A Late Primer on MiLB Infield Fly Ball Rate (IFFB%)

The impetus for this post arises from a Tweet by our very own Al Melchior:

This is, in no way, meant to shame Melchior; if anything, he has afforded us a valuable learning opportunity, especially because it became clear to me there likely exists a large swath of FanGraphs users who routinely misinterpret the relatively new Minor League batted ball data. (Through no fault of their own, by the way. The new data didn’t come with a user’s guide or anything. We have been left to our own devices, and it’s easy to assume such clean data comes without warts.)

Melchior’s Tweet isn’t the first time I’ve seen comments like his. I’ve seen similar disappointed comments about hitter infield fly ball rates (IFFB%), and I’ve seen equally-and-oppositely excited comments about pitcher pop-up rates (as recently as a few days ago), generally all as they relate to batting average on balls in play (BABIP). I quickly picked up on this discrepancy paging through MiLB batted ball leaderboards, noticing the majority of hitters were hitting pop-ups in the double-digits.

The best play when interpreting Minor League batted ball data is to index it. The following tables present (1) the average IFFB% by league and year, and (2) those same rates, but indexed.

Average IFFB%, 2013-17
Season MLB AAA AA
2013 9.7% 19.8% 20.7%
2014 9.6% 19.5% 19.4%
2015 9.5% 20.3% 21.5%
2016 9.7% 20.6% 20.9%
2017 9.6% 20.2% 21.2%
Indexed IFFB%, 2013-17
Season MLB AAA AA
2013 1.00 0.49 0.47
2014 1.00 0.49 0.49
2015 1.00 0.47 0.44
2016 1.00 0.47 0.46
2017 1.00 0.48 0.45

(I neglected to venture farther down the Minor League ladder for simplicity’s sake.)

Interpretation: use indexed rates like a multiplier, e.g., 2013 IFFB%:

  • MLB: 9.7% * 1.00 = 9.7% IFFB%
  • AAA: 19.8% * 0.49 = 9.7% MLB-equivalent IFFB%
  • AA: 20.7% * 0.47 = 9.7% MLB-equivalent IFFB%

However, you may have noticed an easier shorthand methodology, so I’ll suggest doing this instead: simply divide the MiLB IFFB% by two. It’s not perfect, but it’ll be dang close, and it errs on the side of caution (i.e., very slightly inflating the MLB-equivalent IFFB%).

That the MiLB rates are consistent not only year to year but also between levels suggests this is an appropriate way to digest the data. However, there’s an outside chance the reason why infield fly rates are so high in the minors and so low in the majors is selection bias: the best players can simply hack it, and that’s why they turn into productive big-leaguers. To be clear, this would essentially boil Major League success down to being able to not pop up — a gross oversimplification.

I tested the theory anyway, just to put it to rest. Now, there’s no easy or right way to do this; players jump back and forth between the minors, and not everyone produces Minor League sample sizes large enough to feel confident in their reliability. To mitigate any issues related to this, I limited my sample to players who, in the last five years, recorded 300 plate appearances at both a particular Minor League level (e.g., Triple-A) and the Major League level, using the most recent sample for each. For example, Jorge Soler’s 2017 stint at Triple-A is compared to his 2015 stint with the Cubs.

Selection Bias QC (Click to expand)
Selection Bias QC
Player Name MiLB Level MiLB Year MiLB IFFB% MLB Year MLB IFFB% MLB xIFFB% IFFB% ─ indexed
Stephen Vogt AAA 2013 16.7% 2017 19.4% 8.2% -11.2%
Chris Owings AAA 2013 8.5% 2017 15.3% 4.2% -11.1%
Jon Singleton AAA 2016 21.6% 2014 20.7% 10.1% -10.6%
Carlos Asuaje AAA 2016 12.2% 2017 15.9% 5.7% -10.2%
Cheslor Cuthbert AAA 2015 18.3% 2016 17.8% 8.6% -9.2%
Pedro Alvarez AAA 2017 16.4% 2016 15.9% 7.8% -8.1%
Adam Engel AA 2016 29.5% 2017 21.3% 13.9% -7.4%
Donovan Solano AAA 2017 11.1% 2014 12.7% 5.3% -7.4%
Sandy Leon AA 2013 23.5% 2017 18.7% 11.5% -7.2%
Yangervis Solarte AAA 2013 21.1% 2017 17.4% 10.3% -7.1%
Jose Peraza AAA 2016 13.3% 2017 13.2% 6.2% -7.0%
James Jones AAA 2016 11.5% 2014 12.0% 5.4% -6.6%
Leonys Martin AAA 2017 10.0% 2016 11.3% 4.8% -6.5%
Nomar Mazara AA 2015 5.4% 2017 8.8% 2.5% -6.3%
Arismendy Alcantara AAA 2016 11.8% 2014 11.8% 5.5% -6.3%
Josh Bell AAA 2016 12.0% 2017 11.8% 5.6% -6.2%
Paul DeJong AA 2016 16.2% 2017 13.5% 7.6% -5.9%
Aaron Altherr AA 2014 12.4% 2017 11.9% 6.1% -5.8%
Matt Chapman AA 2016 23.4% 2017 16.8% 11.0% -5.8%
Chris Colabello AAA 2017 7.6% 2015 9.4% 3.6% -5.8%
Mark Canha AAA 2017 18.3% 2015 14.3% 8.7% -5.6%
Jurickson Profar AAA 2017 16.3% 2016 13.3% 7.8% -5.5%
Ryan Schimpf AA 2015 20.2% 2016 15.0% 9.5% -5.5%
Jake Marisnick AAA 2014 21.0% 2016 15.7% 10.3% -5.4%
Robinson Chirinos AAA 2013 20.3% 2017 15.3% 9.9% -5.4%
Kevin Pillar AAA 2014 17.4% 2017 13.9% 8.5% -5.4%
Jake Smolinski AAA 2013 22.1% 2016 16.1% 10.8% -5.3%
Travis Snider AAA 2017 15.7% 2014 12.7% 7.5% -5.2%
Pete Kozma AAA 2016 25.4% 2013 16.8% 11.9% -4.9%
Hunter Renfroe AAA 2016 22.0% 2017 15.1% 10.3% -4.8%
Brendan Ryan AAA 2017 26.4% 2013 17.3% 12.6% -4.7%
Oswaldo Arcia AAA 2017 13.2% 2014 10.7% 6.3% -4.4%
Tommy Pham AAA 2014 5.3% 2017 7.0% 2.6% -4.4%
Ketel Marte AAA 2017 16.7% 2016 12.2% 8.0% -4.2%
Blake Swihart AA 2014 16.2% 2015 12.1% 7.9% -4.2%
Justin Bour AAA 2014 15.7% 2017 11.5% 7.7% -3.8%
Cody Asche AAA 2017 16.5% 2015 11.6% 7.9% -3.7%
Steve Lombardozzi AAA 2017 11.6% 2013 9.1% 5.5% -3.6%
Kolten Wong AAA 2013 15.1% 2017 10.9% 7.4% -3.5%
Ronald Torreyes AAA 2014 17.6% 2017 12.0% 8.6% -3.4%
Johnny Giavotella AAA 2017 12.7% 2016 9.3% 6.1% -3.2%
Kevin Kiermaier AA 2013 18.3% 2017 12.2% 9.0% -3.2%
Jorge Bonifacio AAA 2016 20.6% 2017 12.9% 9.7% -3.2%
Michael Taylor AA 2014 10.6% 2017 8.4% 5.2% -3.2%
Tyler Naquin AAA 2017 10.3% 2016 7.9% 4.9% -3.0%
Joc Pederson AAA 2014 17.1% 2017 11.3% 8.4% -2.9%
Billy Burns AAA 2017 42.3% 2016 23.0% 20.2% -2.8%
Jackie Bradley Jr. AAA 2015 12.1% 2017 8.5% 5.7% -2.8%
Gorkys Hernandez AAA 2016 15.3% 2017 9.7% 7.2% -2.5%
Cody Bellinger AA 2016 12.6% 2017 8.4% 5.9% -2.5%
Jose Martinez AAA 2016 10.9% 2017 7.5% 5.1% -2.4%
Ryan Goins AAA 2014 15.5% 2017 9.8% 7.6% -2.2%
Wilmer Flores AAA 2013 17.4% 2017 10.6% 8.5% -2.1%
Andrew Romine AAA 2013 16.9% 2017 10.3% 8.3% -2.0%
Miguel Rojas AA 2013 28.4% 2017 15.9% 13.9% -2.0%
Trey Mancini AAA 2016 11.5% 2017 7.4% 5.4% -2.0%
Joey Gallo AAA 2016 21.2% 2017 11.8% 10.0% -1.8%
Bradley Zimmer AA 2016 13.6% 2017 8.2% 6.4% -1.8%
Ben Gamel AAA 2016 16.1% 2017 9.2% 7.6% -1.6%
Nick Franklin AAA 2014 14.2% 2013 8.6% 7.0% -1.6%
Javier Baez AAA 2015 16.9% 2017 9.4% 7.9% -1.5%
J.P. Arencibia AAA 2016 25.0% 2013 13.2% 11.7% -1.5%
Jonathan Villar AAA 2015 26.3% 2017 13.8% 12.3% -1.5%
C.J. Cron AA 2013 26.8% 2017 14.5% 13.1% -1.4%
Devon Travis AA 2014 17.4% 2016 9.9% 8.5% -1.4%
Francisco Lindor AA 2014 11.1% 2017 6.8% 5.4% -1.4%
Dustin Ackley AAA 2017 16.7% 2014 9.3% 8.0% -1.3%
Domonic Brown AAA 2016 22.6% 2014 11.9% 10.6% -1.3%
Casey McGehee AAA 2016 17.0% 2014 9.2% 8.0% -1.2%
Adam Rosales AAA 2014 20.7% 2017 11.3% 10.2% -1.1%
Maikel Franco AAA 2014 30.9% 2017 16.3% 15.2% -1.1%
Danny Santana AA 2013 17.7% 2014 9.8% 8.7% -1.1%
Will Venable AAA 2016 29.1% 2015 14.8% 13.7% -1.1%
Stephen Piscotty AAA 2015 15.1% 2017 8.2% 7.1% -1.1%
Adam Frazier AA 2015 4.5% 2017 3.2% 2.1% -1.1%
Willson Contreras AA 2015 18.8% 2017 9.9% 8.8% -1.1%
Jorge Soler AAA 2017 23.5% 2015 12.2% 11.2% -1.0%
Austin Hedges AAA 2016 31.3% 2017 15.7% 14.7% -1.0%
James McCann AAA 2014 15.9% 2017 8.8% 7.8% -1.0%
Trevor Story AA 2015 19.5% 2017 10.1% 9.2% -0.9%
Marcus Semien AAA 2014 18.8% 2017 10.1% 9.2% -0.9%
Preston Tucker AAA 2017 26.2% 2015 13.3% 12.5% -0.8%
Paulo Orlando AAA 2014 18.8% 2016 10.0% 9.2% -0.8%
Eddie Rosario AA 2014 13.3% 2017 7.3% 6.5% -0.8%
Brock Holt AAA 2013 10.5% 2016 5.9% 5.1% -0.8%
Gregory Polanco AAA 2014 19.0% 2017 10.0% 9.3% -0.7%
Cory Spangenberg AA 2014 20.6% 2017 10.7% 10.1% -0.6%
Ender Inciarte AA 2013 15.4% 2017 8.1% 7.5% -0.6%
Whit Merrifield AAA 2016 14.6% 2017 7.4% 6.9% -0.5%
Jimmy Paredes AAA 2014 13.4% 2015 7.1% 6.6% -0.5%
Chris Taylor AAA 2016 8.6% 2017 4.5% 4.0% -0.5%
Nick Ahmed AAA 2014 25.6% 2016 13.0% 12.6% -0.4%
Robbie Grossman AAA 2015 17.6% 2017 8.7% 8.3% -0.4%
Jose Ramirez AA 2013 19.0% 2017 9.7% 9.3% -0.4%
Adonis Garcia AAA 2015 21.3% 2016 10.3% 10.0% -0.3%
Ed Lucas AAA 2016 10.5% 2013 5.1% 4.9% -0.2%
Caleb Joseph AA 2013 15.2% 2015 7.5% 7.4% -0.1%
Chad Pinder AAA 2016 11.0% 2017 5.2% 5.2% 0.0%
Adam Duvall AAA 2015 25.5% 2017 12.0% 12.0% 0.0%
Pedro Florimon AAA 2017 17.6% 2013 8.4% 8.4% 0.0%
Matt Davidson AAA 2016 18.2% 2017 8.5% 8.5% 0.0%
Steven Souza Jr. AAA 2014 14.0% 2017 6.8% 6.9% 0.1%
Dansby Swanson AA 2016 13.8% 2017 6.4% 6.5% 0.1%
Mitch Haniger AAA 2016 17.1% 2017 7.9% 8.0% 0.1%
Jesus Aguilar AAA 2016 15.3% 2017 7.0% 7.2% 0.2%
Jorge Polanco AAA 2016 22.7% 2017 10.3% 10.7% 0.4%
Orlando Arcia AAA 2016 24.8% 2017 11.2% 11.6% 0.4%
Logan Schafer AAA 2017 31.0% 2013 14.3% 14.8% 0.5%
Joe Panik AAA 2014 16.5% 2017 7.6% 8.1% 0.5%
Ben Paulsen AAA 2016 14.9% 2015 6.5% 7.0% 0.5%
Mike Zunino AAA 2016 27.7% 2017 12.5% 13.0% 0.5%
Yolmer Sanchez AAA 2014 13.9% 2017 6.3% 6.8% 0.5%
Ezequiel Carrera AAA 2014 18.6% 2017 8.5% 9.1% 0.6%
Enrique Hernandez AAA 2014 17.7% 2017 8.0% 8.7% 0.7%
Eugenio Suarez AA 2013 24.2% 2017 11.0% 11.8% 0.8%
Jake Lamb AA 2014 14.0% 2017 6.0% 6.9% 0.9%
Nick Castellanos AAA 2013 5.2% 2017 1.6% 2.5% 0.9%
Odubel Herrera AA 2014 20.0% 2017 8.8% 9.8% 1.0%
Junior Lake AAA 2016 23.5% 2014 10.0% 11.0% 1.0%
Max Kepler AA 2015 27.1% 2017 11.5% 12.7% 1.2%
Mikie Mahtook AAA 2015 22.0% 2017 8.9% 10.3% 1.4%
Brandon Guyer AAA 2013 21.2% 2016 8.9% 10.4% 1.5%
Drew Stubbs AAA 2017 15.6% 2014 5.7% 7.4% 1.7%
Gary Sanchez AAA 2016 27.4% 2017 10.8% 12.9% 2.1%
Keon Broxton AAA 2015 12.3% 2017 3.7% 5.8% 2.1%
Jose Pirela AAA 2014 23.4% 2017 9.2% 11.5% 2.3%
Randal Grichuk AAA 2014 21.6% 2017 8.3% 10.6% 2.3%
Domingo Santana AAA 2015 9.4% 2017 2.1% 4.4% 2.3%
Darwin Barney AAA 2015 24.4% 2017 9.1% 11.5% 2.4%
Billy Hamilton AAA 2013 29.9% 2017 12.1% 14.6% 2.5%
George Springer AA 2013 23.5% 2017 8.7% 11.5% 2.8%
Nick Williams AAA 2017 9.5% 2017 1.7% 4.5% 2.8%
Scott Schebler AAA 2016 26.9% 2017 9.7% 12.6% 2.9%
Hernan Perez AAA 2014 27.0% 2017 10.2% 13.2% 3.0%
Delino DeShields AA 2014 27.0% 2017 10.2% 13.2% 3.0%
Eric Young AAA 2017 22.7% 2014 7.7% 10.8% 3.1%
Jeff Francoeur AAA 2014 23.3% 2016 8.3% 11.4% 3.1%
Cesar Hernandez AAA 2013 12.9% 2017 3.1% 6.3% 3.2%
Corey Dickerson AAA 2013 18.3% 2017 5.7% 9.0% 3.3%
Travis Shaw AAA 2015 25.6% 2017 8.6% 12.0% 3.4%
Anthony Gose AAA 2013 22.6% 2015 7.4% 11.1% 3.7%
Manuel Margot AAA 2016 28.0% 2017 9.4% 13.1% 3.7%
Leury Garcia AAA 2016 15.7% 2017 3.6% 7.4% 3.8%
J.T. Realmuto AA 2014 18.8% 2017 5.4% 9.2% 3.8%
Matt Duffy AA 2014 13.1% 2016 2.5% 6.4% 3.9%
Tyler Saladino AAA 2014 23.1% 2016 7.4% 11.3% 3.9%
Aledmys Diaz AA 2015 36.5% 2017 13.2% 17.1% 3.9%
Corey Seager AAA 2015 17.8% 2017 4.4% 8.4% 4.0%
Gordon Beckham AAA 2017 30.0% 2014 10.3% 14.3% 4.0%
Danny Espinosa AAA 2013 32.9% 2016 12.1% 16.1% 4.0%
Ryon Healy AA 2015 23.9% 2017 7.2% 11.2% 4.0%
Kirk Nieuwenhuis AAA 2013 20.7% 2016 6.0% 10.1% 4.1%
Aaron Judge AAA 2016 22.2% 2017 6.2% 10.4% 4.2%
Travis Jankowski AA 2015 15.7% 2016 2.9% 7.4% 4.5%
Eric Thames AAA 2013 18.3% 2017 4.0% 9.0% 5.0%
Scooter Gennett AAA 2013 28.6% 2017 8.5% 14.0% 5.5%
Tommy La Stella AA 2013 13.7% 2014 1.2% 6.7% 5.5%
Trea Turner AAA 2016 21.7% 2017 4.5% 10.2% 5.7%
Dee Gordon AAA 2013 18.1% 2017 2.9% 8.9% 6.0%
Tim Beckham AAA 2013 19.8% 2017 3.7% 9.7% 6.0%
Josh Rutledge AAA 2015 21.7% 2014 3.9% 10.2% 6.3%
Tucker Barnhart AA 2013 23.7% 2017 4.8% 11.6% 6.8%
Brandon Barnes AAA 2017 24.8% 2014 5.0% 11.8% 6.8%
Matt Dominguez AAA 2017 37.7% 2014 11.1% 18.0% 6.9%
Omar Infante AAA 2017 36.4% 2015 10.0% 17.3% 7.3%
Emilio Bonifacio AAA 2016 24.3% 2014 4.0% 11.4% 7.4%
Allen Craig AAA 2015 18.2% 2014 1.1% 8.5% 7.4%
Tim Anderson AA 2015 25.0% 2017 4.2% 11.7% 7.5%
Will Middlebrooks AAA 2017 26.6% 2013 5.1% 12.7% 7.6%
Albert Almora Jr. AAA 2016 30.8% 2017 6.8% 14.5% 7.7%
J.B. Shuck AAA 2017 31.9% 2013 7.3% 15.2% 7.9%
Wilmer Difo AA 2016 30.8% 2017 6.3% 14.5% 8.2%
Christian Vazquez AA 2013 25.8% 2017 4.1% 12.6% 8.5%
Clint Robinson AAA 2017 27.9% 2015 4.5% 13.3% 8.8%
Simple Average 19.4% 9.7% 9.3% -0.4%

The results for the qualified 174 hitters jump all over the place, much like you’d see in a normal distribution of outcomes: the “expected” indexed rate ranges from 11.2 percentage points too low to 8.8 percentage points too high. However, the simple average of the player-specific differences equals -0.4% — that is, almost exactly zero, skewed slightly because it’s not a weighted average and I haphazardly did not normalize across player-seasons. Still, if the quick-and-easy check suffices, I assume the in-depth check would, too.

Circling back to the top, Melchior was correct to be concerned about Willie Calhoun’s issues with pop-ups, but he was probably twice as alarmed as he needed to be. Still, Calhoun should pop-up quite a bit — his IFFB% will likely settle in the mid-teens — which helps explain his low Minor League BABIPs and should encourage you to be bearish on those abilities at the Major League level.

Lastly, why is any of this happening? Who knows — someone does, just not me — but I speculate it’s simply a matter of how pop-ups are classified in the minors more than anything related to measurement or stringer error. If they are true errors, they are rampant, which seems improbable. Accordingly, it is probably just differences in measurement definitions.

Now, go wield your new wisdom!



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Currently investigating the relationship between pitcher effectiveness and beard density. Biased toward a nicely rolled baseball pant. Three-time FSWA finalist, one-time winner. Featured in this year's Lindy's Sports' Fantasy Baseball magazine. Doing everything I can to better understand (fantasy) baseball using only publicly available data.

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NickGerli
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NickGerli

Great post. I’ve always wondered how the batted ball data, as well as the pitch data (swinging strike rate, etc.), was calculated for the minor leagues/