# Infield Flies, FIP, and WAR

If you haven’t already, go read David Laurila’s Q&A this morning with Dan Rosenheck, writer for the Economist and New York Times, who gave a presentation on predicting BABIP at the Sloan Conference last week. In that piece, Rosenheck notes that he created a model using just two variables — infield fly rate and rate of contact on strikes — that helped explain 15% of the variance in a pitcher’s future BABIP. The part about infield flies helping reduce BABIP has been noted before, as others have created takeoffs of ERA estimators that incorporate batted ball data — SIERA, tRA, bbFIP, etc… — and Steve Staude wrote a Community Blog post on this topic back in October, also identifying infield fly rate as a significant explanatory tool for BABIP. The potential explanatory effects of inducing popups and the link to Z-Contact% is fascinating, however, and makes Rosenheck’s study a real step forward.

It makes perfect sense that infield flies would help explain some of the variation in a pitcher’s BABIP, of course, since infield flies are almost always outs. In fact, in 2012, there were 4,377 batted balls that were categorized as infield flies in Major League Baseball, and only 13 of those went for base hits. Another 28 did not result in an out due to an error by the fielder, but even with 41 non-outs, that leaves IFFBs with an out rate of 99.1%.

Infield flies are, for all practical purposes, the same as a strikeout. They are basically an automatic out, runners do not advance on infield flies, and perhaps most importantly, we can state with a pretty high level of confidence that the relative abilities of the defenders have nothing to do with the outcome of the play. Sure, maybe you or I wouldn’t turn every IFFB into an out, but for players selected at the Major League level, there is no real differentiation in their ability to catch a pop fly.

So, based on those characteristics, an argument could actually be made that infield flies are essentially a fourth fielding independent outcome. No outcome is 100% fielding independent, of course, as catchers do have some ability to influence BB and K rates, and occasionally a HR is either robbed or knocked over the wall by an outfielder making a leaping grab on a long fly, but by and large, BB/K/HRs are mostly independent of the pitcher’s teammates, which is why they are the three variables in FIP. But, Dan’s comments got me thinking — if an infield fly has the same logistical outcome as a strikeout, should we just give a pitcher credit for IFFBs in the same way we give them credit for Ks?

In Tango’s bbFIP — which adjusts for all batted ball types — he does exactly that, adding Ks and IFFBs together and multiplying them by the same factor. It works because strikeouts and infield flies have almost identical run values. In 2012, a strikeout was worth -.265 runs, while an infield fly was worth -.268 runs. All the empirical data suggests that a pop up and a strikeout are essentially equally good for the pitcher, and both of them have little to do with the defensive support a pitcher gets from his teammates.

For predictive purposes, you definitely want to make a distinction between the two, as getting strikeouts are far more consistent from year to year than generating popups. Last year, Bill Petti ran the year-to-year correlations for basically any measure you can think of, and he got .82 for K% and .37 for IFFB%. There is no question that strikeout rate is more predictive of future strikeout rate than infield fly rate is of future infield fly rate.

However, just because FIP has been used to predict future ERA does not make FIP a predictive metric. It is a descriptive metric that happens to predict future events better than ERA, but as Glenn DuPaul wrote about extensively last year, if you wanted FIP to be predictive, you would use different weights for the formula than the ones that are currently in place. The weights for FIP come from the run values of the events being measured, not how well those events predict future events. In fact, home run rate has a y-t-y correlation of .42, much lower than either strikeout rate or walk rate, but it is included in FIP because it is a defensive independent outcome that the pitcher should be held responsible for.

Maybe we should consider that the IFFB is essentially not that different from a HR, in that it measures the results of specific batted balls that have a distinct run value and that aren’t influenced by the defenders behind any given pitcher. Just like we penalize pitchers for giving up home runs, logic would suggest that we should be giving them credit for infield flies.

At this point, all of the metrics designed to account for batted ball types have not just stopped with IFFB, but have also worked to apportion credit for GB%, OFFB%, and LD%. Knowing the results of those calculations can be useful, but those three batted ball types can all be described as having significant defensive contributions to the outcome, and thus, they don’t really belong in Fielding Independent Pitching. Infield flies, though, are a different animal from the other batted ball types, and I think you could make a pretty good case that they are essentially fielding independent. So, what happens if we just construct a very simple adjustment to FIP that treats IFFBs as Ks, and give them to the pitcher instead of the defenders?

It’s actually pretty easy to do, since the FIP formula is just basic math, and increasing the total number of Ks to include K+IFFB is just adding one additional term to the calculation. However, we do have to make an adjustment to the constant that allows FIP to equal league average ERA, since we’re increasing the number of outs that we’re crediting to the pitcher, which drives down FIP for each hurler. Last year, the constant for FIP was 3.095, but after including IFFBs in the K term, the new constant goes up to 3.196.

Just for fun, here’s a table of the 10 biggest gainers from FIP w/IFFB, or IFFIP, or whatever you want to call this slightly modified calculation.

Name | IFFIP | FIP | ERA |
---|---|---|---|

Bruce Chen | 4.33 | 4.73 | 5.07 |

Phil Hughes | 4.21 | 4.56 | 4.23 |

Matt Moore | 3.69 | 3.93 | 3.81 |

Barry Zito | 4.26 | 4.49 | 4.15 |

Tommy Milone | 3.72 | 3.93 | 3.74 |

Aaron Harang | 3.94 | 4.14 | 3.61 |

Chris Capuano | 3.76 | 3.95 | 3.72 |

Justin Verlander | 2.75 | 2.94 | 2.64 |

Jason Vargas | 4.51 | 4.69 | 3.85 |

Josh Beckett | 3.97 | 4.15 | 4.65 |

Average | 3.89 | 4.13 | 3.91 |

In eight of the 10 cases, the pitcher beat their FIP, and in each of those eight cases, IFFIP is closer to the pitcher’s overall ERA than FIP was. While the group outperformed their FIP by 20 points, their IFFIP was almost exactly dead on to their overall ERA. That’s actually just kind of lucky, as infield fly rate doesn’t explain all of the difference between a pitcher’s FIP and his ERA — there’s also BABIP on non-IFFBs and the sequencing of when different events occur — but including infield flies does help to explain part of variance between a pitcher’s FIP and his ERA, and for pitchers who happen to have generated a lot of infield flies, it can really add up.

For the 88 qualified starting pitchers in Major League Baseball last year, the overall correlation between FIP and ERA doesn’t change much by including IFFBs with Ks. The three true outcome formula returns a .77 correlation between the two, while including IFFBs moves the correlation all the way up to .78. Please don’t take this post as evidence that FIP is completely worthless and that it has been debunked. That said, there are pitchers who got a lot of infield flies, and infield flies are basically guaranteed outs, and giving pitchers credit for those guaranteed outs does make some logical sense when using FIP to describe what happened in the past.

And, of course, FIP is the metric we use in calculating pitcher WAR here on FanGraphs. We use FIP as a descriptive metric that tells us what happened in the three categories that we’re pretty sure that the defense had little impact on, and that we can definitively ascribe to the pitcher. Perhaps it makes sense for us to consider using this kind of slightly adjusted FIP that also gives pitchers credit for their IFFBs when calculating pitcher WAR. It’s certainly not a decision we’re going to make without consideration, but it’s probably worth asking the question.

We’ll talk about this internally, I’m sure, but I’m also curious to know what you guys think – would you prefer that we adjusted pitcher WAR to give pitchers full credit for the infield flies they generate?

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Yes. 100%

I’d prefer WAR to be based off of SIERA

Leaving aside the issue of the quality of batted ball classifications, SIERA regresses HR rate, so WAR would then measure how valuable a pitcher would have been had his HR rate been normalized towards an expected HR rate. It would move from being a measure of things that definitively did happen to a theoretical about a pitcher’s value if things had happened differently. That’s not really the point of WAR.

But at the same time, FIP already essentially normalizes BABIP and LOB% by assuming pitchers have no (or minimal) control over either. I don’t see how that’s much different than SIERA regressing HR rate.

does Cameron even read what he writes? One of the major points of contention with WAR for pitchers is that it does exactly that — it measures things that didn’t actually happen. As you pointed it, FIP normalizes some of its components to take “luck” out of the equation.

If we want to use WAR to “measure what actually happened” with pitchers a good place to start would be be to put luck (notice the lack of quotes this time) back into it.

I agree with this. For practical purposes they are at least very similar.

No, it does not. It doesn’t normalize them, it ignores them. There is a difference between what SIERA does, in giving weights to batted balls, and just ignoring them. FIP just looks at plate appearances that have to do with walks, strikeouts, and home runs. All of the other plate appearances are simply ignored, because teasing out how much credit for the resulting hits and outs should be given to the pitcher and how much should be given to the fielder is something that we can’t do very well.

This is the thing that people get wrong about pitcher WAR the most. FIP-based WAR doesn’t measure anything that did not actually happen. It is not comprehensive in its measurements of things that did happen, but it also does not claim to be comprehensive. It is an incomplete measure of pitcher performance, but it exclusively measures events that demonstrably occurred.

Using an RA based WAR and then making adjustments for assumed defensive contribution — as B-R does — makes that pitching WAR construct guilty of the thing that FIP-based WAR is most often incorrectly accused of doing. When you start making guesses about how much a pitcher or a fielder contributed to the results of their balls in play, then you are no longer measuring what actually happened.

It is completely fair to criticize FIP-based WAR for not measuring everything that happens, much like it is fair to criticize current catcher defense ratings for not measuring everything that catchers do that impact run prevention. It is incorrect to state that FIP measures things that did not happen or represents a hypothetical. FIP constrains itself to only measuring things that we know were almost entirely dependent on solely the pitcher/hitter match-up. Starting at RA9 and working your way backwards from there introduces the hypotheticals, not the other way around.

@ Ben:

FIP doesn’t really “ignore” batted balls in general; it ignores any difference between batted balls (eg. grounder vs. fly vs. line drive). For every batted ball a pitcher allows, it makes the pitchers FIP grow nearer to 3.1 (or whatever the baseline is these days).

I didn’t know that SIERA regressed HR rate.

I’d now prefer prefer batted ball FIP.

Something that recognizes that batted ball exist and pitchers have some control over them.

e.g.: http://www.fangraphs.com/community/index.php/introducing-bera-another-era-estimator-to-confuse-you-all/

It’s like bbFIP, but with the added dimension of Zone Contact%, which Dan Rosenheck just pointed out is an important predictor of BABIP.

I’d also like to proof read this before I posted it.

Holy crap

As madvillain pointed out, Fangraphs WAR certainly doesn’t measure what definitively did happen. If you were doing that, you would use R/9 as the metric in your WAR calculations. You choose to use a metric that strips out some things that are assumed to be out of the pitcher’s control, (in particular, but not limited to, sequencing) to get a better read on his actual performance. If you’re doing that, you have already moved to a metric that’s theoretical, not actual. At that point, it just becomes a matter of choice between which run estimator you feel best represents how a pitcher performed.

See above – this is simply incorrect.

Fairly cryptic answer. But, no, FIP is not measuring what actually happened, else it would stop at HR’s allowed, SO’s, and BB’s. We of course already have that information, so why any need for FIP? From there, it attempts to estimate how many runs a pitcher should have allowed based on those three components. So it is indeed hypothetical, or theoretical, or whatever. For better or worse, it’s an estimate of runs allowed, not a measure of them.

I don’t understand what you don’t get about this. FIP measures some of the things that happened, makes no estimate or claim to the things it doesn’t measure, and does not claim to touch anything it doesn’t measure. If you add regressed metrics you are now measuring something theoretical. FIP currently just ignores things that it would have to treat as theoretical, and then expressly tells you that it is only measuring the things that McCracken found to be stable year over year.

FIP does measure what happened that is under the pitcher’s control. When studies are published that show more things are under a pitcher’s control,such as just happened with infield flies, then those factors should be added to it.

Someday, we may see a much-improved FIP that looks very different from today’s.

I don’t think there is any dispute as to the fact that HR, BB and K are things that DID HAPPEN. It’s important to note, though, that converting those three things into FIP by a linear formula is just a product of multiple linear regression, using ERA (or maybe R/9) as the response variable.

So the estimated effects on ERA of a HR, BB, or K are regressed back to the league mean effects. The regression has attempted to explain as much of the variance in ERA as possible using only those three stats.

But K, BB, and HR also correlate linearly to BABIP and LOB%. By leaving BABIP and LOB% out of the model–among other things–the three stats left in the regression model are trying mightily to explain what BABIP and LOB% should be explaining, in addition to some of the things they can explain independently.

Any environment like baseball is a complex environment with lots of interaction and shared explanation between variables. Because K/9 and LOB%, for instance, are linearly correlated themselves, K/9 ends up explaining some of what LOB% could have explained.

I’m not sure exactly to whom I’m speaking. I don’t think I’m arguing with anyone. Only trying to point out what the linear formula is doing, and what is it not doing.

@Eminor3rd:

“

FIP measures some of the things that happened, makes no estimate or claim to the things it doesn’t measure, and does not claim to touch anything it doesn’t measure.”Um, what? “makes no estimate or claim to the things it doesn’t measure”? Then how come it is scaled to ERA, and is designed to estimate how many runs a pitcher should have given up? It’s not measuring actual runs allowed, it’s trying to provide an estimate of them. There can be no argument that FIP is estimating something that it isn’t directly measuring.

I prefer WAR to measure a player’s true talent, not what he contributed to a team through actual performance. Until we have a good predictive model of IFFBs, it’s not a true talent measure.

Also, since there’s likely an interaction between IFFBs and FBs, and therefore between IFFB and HR, I would want to see validation that the coefficients can still be used with accuracy.

I recognize that K% isn’t a perfect model of K true talent, but the correlation is so much stronger that I’m willing to live with that.

Would you be in favor of removing HRs allowed from WAR, then? They have about as much y-t-y noise as IFFB rate, and aren’t a great true talent measure either.

It sounds like your preference is for WAR to be based on something like a projection system, such as Steamer or ZIPS, rather than on past performance data. My sense is that you’re probably in the minority on that perspective.

Yes, but home runs are 4.33 times as weighty in the FIP equation as a strike out, and for that reason, one tolerates a lot more noise.

My second point is more important, in my opinion: you can’t just introduce an importantly interacting variable, use the same coefficients, and not test the results to see that they are better than the original model.

That’s not exactly how the weights work. The linear multipliers are scaled to account for how we’d expect a player to perform in those stats.

0.111 is average-ish for HR/IP, while 0.777 is average-ish for K/IP. That the K’s multiplier (slope) is about 1/6** that of the HR/IP means very little. In fact, the contribution to the model for an average-ish player would be 0.777*2/IP = 1.554/IP for K’s and 0.111*13/IP = 1.443 for HR’s. From that perspective, I guess you could say that K’s actually has the higher weight.

Though, in reality, a regression model is as much an analysis of variances as it is of means. So all the weights discussed mean nothing unless we understand the underlying variance of the variables so that we can scale the weights appropriately.

**If I’m not mistaken, the coefficient for HR is 13/IP and for K is 2/IP, for an exact ratio of 2/13.

@Matthias

The coefficients are just linear transformations of linear weights, dude. I have no idea what you mean, nor do you, I suspect, when you write “The linear multipliers are scaled to account for how we’d expect a player to perform in those stats.”

If you use IFFBs as a percentage of all batted balls, rather than as a percentage of all fly balls, the Y to Y correlation is much higher and well exceeds the HR rate correlation.

I feel the opposite. I’d much prefer WAR to be about actual results, whether that be for a year or for a career. If I want an estimate of his actual value, then I can look at the different pitching metrics, the parks and defense, and try to form an opinion about his actual talent. Give me the raw numbers first, then I can use that as a baseline if I want to look into it further.

This website is dripping with raw numbers and assorted metrics, feel free to look at those instead of WAR :)

And yet, no R/9 in any of the tables that I can see. Or maybe I just haven’t looked in the appropriate place, so maybe I’ve missed it.

ERA is there, but what do I care about ERA? If I want to look at a metric that’s a direct measure of the runs a pitcher has allowed (and I would think that would be obligatory), I want R/9, not something like ERA.

I strongly disagree with this. To me, measuring a player’s true talent is an entirely different question.

Someone needs to go through each of the above pitchers’ pitch history and collate exactly what pitch is inducing the pop-up in each specific at-bat. I suspect what we’ll see is that they all throw fastballs out of the same arm slot and window they throw their primary off-speed pitch from. It’s that small deception which is throwing some hitters off.

I most certainly would like to see it added. The data seemed fascinating.

I know IFFBs don’t have a high y-t-y corrolation, but have there been any pitchers who have had an above average IFFB% consistantly, and have they beat their FIP well? I wonder.

Zito has been the primary example for this, particularly with the A’s….

That does lead to the question (not beg :p), are these FIP number park adjusted?

Because it’s much easier to get an IFFB (I’m assuming foul outs count also) in stadiums like Network Colisseum than it is in small foul areas like Miller Park.

I think IFFB% is giving people the wrong idea about the repeatability of pitchers inducing infield popups because it’s a misleading stat. IFFB% on FanGraphs is the percentage of

fly ballsthat are popped up, not the percentage of allbatted balls, as I think most people would expect.Therefore, FB%*IFFB% is the percentage of all batted balls that are popped up. There’s about a 0.63 year-to-year correlation for this stat, which is quite a bit better than the 0.42 Bill Petti found for HR/9 IP.

This seems incredibly important to point out

This is what should go into WAR then.

To clarify further, IFFB (not IFFB%), which you can obtain via a custom leaderboard, does represent the actual number of infield fly balls, so you could use that too.

To clarify, the calculations above are based on the number of IFFBs a pitcher induced, not on any kind of percentage. So, this is what would go into WAR if we decided to make this adjustment.

Which is why I’ve always hated IFFB% as a stat; IF/PA or IF/BIP would be far more useful, and should be more prevalent.

I think you make a good case for it. If FIP is descriptive, so is WAR, so the low year-to-year correlation doesn’t really enter into the conversation.

Wouldn’t adding IFFB give some slight advantage to fly ball pitchers in IFFIP compared to sinker ballers or other pitchers who induce weak contact on the ground?

The latter is more defense-dependent, but including them would require only an extension of the same leaps to defensive-interchangeability as IFFIP.

Pitchers who generate IFFBs tend to be flyball pitchers, yes. But, at the same token, fly ball pitchers tend to give up more HRs, so you could make a case that they’re being penalized for their batted ball profile in FIP already. If higher infield fly rates are a biproduct of pitching up in the zone, just as higher HR rates are, then FIP is penalizing pitchers for their approach by docking them for HRs without giving them credit for the good contact that pitching up in the zone can also lead to.

I think it’s probably worth noting that fly ball pitchers tend to outperform their FIP more often than groundball pitchers. So, perhaps this advantage is correcting a previous bias against FB pitchers. This is all still speculative, but I think there might be something to the idea that this adjustment balances the scales more than it tilts them towards FB pitchers.

Great points. I buy into the re-calibrating the scale back towards effective FB pitchers, but will point out that some number (many? few? most?) of FB pitchers are already benefiting in FIP because of the inclusion of strikeouts that may offset the HR issue.

I would be curious if extreme groundballers also outperform their FIP because there is no groundball equivalent of IFFB.

GIDP? I realize that it is extremely defense dependent (and baserunner), but it is the value added corollary for ground balls. Maybe you could distill it into a rate of opportunity stat? or use the batted ball buckets and timer that go into DRS to find balls that reasonably could be expected to be DPs?

Maybe Trevor Bauer is onto something? ;)

I kind of like this idea, but what I’d be interested in finding is how much IFFB% is hitter-dependent, rather than fielding-independent. I mean, it’s in much the same realm as homers, I suppose, but isn’t there a place to discover how much the IFFB is related to the talent/ability of the hitter versus the talent/ability of the pitcher.

What you seem to be implying is that all of the really high IFFB% hitters are accounting for most or all of the IFFB out success of some pitchers. I can just say intuitively that the correlation would not be remotely as high as Steve reports. For there to even be a small correlation those hitters would have to popping up against those pitchers at an astronomical rate.

I agree with this. FIP assumes that pitchers all face a reasonably similar cross-section of hitters, and can thus be compared. In order for this assumption to fail, you would have to see very selective pinch-hitting approaches across the big leagues, which doesn’t happen often. An argument could be made that there are lesser hitters at the plate for pitchers late in the game because starters have been removed through substitution, but in order for any meaningful difference to appear there, the numbers would need to be staggeringly different, due to the sample sizes.

You could also say the same about strikeouts and walks. Both the hitter and the pitcher influence all those outcomes. Which is the essence of FIP, that it’s measuring the interaction between hitter and pitcher, taking only those measures which seem to be truly within the realm of those two.

Uhm … the new (

and interesting) part of Rosenheck’s analysis was all about the significance of z-contact%. Writing that off as not “really a new finding” not only sounds rather petulant, but entirely misses the point. Given that z-contact% allowed would appear to be an actual skill (although I don’t yet know about yearly variance), that’s a pretty momentous addition to the overall concept of BABIP.Yeah, Dave should have said something like “Incorporating z-contact% into FIP and WAR introduces many complications, so for now I’ll focus on how IFFB% might be used to improve both statistics.”

Ahem…

http://www.fangraphs.com/community/index.php/introducing-bera-another-era-estimator-to-confuse-you-all/

I found a 0.738 year-to-year correlation for Z-Contact%, by the way (bottom of http://www.fangraphs.com/community/index.php/proejcting-babip-using-batted-ball-data/ )

Once upon a time, I proposed that one should combine K/PA% plus a IFFB/PA% into a single stat called Hopeless Outcome Percentage (HO%). Anyone got a better a name for such a thing?

Auto-Out Percentage?

FIP is meant to be a defensive independent describer of a pitcher’s performance. Do the fielders have anything to do with an out being recorded on a infield pop up? Obviously the answer is yes they do have to catch, but is you assume that every infield fly ball is going to be caught by a major league infielder (which isn’t a bad assumption) then the fielder really has nothing to do with it. A.K.A. fielding independent.

I say put IFFBs in WAR.

Unless A-Rod yell’s “Ha” in their ear, that is.

Remember that 200 ft infield fly in the playoffs last year? That one needs to be split into thirds: pitcher, SS/OF, and the 5th umpire.

If adding a IFFB adjustment to FIP, would a normalized IFFB rate be added to xFIP? If FIP was altered I would probably want some consistency with xFIP so that both accounted for IFFB in some measure.

The point is that IFFB% has enough y-t-y correlation that it doesn’t have to be normalized, we can assume it’s within a pitchers skill set.

Actually, Cameron points out that its y-t-y correlation is similar to HR rate. So you would think in xFIP you would normalize it just like you do HR rate.

Although, Steve Staude claimed that IFFB actually has a .63 y-t-y correlation not the .37 that Cameron put in the article. If that is the case, maybe FanGraphs would decide that is high enough to not normalize in xFIP.

Striking out means you didn’t hit the ball. (almost every time, some are foul tips of course) Infield flies, some way up into the sky mean you did hit the ball, just not exactly centered. I get the math, but I would value a player that can hit the ball over a player that can’t every time.

Stat heads commence the barrage.

I think what you are saying is that it is likely that high IFFB% hitters are better hitters statistically than high SO% hitters. If you look at that leaderboard, I believe that is correct. So there is really no need for anybody to dispute. I do appreciate the rather aggressive baiting technique, though.

This should already be integrated in that hitter’s greater ball-in-play rate. (Assuming there is one, but I don’t see why you’d give extra value to a player who hit more popups while still having the same rate of other balls-in-play.)

But the run values are essentially the same, so from a pitcher’s perspective, you’d be fine with either outcome.

the low yty correlation of IFFB% would lead me to say no, though that’s just the same problem that HR/FB% has…

it would at least be interesting to see a win value exist for IFFIP, but how much different is it than the other non-FIP win values (FDP- and BIP- wins) that we already have?

There’s actually a 0.63 year-to-year correlation for popups per batted ball, defined as FB%*IFFB%, which is pretty high. IFFB% isn’t what most people probably think it is.

for the baseline WAR, that may be getting a little esoteric for my tastes. i say leave FIP-wins as the default WAR on the dashboard, and put IFFIP-wins on the value tab

Well, the IFFB count is pretty straightforward, but IFFB% is not. Anyway, popup pitchers are always going to be undervalued by an FIP-based WAR.

This is explaining part of FDP (in particular, the BIP-wins), not separate from that. Essentially, this calculation would be crediting part of the BIP-wins to a pitcher’s FIP instead of his FDP. Which, I think, follows logically from the definition of fielding dependent or fielding independent wins.

No idea what this article is about.

/Votto

Question: You bring up the out percentage on IFFbs…what’s the out percentage of strikeouts? Obviously higher than the 99.1% there is for IFFBs I suppose, but there’s still a bit of wiggle room there too, yeah?

Not that it changes anything, I’m just interested.

Why would it necessarily be higher? Two different things, I’m not sure why it would logically follow that SO’s would have a higher out% than IFFB’s.

Nothing inherently makes it higher I guess, my statement was more based on my personal observations which could obviously be wrong. If it doesn’t end up being much higher, then the case for IFFBs == Ks ends up being stronger, though my guess would be the number of Ks that end up in the runner on base is closer to a magnitude higher than IFFBs (1 in 1000 as opposed to 1 in 100, maybe).

Hence why I wouldn’t mind seeing the numbers but have no idea where to start.

If it was included I would like to see IFFB% included in the dashboard. For proper context would have to see all components of WAR visible in one place when looking at a pitcher

I would definitely be interested in the “new” FIP, but I don’t think I’d like to replace it all together. I’d like to be able to look at both and see how they changed differed, and analyze those a little more.

I would like to see IFFB% included, and the most recent Community article (Greenlee, 3/4) seems to make a strong case for rSB and rPM to be included as well. Incorporating all three would stay true to DIPS theory without overlooking as much of a pitcher’s skill-set as FIP.

I don’t understand FG’s baby-steps in pitcher-WAR either. Why is a pitcher’s defense and steal-prevention irrelevant again? Real, significant, repeatable, support-independent skills should be considered. Period.

Steal prevention is support-independent? So catcher’s have nothing to do with it?

Whoops, posted too far down — rSB sets out to separate a pitcher’s contributions in steal prevention from the catcher’s.

Thank you — I hadn’t actually taken a look at it, so I’m glad you pointed that out.

I dont know where to find the data, but I would be willing to bet that more batters reached base on strikeouts than on infield flies.

Well, there are far more strikeouts than infield fly balls, so the percentage reached on infield flies would have to be far higher than that for strikeouts for the two to be equal.

“for players selected at the Major League level, there is no real differentiation in their ability to catch a pop fly.” I’ve heard a radio announcer call an infield fly (while in the air) as not catchable by the 1B. After quite awhile in the air it landed about 20 feet back from the bag but the inexperienced 1B didn’t account for the different late break of a LHB and wasn’t near where it fell.

Nevertheless, with just 1% falling in for non-outs, even the worst cannot be that bad.

Luis Castillo

Can’t believe it took this long to find a Castillo mention.

Is there a way to incorporate so that the pitcher doesn’t get full credit for the IFFB? Considering the correlation is lower, could they get a % of the credit. I agree with the idea of it being included but also agree with some of the comments about how the hitter is still making contact. It could be a large part pitcher skill that the batter is unable to square it up but ‘full credit’, equaling it to a K, seems flawed.

I’m sounding like a broken record here, but the 0.37 year-to-year correlation for IFFB% is extremely misleading, because IFFB%=IFFB/FB.

If we’re talking about IFFB/Batted Balls (similar to how LD%=LD/Batted Balls, or FB%=FB/Batted Balls), the YTY correlation is 0.63. Compare that to 0.69 for BB%, 0.79 for K%, and only 0.42 for HR/9.

I’m not sure it’s legitimate to compare rate stats with different denominators. In any event, the stat we’re proposing to add is IFFB/IP, so that’s the relevant correlation to consider.

Alright, IFFB/IP has a 0.60 YTY correlation amongst qualified pitchers. If you want to stay consistent with the denominators of BB% and K%, then IFFB/TBF has a 0.61 (I prefer this to an IP basis). HR/TBF is only 0.40.

There seems to be a lot of confusion about why the variables for FIP were chosen. They were not chosen because they have high year-to-year correlation. The weights were not chosen to maximize year-to-year correlation. When talking about a metric that measures past performance, you don’t actually care about y-t-y correlation very much.

The question is simply which outcomes we believe are the responsibility of the pitcher himself and which ones had some interaction with his defenders. BB/K/HR have little to no interaction with the fielders, while most balls in play have a very large degree of interaction. IFFBs are a subset of BIPs that have very little interaction, so on that basis alone, they seem to be more like BB/K/HR than GB, LD, or OFFBs.

Makes sense to me. Does this mean that the next step in calculating a FIP-based WAR would be analyzing which balls in play would have been turned into outs by all major league players? For example, pitcher X induces 100 soft ground balls right to the shortstop over the course of the season. Assume that batted balls of a similar velocity and trajectory are turned into outs 98% of the time. Any out made on such a batted ball appears to be independent of who is playing defense.

Of course, I haven’t seen this sort of public data publicly available. When it is, then FG may even consider to adding to the cartegories of defense independent outcomes (weak ground balls, lazy flies, etc). In the meantime, I think this is a good step.

There’s still noise there, because location is such a crucial variable to whether or not a ball can be fielded, and DIPS tells us that pitchers cannot seem to control the location of a batted ball.

I would think that there might be some value in measure velocity of the ball off the bat though, or something that indicates how hard the ball is hit.

The allure of rSB is that it separates a pitcher’s contributions in steal prevention from the catcher’s (or at least that is its objective).

“runners do not advance on infield flies”

Bullshit.

For the most part, they don’t.

They can advance on a K + WP, which is probably more common than a dropped IFFB.

Additionally, it is safe to attempt a steal on a strike out, and the batter occasionally reaches base on a dropped third strike.

“Never” might be hyperbole, but it isn’t bullshit.

My problem is this.

We use FIP and other factors to throw out luck and look at pure pitcher skill.

But unless we could identify IFFB% as being a skill along the lines of K%, we shouldn’t include it.

The discussion seems to be centered around IFFBs and FIP, but the question is asking about IFFBs and WAR. To me, this is the real question: What correlates better to team wins, an IFFIP-based WAR or the normal FIP-based fWAR?* The basic logic of including IFFBs in FIP appears sound, so if IFFIP-based WAR correlates better, I would vote for including IFFBs. After all, the goal of FIP and WAR should be to provide a metric that most accurately describes how many games a pitcher won or lost for their team.

*Hopefully someone with a better stats background can chime in. Would calculating the correlation between different FIP-based WARs and team wins rely on the accuracy of the hitting, baserunning, and fielding metrics? I.e., would the correlation between various FIP-based WARs and team wins be skewed if the other metrics were either more or less accurate?

I will simply agree with what many others said. FIP based WAR is completely useless to me. It gets stuck in the in between. It tries to adjust for LOB% and BABIP which are largely out of a pitchers control and doesn’t adjust for HR/FB. I’d rather you base WAR on simple ERA which is basically what you do with hitters. We don’t pushing a hitters WAR because his BABIP was high. WAR is a measurement of results, it isn’t a measurement of skill.

While we are on the subject I don’t agree with park adjusting WAR for hitters either since all of the park adjustments are completely flawed. Petco doesn’t have the same effect on Venable as a LH pull hitter as it does on Quentin as a RH pull hitter or on Cabrera who is a spray slap hitter.

The WAR you have created doesn’t show what happened or the talent of the player, it is some odd amalgamation that doesn’t seem very useful.

” BB/K/HR have little to no interaction with the fielders”

That is really the root of the problem. HR depend on the hitter, they depend on the park, they depend on the wind, they do sometimes depend on the fielders as many get robbed each year taking them away, they depend on tons of things out of the pitchers control and things we can’t accurately measure. You are putting all kinds of bias into WAR when you try to correct for things like this.

Park factors include all types of hits and account for handedness.

Sure, let’s include it. I’m not sure how much of IFFB% is due to park and how much to the pitcher, but park has an effect on HR too. If it gets you closer to what the pitcher’s actual performance was in terms of factors they control then it should be in there.

In the vast majority of instances, an infield popup is a non-advancing out no matter which major league infield is behind the pitcher. That’s more or less the definition of “fielding independent”. If the goal of FIP is to count fielding independent events generated by this pitcher then IFFB should be included.

“Sure, maybe you or I wouldn’t turn every IFFB into an out, but for players selected at the Major League level, there is no real differentiation in their ability to catch a pop fly.”

Are you sure about this, Dave Cameron? Have you allowed for the well-documented Mark Reynolds factor?

http://www.baseballprospectus.com/article.php?articleid=19409

Before you decide, you should check the year-to-year correlation of K+IFFB%. While you have the correlations for K% and for IFFB%, you don’t necessarily, know what the combined correlation would be if K’s and IFFB’s are themselves correlated with each other.

Is it just me or is all of this conversation about Pitcher WAR going to entirely unnecessary once we have reliable batted ball data that can give a run value to every ball contact?

ctrl+f “braves”

not a single result

Great stuff, Dave.

In September of 2014, unfortunately FIP has not been changed to reflect IFFB’s. What a shame.