A Simple Fix for Barrels in 2021

Here is a disputed fact: MLB changed the ball. League brass, on the record, wanted to make the ball livelier but also raise the height of the seams, which would increase drag. The two changes — more bounce, but also more air resistance — would, more or less, offset each other.

The fact is disputed because some of the game’s most intelligent minds — namely, renowned baseball physicists, the very people most capable of determining if the ball is, indeed, different — doubt the ball has changed. It’s imperative I tell you this because they may be right, which would make me (and MLB, for the umpteenth time), well, wrong. Everything that follows assumes the ball has changed. Maybe this meshes with what you’ve witnessed, maybe it doesn’t. This is simply one stupid man’s interpretation of the data available to us thus far.

Early returns suggest MLB accomplished what it set out to accomplish. We can use weighted on-base average on contact (wOBAcon) to describe hitter production on balls in play, aka batted ball events (BBE). The average hitter is slightly less productive in 2021 than in past years, but not egregiously so, as shown below. Also, it’s only April; as the weather warms, so should be the bats. It’s reasonable to expect 2021’s league-wide wOBAcon value to climb a few ticks before year’s end.

wOBAcon by Year, Statcast Era (2015-)
Year wOBAcon
2015 0.361
2016 0.369
2017 0.373
2018 0.368
2019 0.378
2020 0.375
2021 0.364

In short, to the naked, unskeptical eye, hardly anything has changed. But the clothed, skeptical eye of the typical FanGraphs reader — and certainly of a fantasy player who routinely uses Statcast data and leverages advanced analytics to their advantage — may have noticed other collateral damage: every hitter and his mother set a personal record for maximum exit velocity (max EV), including the GOAT, Mike Trout. Giancarlo Stanton seems to set and promptly re-set some kind of league-wide exit velocity (EV) record with each passing game.

Statcast purveys a series of metrics the community fondly calls “xStats,” headlined by its expected wOBA (xwOBA) metric. xStats are intended to describe the performance a hitter (or pitcher) deserved based on the outcomes we’ve already observed. When isolated to only BBE, xwOBA becomes xwOBAcon and allows the user to compare a hitter’s actual production (wOBAcon) to his deserved production (xwOBAcon). They can be powerful (albeit misused) tools in one’s tool belt.

The table below shows the difference between wOBAcon and xwOBAcon by year. One of these things is not like the others.

Difference, wOBAcon and xwOBAcon, by Year
Year wOBAcon – xwOBAcon
2015 +0.014
2016 +0.012
2017 +0.008
2018 +0.006
2019 +0.006
2020 -0.003
2021 -0.029

But the weather! Yes, cold April weather should affect production. So, I compared wOBAcon and xwOBAcon through May 1 of each year. This provides more of an apples-to-apples comparison, whereas the previous table compared only April of 2021 to the entireties of previous years, which included their warm summers. Suddenly, wOBAcon trails xwOBAcon — this is what the weather argument would have posited to begin with — but still by only a fraction of the margin that it does in 2021.

Difference, wOBAcon and xwOBAcon, by Year
Year wOBAcon – xwOBAcon
2015 +0.014
2016 -0.003
2017 -0.009
2018 -0.011
2019 -0.009
2020 n/a
2021 -0.029

The Statcast team has dealt with this issue every year. Each season begets a new run-scoring environment, yet Statcast’s xStats are predicated upon the run-scoring environment of previous seasons. Only until the current season has terminated, or is at least sufficiently far-enough along, can the Statcast folks recalibrate their models. It is what it is.

That, until further notice, still leaves us fantasy nerds in a bind. The margin between actual and deserved production is about 30 points of wOBAcon. However, we cannot, should not, simply subtract 30 points from each hitter’s deserved outcomes to make it match his actuals. The bouncier ball and the increased drag affects batted balls differently. Ground ball hitters (or even line drive hitters) might be affected less by the new ball than fly ball hitters.

I am not equipped with the requisite intellect, skills, or time to refine xwOBAcon and make it usable for the masses. However, I am equipped with just enough of these traits to refine barrel percentage, a Statcast metric that calculates the frequency with which a batter hits a well-struck ball at an optimal launch angle. Barrels are consistently highly productive, and their frequency correlates strongly with both actual and deserved hitter production.

Barrels rely on a baseline EV threshold — 97.5 miles per hour, if I’m not mistaken. I outline the process for calculating barrels here; the farther a batted ball strays from a range of optimal launch angles, the more EV is required to make the batted ball productive.

Unfortunately, this baseline EV loses its meaning if the ball cultivates “harder” hits simply by being bouncier. For the first time during the Statcast era, batted balls with EVs of 98 mph or greater comprise more than 5% of all pitches — and that’s all while hitters are striking out at a historically high clip. It leaves us with this conundrum: for the first time during the Statcast era, an increase in barrels does not coincide with an increase in hitter productivity. Barrel rate and wOBAcon through April 30 of each year, to control (however crudely) for those dastardly weather-related effects:

wOBAcon vs. Barrel% (April of Each Year)
Year wOBAcon Barrel%
2015 0.350 5.0%
2016 0.360 6.7%
2017 0.360 6.7%
2018 0.364 7.8%
2019 0.369 8.0%
2020 n/a n/a
2021 0.364 8.5%

The trendline is obvious. The two metrics increase in lockstep until 2021 when they diverge. Thanks to the ball being bouncier, “hard” hits are coming more cheaply than ever — yet the higher seams, causing more drag, are suppressing the efficacy of these “hard” hits. Predictably, production specifically on barrels has taken a hit, too, trailing prior Statcast-era seasons by anywhere from 50 to 150 points of wOBAcon.

I propose a simple, temporary fix: increase the minimum EV threshold, and all other dynamic calculations therein, just one mile per hour, from 97.5 mph to 98.5 mph:

if EV < 98.5 mph, then barrel = no
if LA > 25.5° and LA < 30.5°, then barrel = yes
if LA < 25.5° and (25.5 – LA) < (EV – 98.5), then barrel = yes
if LA > 30.5° and ((LA – 30.5) * 2) < ((EV – 98.5) * 3), then barrel = yes
if EV > 98.5 mph but none of these apply, then barrel = no

This change to the algorithm increases league-wide wOBAcon on barrels by 50 points and reduces barrel rate by 1.3%, making both nearly identical to the offensive context we witnessed in 2018 (which also featured an overall wOBAcon of 0.364):

2018: 7.8% barrels, 1.375 wOBAcon on barrels
2021 before: 8.5% barrels, 1.321 wOBAcon on barrels
2021 after: 7.8% barrels, 1.379 wOBAcon on barrels

It’s a small change, but it should now permit us to compare barrel rate year over to year to assess who has actually improved rather than be duped by increased EVs across baseball.

The table below shows, for all hitters with at least 40 BBE through April 30, hitters’ barrel rates before and after the change, sorted by who it impacted most. I can’t afford to update this table routinely, so I will add it to the ‘BBE’ tab of my Pitch Leaderboard, where this “new” barrel rate will replace the original, until further notice. (The “new” barrel rate will go live later today, so if you click through right after this was published, it might not be ready yet.)

Enjoy!

Change in Barrel% (Min. 40 BBE)
Hitter Name BBE Old Barrel% New Barrel% Change
Matt Chapman 54 14.8% 9.3% -5.6%
Tyler Naquin 45 17.8% 13.3% -4.4%
Franmil Reyes 54 24.1% 20.4% -3.7%
Colin Moran 58 12.1% 8.6% -3.4%
Shohei Ohtani 61 24.6% 21.3% -3.3%
Randal Grichuk 63 11.1% 7.9% -3.2%
Nick Solak 65 15.4% 12.3% -3.1%
Rhys Hoskins 66 18.2% 15.2% -3.0%
Rafael Devers 69 21.7% 18.8% -2.9%
Cesar Hernandez 69 8.7% 5.8% -2.9%
Ryan McMahon 74 10.8% 8.1% -2.7%
Xander Bogaerts 75 10.7% 8.0% -2.7%
David Peralta 78 9.0% 6.4% -2.6%
Brandon Nimmo 40 7.5% 5.0% -2.5%
Jose Adolis Garcia 42 21.4% 19.0% -2.4%
Brandon Belt 42 19.0% 16.7% -2.4%
Keston Hiura 42 11.9% 9.5% -2.4%
Luis Urias 42 11.9% 9.5% -2.4%
Zach McKinstry 41 9.8% 7.3% -2.4%
Leury Garcia 42 4.8% 2.4% -2.4%
Fernando Tatis Jr. 44 22.7% 20.5% -2.3%
Juan Soto 44 18.2% 15.9% -2.3%
Francisco Mejia 44 6.8% 4.5% -2.3%
Mitch Moreland 43 4.7% 2.3% -2.3%
Pete Alonso 45 20.0% 17.8% -2.2%
Rowdy Tellez 45 13.3% 11.1% -2.2%
Adam Duvall 48 12.5% 10.4% -2.1%
Byron Buxton 50 24.0% 22.0% -2.0%
Yordan Alvarez 51 11.8% 9.8% -2.0%
Alex Dickerson 50 10.0% 8.0% -2.0%
Jonathan India 50 10.0% 8.0% -2.0%
Mike Yastrzemski 50 10.0% 8.0% -2.0%
Brandon Crawford 52 13.5% 11.5% -1.9%
Wil Myers 54 13.0% 11.1% -1.9%
Tommy Pham 52 11.5% 9.6% -1.9%
Lourdes Gurriel 54 7.4% 5.6% -1.9%
Tommy La Stella 54 7.4% 5.6% -1.9%
Willson Contreras 55 14.5% 12.7% -1.8%
Brandon Lowe 57 14.0% 12.3% -1.8%
Paul DeJong 55 12.7% 10.9% -1.8%
C.J. Cron 57 12.3% 10.5% -1.8%
Travis d’Arnaud 57 10.5% 8.8% -1.8%
Omar Narvaez 57 8.8% 7.0% -1.8%
Jonathan Schoop 56 7.1% 5.4% -1.8%
Yoan Moncada 58 10.3% 8.6% -1.7%
Asdrubal Cabrera 59 8.5% 6.8% -1.7%
Charlie Blackmon 59 8.5% 6.8% -1.7%
Kris Bryant 63 12.7% 11.1% -1.6%
Vladimir Guerrero Jr. 63 12.7% 11.1% -1.6%
Ramon Laureano 64 12.5% 10.9% -1.6%
Giovanny Urshela 62 8.1% 6.5% -1.6%
Joey Wendle 63 4.8% 3.2% -1.6%
Luis Arraez 64 3.1% 1.6% -1.6%
Alec Bohm 67 10.4% 9.0% -1.5%
Marcell Ozuna 68 8.8% 7.4% -1.5%
Mookie Betts 67 4.5% 3.0% -1.5%
Jed Lowrie 72 9.7% 8.3% -1.4%
Anthony Rizzo 74 8.1% 6.8% -1.4%
Paul Goldschmidt 73 8.2% 6.8% -1.4%
Gleyber Torres 72 4.2% 2.8% -1.4%
Jose Ramirez 76 13.2% 11.8% -1.3%
Nolan Arenado 78 9.0% 7.7% -1.3%
Jake Cronenworth 79 5.1% 3.8% -1.3%
Corey Seager 81 11.1% 9.9% -1.2%
Whit Merrifield 86 3.5% 2.3% -1.2%
Bryce Harper 59 22.0% 22.0% 0.0%
Mike Trout 47 21.3% 21.3% 0.0%
Nelson Cruz 61 21.3% 21.3% 0.0%
Jazz Chisholm 45 20.0% 20.0% 0.0%
Evan Longoria 47 19.1% 19.1% 0.0%
Javier Baez 47 19.1% 19.1% 0.0%
Aaron Judge 53 18.9% 18.9% 0.0%
Jesse Winker 59 18.6% 18.6% 0.0%
Matt Olson 66 18.2% 18.2% 0.0%
Ronald Acuna 72 18.1% 18.1% 0.0%
Carson Kelly 40 17.5% 17.5% 0.0%
Bobby Dalbec 42 16.7% 16.7% 0.0%
Kyle Seager 78 16.7% 16.7% 0.0%
Will Smith 47 14.9% 14.9% 0.0%
Chris Taylor 54 14.8% 14.8% 0.0%
Giancarlo Stanton 61 14.8% 14.8% 0.0%
Bo Bichette 64 14.1% 14.1% 0.0%
Gregory Polanco 43 14.0% 14.0% 0.0%
J.T. Realmuto 57 14.0% 14.0% 0.0%
Yadier Molina 51 13.7% 13.7% 0.0%
Willy Adames 52 13.5% 13.5% 0.0%
Nathaniel Lowe 67 13.4% 13.4% 0.0%
J.D. Martinez 68 13.2% 13.2% 0.0%
Salvador Perez 68 13.2% 13.2% 0.0%
Dominic Smith 46 13.0% 13.0% 0.0%
Avisail Garcia 55 12.7% 12.7% 0.0%
Joey Votto 71 12.7% 12.7% 0.0%
Justin Turner 71 12.7% 12.7% 0.0%
Trey Mancini 71 12.7% 12.7% 0.0%
Wilson Ramos 63 12.7% 12.7% 0.0%
Mark Canha 65 12.3% 12.3% 0.0%
Jorge Soler 49 12.2% 12.2% 0.0%
Yermin Mercedes 67 11.9% 11.9% 0.0%
Carlos Santana 68 11.8% 11.8% 0.0%
Eduardo Escobar 76 11.8% 11.8% 0.0%
Eugenio Suarez 51 11.8% 11.8% 0.0%
Justin Upton 51 11.8% 11.8% 0.0%
Mitch Haniger 76 11.8% 11.8% 0.0%
Austin Meadows 60 11.7% 11.7% 0.0%
Jared Walsh 61 11.5% 11.5% 0.0%
Trea Turner 61 11.5% 11.5% 0.0%
Luis Robert 62 11.3% 11.3% 0.0%
Ty France 71 11.3% 11.3% 0.0%
Brian Anderson 45 11.1% 11.1% 0.0%
Nicholas Castellanos 72 11.1% 11.1% 0.0%
David Bote 46 10.9% 10.9% 0.0%
Hunter Dozier 46 10.9% 10.9% 0.0%
Freddie Freeman 74 10.8% 10.8% 0.0%
Buster Posey 47 10.6% 10.6% 0.0%
Kyle Tucker 76 10.5% 10.5% 0.0%
Ryan Mountcastle 58 10.3% 10.3% 0.0%
Aledmys Diaz 41 9.8% 9.8% 0.0%
Yasmani Grandal 41 9.8% 9.8% 0.0%
Trevor Story 72 9.7% 9.7% 0.0%
Anthony Santander 42 9.5% 9.5% 0.0%
Jose Altuve 53 9.4% 9.4% 0.0%
Pavin Smith 64 9.4% 9.4% 0.0%
Billy McKinney 43 9.3% 9.3% 0.0%
Michael Taylor 54 9.3% 9.3% 0.0%
Marcus Semien 65 9.2% 9.2% 0.0%
Bryan Reynolds 66 9.1% 9.1% 0.0%
Manny Machado 77 9.1% 9.1% 0.0%
Robbie Grossman 55 9.1% 9.1% 0.0%
Starling Marte 44 9.1% 9.1% 0.0%
Ian Happ 45 8.9% 8.9% 0.0%
Jacob Stallings 45 8.9% 8.9% 0.0%
Josh Naylor 56 8.9% 8.9% 0.0%
Michael Conforto 45 8.9% 8.9% 0.0%
Aaron Hicks 57 8.8% 8.8% 0.0%
Max Muncy 57 8.8% 8.8% 0.0%
Travis Shaw 57 8.8% 8.8% 0.0%
Albert Pujols 59 8.5% 8.5% 0.0%
Trent Grisham 47 8.5% 8.5% 0.0%
Dylan Carlson 61 8.2% 8.2% 0.0%
Erik Gonzalez 61 8.2% 8.2% 0.0%
Phillip Evans 61 8.2% 8.2% 0.0%
Dansby Swanson 62 8.1% 8.1% 0.0%
Enrique Hernandez 77 7.8% 7.8% 0.0%
Jose Abreu 64 7.8% 7.8% 0.0%
Yulieski Gurriel 79 7.6% 7.6% 0.0%
Clint Frazier 40 7.5% 7.5% 0.0%
Rougned Odor 40 7.5% 7.5% 0.0%
Garrett Cooper 42 7.1% 7.1% 0.0%
Michael Brantley 70 7.1% 7.1% 0.0%
Ozzie Albies 70 7.1% 7.1% 0.0%
Gary Sanchez 43 7.0% 7.0% 0.0%
Luis Torrens 43 7.0% 7.0% 0.0%
Tim Anderson 43 7.0% 7.0% 0.0%
Alex Verdugo 74 6.8% 6.8% 0.0%
David Dahl 59 6.8% 6.8% 0.0%
Austin Slater 45 6.7% 6.7% 0.0%
Carlos Correa 80 6.3% 6.3% 0.0%
Adam Eaton 65 6.2% 6.2% 0.0%
Jeff McNeil 49 6.1% 6.1% 0.0%
Eddie Rosario 68 5.9% 5.9% 0.0%
Jean Segura 51 5.9% 5.9% 0.0%
Jesus Aguilar 68 5.9% 5.9% 0.0%
Willians Astudillo 52 5.8% 5.8% 0.0%
Jose Trevino 53 5.7% 5.7% 0.0%
Austin Riley 54 5.6% 5.6% 0.0%
Freddy Galvis 54 5.6% 5.6% 0.0%
Nick Senzel 54 5.6% 5.6% 0.0%
Maikel Franco 74 5.4% 5.4% 0.0%
Jason Heyward 58 5.2% 5.2% 0.0%
Alex Bregman 59 5.1% 5.1% 0.0%
Randy Arozarena 59 5.1% 5.1% 0.0%
Donovan Solano 40 5.0% 5.0% 0.0%
Manuel Margot 60 5.0% 5.0% 0.0%
Hunter Renfroe 42 4.8% 4.8% 0.0%
Joey Gallo 42 4.8% 4.8% 0.0%
Yandy Diaz 62 4.8% 4.8% 0.0%
Garrett Hampson 65 4.6% 4.6% 0.0%
Starlin Castro 65 4.6% 4.6% 0.0%
A.J. Pollock 47 4.3% 4.3% 0.0%
Jorge Polanco 70 4.3% 4.3% 0.0%
Yonathan Daza 46 4.3% 4.3% 0.0%
Jose Iglesias 75 4.0% 4.0% 0.0%
Cedric Mullins 77 3.9% 3.9% 0.0%
Andrew Benintendi 57 3.5% 3.5% 0.0%
Corey Dickerson 59 3.4% 3.4% 0.0%
Isiah Kiner-Falefa 87 3.4% 3.4% 0.0%
Didi Gregorius 63 3.2% 3.2% 0.0%
Willi Castro 63 3.2% 3.2% 0.0%
Christian Vazquez 64 3.1% 3.1% 0.0%
Jeimer Candelario 72 2.8% 2.8% 0.0%
Raimel Tapia 74 2.7% 2.7% 0.0%
DJ LeMahieu 76 2.6% 2.6% 0.0%
Tim Locastro 40 2.5% 2.5% 0.0%
Yoshitomo Tsutsugo 40 2.5% 2.5% 0.0%
Eric Hosmer 83 2.4% 2.4% 0.0%
Cavan Biggio 43 2.3% 2.3% 0.0%
Josh Harrison 44 2.3% 2.3% 0.0%
Nick Ahmed 46 2.2% 2.2% 0.0%
Pedro Severino 45 2.2% 2.2% 0.0%
Victor Robles 46 2.2% 2.2% 0.0%
Amed Rosario 51 2.0% 2.0% 0.0%
Evan White 50 2.0% 2.0% 0.0%
Wilmer Flores 50 2.0% 2.0% 0.0%
Andrew McCutchen 52 1.9% 1.9% 0.0%
Jackie Bradley Jr. 57 1.8% 1.8% 0.0%
Marwin Gonzalez 55 1.8% 1.8% 0.0%
Francisco Lindor 61 1.6% 1.6% 0.0%
Elvis Andrus 66 1.5% 1.5% 0.0%
Miguel Rojas 68 1.5% 1.5% 0.0%
Kevin Newman 74 1.4% 1.4% 0.0%
Tommy Edman 91 1.1% 1.1% 0.0%
Adam Frazier 82 0.0% 0.0% 0.0%
David Fletcher 92 0.0% 0.0% 0.0%
Hanser Alberto 43 0.0% 0.0% 0.0%
J.P. Crawford 68 0.0% 0.0% 0.0%
Josh Fuentes 53 0.0% 0.0% 0.0%
Jurickson Profar 68 0.0% 0.0% 0.0%
Myles Straw 65 0.0% 0.0% 0.0%
Nick Madrigal 70 0.0% 0.0% 0.0%
Nicky Lopez 60 0.0% 0.0% 0.0%
Click headers to sort!
(Default sort: “Difference” ascending, “New Barrel%” descending)





Currently investigating the relationship between pitcher effectiveness and beard density. Two-time FSWA award winner, including 2018 Baseball Writer of the Year, and 8-time award finalist. Previously featured in Lindy's Sports' Fantasy Baseball magazine (2018, 2019). Tout Wars competitor. Biased toward a nicely rolled baseball pant.

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drewcorbedcallahan Recent comment authors
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edcallahan
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edcallahan

Every batter wants to increase their hard hit rate but also reduce their ground ball numbers. So why not use bats with a larger diameter but, and this is the important part, saw off the lower third of the bat? Better to swing and whiff than hit into a double play.

I think that I am on to something here; way to improve the game without changing the design of the ball.

drewcorb
Member
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drewcorb

Larger diameter bats mean less backspin imparted on the ball when you get under it. So even if you kept the cross-sectional area the same as a regular bat but cut out the bottom portion, you may reduce the distance traveled by fly balls.

On the other hand, you could keep the bat diameter the same, but still cut that bottom portion. Then you wouldn’t have reduced distance on the fly balls. But it’s not obvious to me that converting ground balls into whiffs would improve hitter performance either. A soft ground ball doesn’t have a high expected average, but increasing the number of strikes in the count by 1 also has a negative effect. So I doubt it would be beneficial, but that’s just my gut.