accurate. Obviously a high HR/FB rate is harder to maintain and the powers of regression and effects of aging are going to weight on that mark. But again, it’s no different than any group of hitters with a high HR/FB rate, remaining in the top quadrant in a skill is hard!