Is Popup Rate a Skill?
When I wrote about Mike Soroka this week, I mentioned that he’s one of the best players in baseball at getting popups. Nearly 20% of the fly balls opponents have hit against him have ended up in an infielder’s glove, one of the best rates in baseball. It’s clear that this is a valuable skill for the Braves — a fifth of Soroka’s fly balls are automatic outs. But there’s a follow-up question there that’s just begging to be asked. Does Soroka have any control over this? Do pitchers in general have any control over how many popups they produce?
This is the kind of question where it’s important to know exactly what you’re asking. FanGraphs has a handy column in our batted ball stats, IFFB%, that looks like it cleanly answers what you’re looking for. Be careful, though! IFFB% refers to the percentage of fly balls that don’t leave the infield, not the percentage of overall balls in play. Let’s use Soroka as an illustration of this, because his extremely high groundball rate will make the example clear. Take a look at Soroka’s batted ball rates this year:
| GB/FB | LD% | GB% | FB% | IFFB% | HR/FB |
|---|---|---|---|---|---|
| 2.97 | 22.0 | 58.4 | 19.7 | 17.6 | 2.9 |
Soroka allows 19.7% fly balls, of which 17.6% are infield fly balls. In other words, roughly 3.5% of balls put in play against Soroka this year have been popups. For me, that helps contextualize what we’re talking about. Lucas Giolito has the highest rate of popups per batted ball in the major leagues this year among qualified starters, a juicy 7.4% (in a lovely bit of symmetry, teammate and other half of the Adam Eaton trade package Reynaldo Lopez is second). Eduardo Rodriguez is last among qualified starters at 0.5%. There’s a spread in how many popups players allow, but it’s not enormous.
Before we delve deeper into popup rates and whether they’re a skill, I’d like to take a quick digression to talk about my thought process when I set out to answer questions in this general vein. The first thing I like to do is look for prior research on the same topic. There’s nothing worse than spending thirty minutes thinking up a fun experimental treatment, only to see that Russell Carleton or Jeff Sullivan answered the question years ago. As it so happens, there’s an excellent article at FanGraphs about whether popups are a skill, written by none other than David Appelman.
The conclusion of this article is straightforward: popups as a percentage of all balls in play are tied to groundball (and fly ball) rate. That makes general sense — the more fly balls, the more chances a pitcher has to get some infield fly balls. Before we go any further, let’s quickly replicate that study using 2016-2018 data to see whether there’s been meaningful movement in relationships since 2010. Here are the groundball and popup rates for every pitcher in baseball who threw 100 innings between 2016 and 2018:

Okay, yeah, that relationship looks to be pretty similar to what Appelman found in 2010. That’s good to see, because if something as fundamental as “more groundballs means fewer infield fly balls” had changed completely in 10 years, baseball analysis would have a tremendously short shelf life. With that out of the way, we can move on to answering variations on that question.
To start things off, here’s a question for you. There’s no question that fly ball and groundball rates are skills that pitchers retain from year to year. Zack Britton isn’t just getting lucky every year to have a high groundball rate, and Chris Young wasn’t lucking into a mountain of fly balls every season. What about popups, though? There are two ways to go about this question. First, we could look at year-over-year changes in the amount of popups players get per ball in play.
To check this out, I took one data point for each back-to-back season where a pitcher threw 40 innings or more in both seasons from 2015-2018. Clayton Kershaw, for example, shows up three times: 2015-2016, 2016-2017, and 2017-2018. Then, I looked at their year one and year two popup rates. Given that groundball rates are consistent from year to year and highly correlated to popup rates, we’d expect year one popup rate to predict year two popup rate reasonably well, and luckily, that appears to be the case.

This relationship has a .20 r-squared; in other words, 20% of the variation in a pitcher’s popup rate can be explained by the previous year’s popup rate. That’s a robust relationship, which makes sense. If you want to know how many popups a pitcher will allow, it’s useful to know how many they’ve allowed in the past.
That question, though, has an obvious answer. Let’s get to a trickier one: if a pitcher has an extremely high IFFB% (popups as a percentage of fly balls) in year one, what does that tell us about year two? In other words, if an outlandish percentage of Mike Soroka’s fly balls don’t leave the infield in year one, should we expect that trend to continue in year two?
The experimental design for this is a variation on what we just looked at. I took the same pitcher-season pairs as above, but this time I looked at IFFB% instead of popup rate. Before looking at the answers, though, let’s talk about what to expect. We should expect less of a relationship than popup rate, because lots of the year-to-year pattern in popup rate is being driven by a pitcher’s other batted-ball tendencies. If a pitcher has a tremendously high groundball rate in year one, that’s likely to continue into year two, which means fewer fly balls and fewer opportunities for popups. The opposite is true as well: pitchers who allow a ton of fly balls have more chances to get popups. IFFB% uses fly balls as a denominator, however, which means that groundball rate doesn’t drive the results much, if at all.
With that said, the result looks pretty clear: there’s nearly no relationship between year-one IFFB% and year-two IFFB%. The r-squared is merely .025 — significantly less than that of popup rate and small to the point of irrelevance. The data look like the statistical version of a Rorschach test:

This isn’t to say that there’s no relationship at all, or that you should completely ignore what a pitcher has done in the past. The top 10% of pitchers, for example, had a year-one IFFB rate of 16.7%, which declined to 10.7% in year two. The bottom 10%, on the other hand, had a 3.1% rate in year one and 8.3% in year two. There’s a positive relationship, which makes sense — it’s just tremendously small and outstripped to large extent by noise.
There are more ways to ponder the question of whether pitchers can exert some influence over whether batters hit popups, but given the tiny year-two variation from the top to bottom 10%, these changes aren’t likely to amount to much. Consider this fact: James Shields allowed more fly balls than any other pitcher in 2018, a whopping 231. There’s no pitcher who could make better use of a skill for inducing popups. The entire gap from bottom 10% to top 10% is 2.4%, or about five popups a year. Are those five popups worth something? Of course! They pale, though, in comparison to the value of getting more strikeouts or walking fewer batters. For comparison’s sake, a strikeout rate change of 0.5% would have been worth as many outs for Shields last year as that 2.4% infield fly ball rate change.
There are other questions that will inevitably come up on the topic of popups, which I’ll briefly answer here, but which merit further study. Do pitchers who get more popups allow fewer home runs per fly ball? A bit in year one, not so much in year two. Do groundball pitchers have a lower IFFB% as a whole than fly ball pitchers? Yes, but not by much. Can IFFB% tell you anything about year-two run prevention? Don’t count on it.
Overall, though, the data pretty much line up. Fly balls become infield fly balls at a varying rate across baseball, but pitchers can’t exert much skill on that rate from one year to the next. If your favorite pitcher is getting a boatload of popups, rejoice! There’s nothing more satisfying than a pitcher pointing straight up as the second baseman camps out to catch a weak fly ball. Don’t count on that skill carrying over, though. Popups as a percentage of all fly balls appear to be essentially the reverse of line drive rate: tremendously valuable (popups to the pitcher, line drives to the batter) but more or less uncontrollable by pitchers.
Ben is a writer at FanGraphs. He can be found on Bluesky @benclemens.
The front page description had me rolling. The analysis had me very interested, even though I saw the conclusion coming; contact rates and types in general don’t seem to be anything a pitcher can control in a sustainable way. Nicely done, Ben.
Just want to say I’m a big fan of your work, Ben. Something about your writing style really appeals to me. Keep up the great work.
Can we get a Fangraphs Tableau Server going, where you publish all these graphics but in an interactive way so I can find out who the outliers are by hovering over them?
That would be awesome. If there isn’t budget for that, is it possible to make the data sets available?
I suppose in some cases there will be proprietary data involved that can’t be shared freely; it’d certainly be fine to withhold the data in that case 🙂
I warn you that the spreadsheet I used to make this is a bit of a disgrace, and I’m doing it with Index/Match rather than some gorgeous pandas design, but here it is:
https://docs.google.com/spreadsheets/d/15OwtFwqKBKe79F8l5y4PdBfa1-Db6UaHwG_6mFb4d9Y/edit?usp=sharing
All taken from FanGraphs’ leaderboards, but with the work of slicing and dicing all partially done already.
If there were a button I could press to receive a scatterplot of two randomly chosen (dimensionally compatible) stats, I would press that like a rat for cocaine.
And there could be a way to share and upvote interesting ones.
This was my question on the last post! Thanks for answering it so definitively.
Haha yeah I was about halfway through researching this when I saw your post, so I thought about replying right away, but thought I’d save a surprise for today. Definitely seems like a good question, and something that is overall worth researching, so I’m glad we were on the same page there.
If you look at last years leaderboard the top iffb guys seem to be more FB guys as the article said
I think this makes sense not just because of the general fb tendency but also because the straight high spin 4 seamer causes you to hit under the ball a lot. Gb pitchers tend to be hit either line drive or rolled over but not so much pop up or high flyball.
The top gb guys seem to have average to below pop up rates
You can also see that most gb pitchers don’t have super low hr/fb rates which makes sense because if you lift them it tends to be squared up while high 4 seamers tend to be hit under with too much backspin.
This makes me sceptical about soroka. He is a good pitcher and the command and gb rate is sustainable but the hr/fb and babip is not typical of a pitcher of his type.
Still think he can be an above average starter long term but maybe not a star.
Yeah. Mariano Rivera was the rare groundball-oriented guy who had the skill. 29.9% flyball rate but a 16.9% IFFB%. No season below 11.1% IFFB%, average of 16.9%.
Doolitte (8 years, one year 10.5% IFFB%, one 13.5%, rest above 15%, average 16.2%), Chris Young, guys like that, flyball guys, but also have a skill it seems to have a large amount of those flyballs be infield flies. It’s a rare skill, but some seem to have it, unless just unusually lucky. Rivera was above average, often way above average, 12 years in a row, the entire portion of his career that spanned the fangraphs era.
Relievers may have an advantage because more relievers seem to have the skill, and perhaps because hitters don’t see them twice.
I agree that Soroka isn’t likely to actually have the skill, and you’d need several years to back it up.
How did you choose the 40 IP threshold? Do the correlations change much if you raise it?
How did I choose it? More or less arbitrarily. It didn’t seem to matter much where I cut it off, so I just went for it. For comparison, 50 IP minimum and 60 IP minimum didn’t look any different. Just for funsies I cut it off at 100 IP — the relationship got marginally stronger but the R-squared for IFFB% was still about .045.
What about career IFFB% (min. 500 IP) predicting future IFFB%?
Not sure that most readers really thought 40 IP (~0 to 5 IFFB) would predict much of anything.
Never understood why there is the belief that pitchers can not prevent home runs either making xFIP completely invalid.
Can someone enlighten me?
They’ve censored the link, but google: “Evaluating Pitchers and Home Runs” It’s an article from Lookout Landing
Pop ups per fly ball is a shrinking sample size. So of course it’s going to be more random. Its a formula that’s dependent is only FB outcomes. Which of itself varies from pitcher to pitcher wildly. You proved how useless IFFB as a stat is. While at least PU rate has some merit.
Mostly about hang-time FBs. Gives your defenders more time to react. Being in the IF or OF, is just, whatever. At least when it comes to BABIP potential. HR rates, a different story.
Chris Young seems to have had the skill. 15.2% career IFFB%. A heavy flyballer, and if that’s the rate of infield pop-ups to overall flyballs, it’s pretty high. 13th of 754 career qualifiers in the fangraphs career leaderboards. His HR/FB was 9.1%, 128th lowest of 754, so despite being a heavy flyball pitcher he could somewhat keep the HR/FB somewhat reasonable. Helps explain why his career ERA is a run lower than his xFIP..
Al Leiter only pitched a few years in the fangraphs era, but had a IFFB% of 15.6% (8th) and hr/fb of 8.4% (64th). HR/FB rates were lower back in his day. And he somehow could post 3.50ish ERAs with 5+ xFIPs near the end.
Tyler Clippard 16.2% IFFB% (5th) and 9.2 HR/FB% (136th) carer ERA 3.18, xFIP 4.18.
Seems relievers more likely to have this ability. Mariano Rivera, not a flyball pitcher, 16.9% IFFB%, 2nd of 754 qualifiers, HR/FB% 6.5%, 8th lowest. Of course he was great.
Sean Doolittle, is a flyball pitcher, 16.2% IFFB%6th, 6.8% HR/FB% 14th.
Aroldis Chapman, 35th in IFFB% (highest), 33rd in HR/FB% (lowest).
The immortal Kiko Calero, 14.2% IFFB%, 24th, 6.7% HR/FB% 12th. Go figure.
Johan Santana, 13% IFFB%. 60th of all qualifiers. Limited to starters with 500 innings or more, 25th of 322. Others high up, top 10 for starters, Marco Estrada, Rich Hill, Orlando Hernandez.
A lot of this may be random. You have a huge set of pitchers, overall it’s going to smooth out probably. There will be outliers.
Chris Young, Clippard, Doolittle, Leiter, Rivera, it seems like a skill. Rivera being fairly unique in that it didn’t go with a flyball heavy profile. Doolittle just has a great blend of skills.
I expected Doolittle’s IFFB% to drop moving from Oakland with huge foul ground to Washington with normal foul ground. It didn’t. Barry Zito’s IFFB% did plummet moving from Oakland to San Francisco. I’m not sure whether IFFB% includes foul pops or not. I couldn’t tell from the glossary. More broadly, I’m wondering how park effects factor into this.
Good point re: Oakland’s foul territory. But it looks like Doolittle does have the skill. It’s rare, but I’m pretty confident there are some guys that have it.
Follow up article: pitchers who increase their inside corner pitching rates (zones 11, 14, 19, 21, 24, 27 to right handed hitters, and zones 13, 16, 19, 23, 26, 29 to lefties), improve their IFFB%. And what’s the likelihood the increase in inside corner pitching sticks year to year…
Their HBP will increase, too, but I bet that the trade off, with regards to FIP that includes IFFB (or SIERA), is worth it!