Fixing xFIP, Pt. 1: Line Drives and Pop-Ups
One might argue that xFIP is slightly misaligned. One might make that argument in blog form, on the website FanGraphs, today, here, now.
One only might argue that xFIP is slightly misaligned because xFIP is commonly understood to serve a purpose distinct from FIP. FIP, aka Fielding Independent Pitching, is calculated as a function of strikeouts, walks, and home runs — that is, outcomes over which fielders bear no influence. The equation that underpins FIP is derived from a linear regression equation intended to resemble ERA, for ease of interpretation. Because it is based exclusively on outcomes, its purpose is more descriptive than predictive. In other word, it finds greater purpose describing what should have happened but not necessarily what will happen.
xFIP, on the other hand, seeks to achieve the inverse. A large swath of evidence exists to suggest home run-to-fly ball rate (HR/FB) for pitchers is incredibly noisy season to season. Sure, certain pitchers might anecdotally buck the norm — apparently, Michael Pineda was born to be a cafeteria lunch lady, serving up meatballs and taters — but, by and large, HR/FB is a fool’s errand to predict. Accordingly, xFIP replaced home runs with expected home runs, by way of multiplying the number of fly balls allowed by a pitcher by the league-average HR/FB, thereby normalizing home run damage, making it, in theory, a better descriptor (and perhaps a better predictor) of pitcher performance over time.
And therein lies the rub, although, if you missed it, you mustn’t be blamed.
HR/FB, the backbone of xFIP, is inherently flawed because:
- Home runs are never hit on infield fly balls (aka pop-ups), yet pop-ups are included in HR/FB (because pop-ups are included in all fly balls*); and
- Home runs occasionally are hit on line drives, yet line drives are not included in HR/FB.
*Fly ball percentage (FB%) includes both infield and outfield fly balls.
This has bothered me a long time, this seemingly minor but potentially substantial ideological discrepancy. If we seek to normalize home run behavior for pitchers, we should endeavor to do so in a way that is most theoretically appropriate. I’m not here to reinvent the wheel — there are significantly more complex ways to normalize home run behavior — but I, at least, can pick the low-hanging fruit and make subtle adjustments to existing metrics.
I sampled all qualified pitcher-seasons from 2010 through 2018 (n = 709) and calculated unique ratios of home runs to outfield fly balls and line drives. This abbreviates to HR/(oFB+LD), which doesn’t quite roll off the tongue, but it’ll do in a pinch.
Although HR/(oFB+LD) generally behaves proportionally to HR/FB…
| Season | HR/FB | HR/(oFB+LD) |
|---|---|---|
| 2010 | 9.4% | 6.8% |
| 2011 | 9.7% | 6.7% |
| 2012 | 11.3% | 7.5% |
| 2013 | 10.5% | 6.9% |
| 2014 | 9.5% | 6.3% |
| 2015 | 11.4% | 7.5% |
| 2016 | 12.8% | 8.5% |
| 2017 | 13.7% | 9.3% |
| 2018 | 12.7% | 8.5% |
… HR/(oFB+LD) cannot be directly substituted for HR/FB in FIP or else it breaks the equation. If I simply plug in HR/(oFB+LD), all pitchers would suddenly “underperform” their ERA because their xFIPs would improve by several tenths of a run without merit.
To account for this difference, I ran a fresh regression, setting up the equation exactly as specified by xFIP, by virtue of FIP. (I also included year fixed effects, which is a component of FIP and xFIP, too, appearing in the form of the “year constant” term.) However, in lieu of normalizing home runs by all fly balls, I normalized home runs by, yes, outfield fly balls and line drives — the only batted ball events that can produce home runs (inside-the-park home runs notwithstanding).
The regression produced an adjusted r2 of 0.55 — weaker than FIP (r2 = 0.62) but a good deal stronger than the original xFIP (r2 = 0.42). What this means is, from a purely descriptive standpoint, xFIP that relies on outfield fly balls and line drives is a better description of “skill” (or “deserved” outcomes) than xFIP that relies on outfield fly balls and also pop-ups but not line drives.
From a predictive standpoint (by measure of correlation to next-year ERA), the original xFIP outperforms the new xFIP, but not significantly, resulting in r2 values of 0.20 and 0.18, respectively. Even SIERA hardly outperforms the new xFIP (r2 = 0.20), and its descriptive (same-year) value is notably weaker (r2 = 0.36).
| ERA | y+1 ERA | |
|---|---|---|
| FIP | 0.62 | 0.17 |
| xFIP | 0.42 | 0.20 |
| SIERA | 0.45 | 0.20 |
| New xFIP | 0.55 | 0.18 |
FIP, xFIP, and SIERA all boast ideological differences, yet none prevails as a superior predictive option. That “new xFIP” lands in the middle of an indistinguishable pack predictively while also prevailing above xFIP and SIERA descriptively lends merit to the original argument — the argument that xFIP might be theoretically misaligned, such that it artificially restricts its descriptive power.
You could calculate New xFIP manually using the equation above, if you’d like. (The constant term for 2019, as of now, is something like 0.834. I say “as of now” because it’s a moving target — as league-wide ERA changes, so, too, does the constant term.)
If not, you could make mental adjustments to xFIP in its current state according to a couple of intuitive rules of thumb. How does a pitcher’s line drive rate (LD%) compare to the league average? If it’s higher, then xFIP might be overrating his performance; if lower, then underrating. Same with infield fly ball percentage (IFFB%): if it’s higher, xFIP might be underrating his performance, and vice versa. It’s inexact, but, to be fair, all of this (gesturing broadly to sabermetrics) is inexact.
* * *
Here’s the “New xFIP” leaderboard, as of Sunday, May 12.
| Name | Team | IP | ERA | Old xFIP | New xFIP | diff |
|---|---|---|---|---|---|---|
| Blake Snell | Rays | 43.0 | 3.56 | 2.49 | 2.62 | 0.13 |
| Tyler Glasnow | Rays | 48.3 | 1.86 | 2.78 | 2.71 | -0.07 |
| Hyun-Jin Ryu | Dodgers | 52.3 | 1.72 | 2.50 | 2.77 | 0.27 |
| Stephen Strasburg | Nationals | 57.0 | 3.63 | 2.71 | 2.89 | 0.18 |
| Gerrit Cole | Astros | 55.7 | 3.88 | 2.33 | 2.94 | 0.61 |
| Luis Castillo | Reds | 56.3 | 1.76 | 3.08 | 2.99 | -0.09 |
| Cole Hamels | Cubs | 49.7 | 3.08 | 3.57 | 3.30 | -0.27 |
| Caleb Smith | Marlins | 42.7 | 2.11 | 2.98 | 3.32 | 0.34 |
| German Marquez | Rockies | 57.7 | 3.43 | 3.16 | 3.32 | 0.16 |
| Max Scherzer | Nationals | 59.3 | 3.64 | 2.75 | 3.37 | 0.62 |
| Noah Syndergaard | Mets | 49.0 | 5.14 | 3.41 | 3.37 | -0.04 |
| Carlos Carrasco | Indians | 40.3 | 4.91 | 3.14 | 3.50 | 0.36 |
| Jacob deGrom | Mets | 47.0 | 3.26 | 2.93 | 3.51 | 0.58 |
| Zack Greinke | Diamondbacks | 57.0 | 3.16 | 3.19 | 3.53 | 0.34 |
| Max Fried | Braves | 44.3 | 3.25 | 3.32 | 3.59 | 0.27 |
| Matthew Boyd | Tigers | 54.3 | 3.15 | 3.38 | 3.61 | 0.23 |
| Luke Weaver | Diamondbacks | 45.3 | 2.98 | 3.53 | 3.66 | 0.13 |
| Zack Wheeler | Mets | 49.7 | 4.35 | 3.34 | 3.66 | 0.32 |
| Marcus Stroman | Blue Jays | 52.0 | 3.12 | 3.81 | 3.71 | -0.10 |
| Tyler Mahle | Reds | 45.3 | 3.97 | 3.02 | 3.71 | 0.69 |
| Chris Sale | Red Sox | 44.0 | 4.50 | 3.46 | 3.72 | 0.26 |
| Charlie Morton | Rays | 44.3 | 2.64 | 3.53 | 3.73 | 0.20 |
| Jose Quintana | Cubs | 46.3 | 3.50 | 3.39 | 3.75 | 0.36 |
| Justin Verlander | Astros | 57.3 | 2.51 | 3.56 | 3.77 | 0.21 |
| Jon Lester | Cubs | 38.7 | 1.16 | 3.40 | 3.82 | 0.42 |
| Frankie Montas | Athletics | 45.3 | 2.78 | 3.63 | 3.83 | 0.20 |
| Mike Minor | Rangers | 53.7 | 2.68 | 4.17 | 3.86 | -0.31 |
| Pablo Lopez | Marlins | 41.0 | 5.93 | 3.82 | 3.87 | 0.05 |
| Yusei Kikuchi | Mariners | 54.3 | 3.64 | 4.00 | 3.92 | -0.08 |
| Patrick Corbin | Nationals | 50.7 | 3.20 | 3.85 | 3.94 | 0.09 |
| Zach Eflin | Phillies | 51.0 | 2.47 | 4.41 | 3.94 | -0.47 |
| Shane Bieber | Indians | 49.7 | 3.81 | 3.99 | 3.99 | 0.00 |
| Jack Flaherty | Cardinals | 41.7 | 4.32 | 3.56 | 4.01 | 0.45 |
| Masahiro Tanaka | Yankees | 52.3 | 3.44 | 4.00 | 4.05 | 0.05 |
| Joey Lucchesi | Padres | 41.3 | 4.57 | 3.92 | 4.06 | 0.14 |
| Madison Bumgarner | Giants | 55.7 | 4.04 | 3.47 | 4.08 | 0.61 |
| Domingo German | Yankees | 43.3 | 2.70 | 3.94 | 4.10 | 0.16 |
| Kyle Hendricks | Cubs | 42.3 | 3.19 | 3.81 | 4.11 | 0.30 |
| Jon Gray | Rockies | 48.7 | 4.25 | 3.72 | 4.13 | 0.41 |
| Jose Berrios | Twins | 59.0 | 3.05 | 4.13 | 4.13 | 0.00 |
| Robbie Ray | Diamondbacks | 48.7 | 3.14 | 3.77 | 4.19 | 0.42 |
| Walker Buehler | Dodgers | 43.3 | 4.15 | 3.96 | 4.22 | 0.26 |
| Adam Wainwright | Cardinals | 43.3 | 4.15 | 4.17 | 4.24 | 0.07 |
| Miles Mikolas | Cardinals | 54.0 | 3.83 | 4.33 | 4.30 | -0.03 |
| Yonny Chirinos | Rays | 42.3 | 3.61 | 4.42 | 4.35 | -0.07 |
| Jordan Lyles | Pirates | 38.7 | 2.09 | 4.48 | 4.36 | -0.12 |
| Eduardo Rodriguez | Red Sox | 43.7 | 4.53 | 3.76 | 4.38 | 0.62 |
| Homer Bailey | Royals | 41.0 | 4.83 | 4.01 | 4.38 | 0.37 |
| Trevor Bauer | Indians | 59.7 | 3.02 | 3.84 | 4.39 | 0.55 |
| Kevin Gausman | Braves | 42.0 | 4.50 | 4.05 | 4.40 | 0.35 |
| Trevor Williams | Pirates | 50.3 | 3.40 | 4.31 | 4.45 | 0.14 |
| Jose Urena | Marlins | 46.7 | 4.82 | 4.52 | 4.48 | -0.04 |
| Wade Miley | Astros | 45.3 | 3.18 | 4.43 | 4.48 | 0.05 |
| Anthony DeSclafani | Reds | 41.0 | 4.17 | 4.26 | 4.50 | 0.24 |
| Andrew Cashner | Orioles | 42.3 | 4.25 | 4.84 | 4.53 | -0.31 |
| Martin Perez | Twins | 46.3 | 3.11 | 4.28 | 4.53 | 0.25 |
| Brad Peacock | Astros | 42.7 | 4.01 | 4.19 | 4.54 | 0.35 |
| Jake Arrieta | Phillies | 50.0 | 3.78 | 4.44 | 4.55 | 0.11 |
| Jake Odorizzi | Twins | 42.7 | 2.32 | 4.49 | 4.58 | 0.09 |
| Julio Teheran | Braves | 50.7 | 4.26 | 4.50 | 4.68 | 0.18 |
| Zach Davies | Brewers | 46.7 | 1.54 | 4.72 | 4.69 | -0.03 |
| Joe Musgrove | Pirates | 40.7 | 4.20 | 4.43 | 4.72 | 0.29 |
| Aaron Nola | Phillies | 46.3 | 4.86 | 3.88 | 4.73 | 0.85 |
| Mike Fiers | Athletics | 51.0 | 5.12 | 5.18 | 4.74 | -0.44 |
| Mike Leake | Mariners | 47.3 | 4.37 | 4.69 | 4.79 | 0.10 |
| Spencer Turnbull | Tigers | 44.7 | 2.42 | 4.46 | 4.80 | 0.34 |
| Marco Gonzales | Mariners | 56.7 | 3.18 | 4.89 | 4.86 | -0.03 |
| Nick Margevicius | Padres | 41.3 | 4.14 | 4.87 | 4.90 | 0.03 |
| J.A. Happ | Yankees | 43.3 | 4.36 | 4.91 | 4.91 | 0.00 |
| Collin McHugh | Astros | 42.7 | 6.33 | 4.21 | 4.94 | 0.73 |
| Rick Porcello | Red Sox | 43.7 | 5.15 | 4.97 | 4.98 | 0.01 |
| Kenta Maeda | Dodgers | 44.7 | 4.03 | 4.66 | 5.01 | 0.35 |
| Merrill Kelly | Diamondbacks | 46.0 | 4.70 | 4.66 | 5.02 | 0.36 |
| Kyle Freeland | Rockies | 44.7 | 5.84 | 4.92 | 5.03 | 0.11 |
| Aaron Sanchez | Blue Jays | 48.0 | 3.75 | 4.72 | 5.06 | 0.34 |
| Jakob Junis | Royals | 48.3 | 5.77 | 4.63 | 5.08 | 0.45 |
| Lance Lynn | Rangers | 47.7 | 5.48 | 4.53 | 5.14 | 0.61 |
| Ivan Nova | White Sox | 44.3 | 6.29 | 4.44 | 5.15 | 0.71 |
| Jeff Samardzija | Giants | 41.0 | 3.51 | 5.08 | 5.17 | 0.09 |
| Jhoulys Chacin | Brewers | 45.3 | 4.57 | 5.43 | 5.23 | -0.20 |
| Brett Anderson | Athletics | 43.0 | 4.19 | 5.24 | 5.26 | 0.02 |
| Michael Pineda | Twins | 40.0 | 5.85 | 4.71 | 5.27 | 0.56 |
| Dylan Bundy | Orioles | 40.7 | 5.31 | 5.13 | 5.32 | 0.19 |
| Reynaldo Lopez | White Sox | 50.0 | 5.58 | 5.46 | 5.38 | -0.08 |
| Brad Keller | Royals | 52.3 | 4.47 | 5.09 | 5.43 | 0.34 |
| Dereck Rodriguez | Giants | 41.0 | 5.05 | 5.06 | 5.45 | 0.39 |
| Jorge Lopez | Royals | 43.0 | 6.07 | 4.59 | 5.45 | 0.86 |
| Sandy Alcantara | Marlins | 44.0 | 5.11 | 5.54 | 5.45 | -0.09 |
| Trevor Richards | Marlins | 42.3 | 4.46 | 5.57 | 5.52 | -0.05 |
| Tanner Roark | Reds | 41.3 | 3.27 | 4.81 | 5.58 | 0.77 |
| Anibal Sanchez | Nationals | 41.0 | 5.27 | 5.37 | 6.21 | 0.84 |
* * *
[Edit (5/21/19 8:32 pm ET)] It should be noted all calculations below relied on Statcast data rather than FanGraphs data. I had a moment of panic when I realized FanGraphs and Statcast data do not perfectly align in terms of how batted ball events (fly balls, etc.) are strung/coded. Fortunately, I am also able cross-validate the results below using FanGraphs data. The change to xFIP I recommended below still bears a substantial improvement in xFIP’s correlation with same-year ERA; its adjusted r2 improving from 0.44 to 0.53 — not exactly the same values shown below, but darn close. That’s all. Thanks![/Edit]
Carson got the job with Toronto despite this annoying writing style, not because of it.
That you even thought to compare me to Carson is considerably high praise I don’t deserve — thank you!
Surprised to see Berrios and Bauer with such high numbers.
Love the concept and refresh, but I was bummed to see the predictive value of new xFIP as inferior to the original xFIP 🙁 — even if only marginally so.
I guess that improvement in descriptiveness just doesn’t hit the spot in the same way an improvement in predictiveness would for me …
Think you misread. It’s MORE predictive than original xFIP.
“The regression produced an adjusted r2 of 0.44 — a good deal weaker than FIP (r2 = 0.55) but a good deal stronger than the original xFIP (r2 = 0.36).”
In other words, its not as good as the FIP results calculated based on actual HR/FB rate to their ACTUAL ERA. But new xFIP is a better method of substituting a constant for leaguewide homerun rate than the old xFIP.
Those were the descriptive r^2 values relating it to current ERA. For predictions, based on relating it to the next year ERA it was was slightly worse than current xFIP. However, given that there have been some pretty significant swings in year to year HR/FB rate I wonder if that is introducing too much noise for any league wide HR/FB adjustment to be useful year to year, even if the model is trying to take that into account
So, right, “new xFIP” is very slightly less predictive than “old xFIP” of next-year ERA. It’s so close, though, that I would be reluctant to call one considerably superior to the other. Also (and this isn’t mentioned explicitly in the post): “new xFIP” is very slightly stickier year-to-year than “old xFIP” —something that originally was supposed to be make old xFIP superior to FIP, its ability to “predict” itself year to year.
So, I would argue that the very slight (potentially inconsequential) sacrifice to predictiveness is worth the significant enhancement to descriptiveness and slight (although also potentially inconsequential) enhancement to year-to-year stickiness.
Hope that all makes sense, but let me know if you have further questions of clarification!
I like how I ironically called out someone for misreading, then I misread by completely glancing over the predictiveness table and associated paragraph. Apologies, Aaron
Very cool. I did this on the side in Excel, but could you update the table to include a last column of Old xFIP – New xFIP. Then people can quickly tell who gets affected most by the change. I’d even default sort that way. VERY INTERESTING for a couple of Phillies pitchers…
Zach Eflin (0.47 improvement with new xFIP)
Aaron Nola (0.85 Worse with new xFIP and about in line with his current ERA)
Nola was one of the first to pop out to me, although Eflin is very interesting, too! I’ll add that “diff” column now.
Pitchers like Trevor Williams, Brad Keller and Spencer Turnbull have shown an “ability” (or is it luck?) to have a lower rate of home runs per fly ball from the minors up to the majors.
Do we know “for sure” if this is not a skill ? I have not been able to find any analysis on this, although I’m curious to know where I can find that information.
It seems some pitchers are more susceptible to home runs; why couldn’t others limit them?
I would be reluctant to read too much into above-average HR/FB allowed at the minor league level. I think the translation from MiLB to MLB is not one-to-one — presumably (but not always) the best MiLB pitchers are called up to become anywhere from bad to good MLB pitchers. It’s probably safe to assume the best MiLB pitchers were best at limiting home runs, a skill that may or may not hold up at the big-league level.
So my short(er) answer is, I’m not sure if it’s a skill, and I’m not sure what literature exists re: specifically minor-league HR/FB, but I think, as implied by the main projection systems, if it is a skill it’s not worth buying too heavily into as something that can drive major-league success. Hope that makes sense, but let me know if not.
Forget the minor-league aspect of this. Just looking at their major league numbers (is it a large-enough sample size?) they’ve consistently been below the average percentage of 10-11% HR/FB. They are not world-beaters but maybe this is some of the reason they have reasonable success in the majors.
Oh! I misread. I wouldn’t doubt there’s an underlying skill there. I think there’s a lot more we can learn just about HR/FB in general from a high level, let alone all the ways different pitch types behave from a contact quality standpoint. It’s something I’ve been meaning to explore with the advent of Statcast.
I believe there has been found to be some skill in HR/FB%, specifically, GB pitchers tend to be worse it it, although I think there might be other factors as well.
There is definitely a park factor at play as well. The numbers are a decade old, but the Tigers, Pirates and Royals all play in neutral to homer run suppressing parks so that would explain quite a bit for two of your trio.
https://blogs.fangraphs.com/infield-fly-balls-and-xfip/
https://tht.fangraphs.com/tht-live/hr-fb-park-factors/
For his career Trevor Williams has given up the same number of homers on the road as home. The same with Keller. Turnbull has given up 3 HR on the road and one at home.
My reply with numbers got eaten up unfortunately. Regardless, we’re talking about HR/FB% right? 2 of the 3 have lower HR/FB% at home though. One of them by a lot. However, we’re dealing with pitchers with small MLB samples so I wouldn’t look into the numbers too much either way. Maybe look at players with longer track records? I’m sure this has been studied before and you can find an article or two that measures pitchers’ ability to control for HR/FB% beyond just their FB% allowed. If it’s a skill, someone’s found it.
Hmm, why not just use groundball rate? I get the thing about pop-ups, except that flyball pitchers get more pop-ups in a very linear way so those are accounted for in the groundball/home run conversion math. And if we really want it to be predictive, don’t we want to include park factors and defense?
1) Ground ball rate would imply fly balls + line drives as its inverse, where fly balls still includes infield fly balls. So you could do “GB+IFFB”. But, HR/(IFFB+GB) doesn’t make intuitive sense — while it would (probably) bear the same results in xFIP, it becomes logically inconsistent to say “the percentage of home runs hit on ground balls and pop-ups.” So, ultimately I think it becomes a matter of semantics.
2) re: predictiveness, I wasn’t trying to set out to build a new ERA estimator — just to improve one that is widely cited and has tremendous structural inertia. The primary goal of this post is to illuminate the drawbacks of relying on xFIP, which, while good, could still be better.
Is there any consideration for outfield fly balls that are caught in foul territory? It’s an addition of “Home runs are never hit on infield fly balls or foul balls. “
I don’t think those are coded into FB — only FB that occur in fair territory. It would probably be pretty difficult to capture (although I bet Statcast might have tracking data for some foul balls, but I would be surprised if they had it for all of them).
This is generally something I feel is lacking in a lot of baseball data. Foul balls are valid points to plot physically and geospatially, especially hard hit ones.
I don’t disagree there!
Thanks Alex! I noticed Chris Paddack, the Prince Who Was Promised, was missing? He had 40.2 IP as of the 12th, so should have made any innings limit?
Hmm, interesting. The qualified pitcher list came from FG — not sure why he didn’t make it! Sorry about that!
If my mental arithmetic is correct, his new xFIP is 3.50, a -0.15ish improvement on his current 3.65 xFIP.
Woodruff also missing, had over 40 IP as of the 12th.
Have him pegged for about 4.23. Exorbitant LD%. I imagine that will regress toward league average after a while, but it also likely means those LDs turn into FBs rather than GBs. So it might be worth heeding the red flag it’s throwing up there.
You probably should have checked with your overlord before posting this because it sounded familiar.
“So getting back to xFIP, does it really matter whether or not you exclude popups? The answer is, not really. You’re going to get almost the same results because HR/OFFB on average exhibits more or less the same issue as HR/FB. In fact, the correlation between using OFFB vs total FBs in xFIP is .996. The two, in practice, are virtually identical.” – David Appelman
https://blogs.fangraphs.com/infield-fly-balls-and-xfip/
I can confirm Appelman’s finding independently; xFIP benefits almost zero from excluding pop-ups by themselves. What this adds is line drive rate, which more adequately captures launch angle allowed.
True. Thanks!
Wondering what happens if you rerun the model using barrels/(OFFB+LD). I know statcast metrics have proven to be much more valuable as descriptors than as predictors, but barrels are still an improvement on predicting HRs with HRs, and likely enough to beat SIERA and xFIP.
My next steps (in tangentially related research) are to replicate FIP, xFIP, and SIERA using Statcast metrics. Because of how differently each entity strings batted balls (Statcast is much more liberal with its classification of pop-up, because it doesn’t have to strictly land within the infield), there’s opportunity to compare how these metrics would shape up using different data sources (aka basically what you’ve suggested — I like where your head is at!). I have a long to-do list, but this is already at the top of it. 🙂
It would seem to me that while LD do sometimes become HR, they do so at a significantly different rate than FB do (as evidenced by the lower rates in the first table). So why lump them together? A pitcher who allows 40% LD and 10% oFB is not likely going to give up the same number of HR as one who is 10%/40%, are they?
Did you try, or have you thought about, running a regression with HR/oFB and HR/LD separately? Or do we only have total HR numbers and it’s too hard to break them up that way?
Is this the same question from Twitter or Reddit? I think Twitter? Or does it just happen to be a nearly identical question asked by a different person?
At any rate, you’re correct, HR/oFB and HR/LD would be extremely different. There are also no pitchers who achieve either of those splits you mentioned (too extreme), although I do understand your sentiment, of course. I think the big thing is LDs can be kind of noisy, so while a pitcher with a high LD% might not sustain such a high rate for long, as it regresses those LDs will more likely turn into FBs than GBs. So the inclusion of LD (my best interpretation of the relationship) is helping capture more information about launch angles allowed by pitchers. Oftentimes line drives are classified by stringers as line drives simply because they are hard hits that result in home runs. In other words, it’s kind of an eye-of-the-beholder thing, one that would suggest that a low FB% may not be sustainable with a high LD%.
That felt like brain vomit, so let me know if that didn’t make sense.
Ultimately, splitting up the two rates (/LD and /oFB) would deviate from the intent of xFIP, the integrity of which I wanted to maintain. Not that splitting them apart wouldn’t be justifiable — just that I was trying to improve xFIP specifically in its existing framework.
Forgive my ignorance if this exists already, but is there a way to measure xHR based on statcast data? With average flyball distance and FB% for example. Or hard% with launch angle. Might this be a way to have a “how many HR he should have allowed” component in xFIP?
Not sure about the pitcher side of things. I know there is for hitters (although nothing formal from Statcast, just work that folks like me have done.)
This is something I intend to explore soon for pitchers!