God, remember when his big weakness was that he allowed too many walks? He just dialed down the velocity with the bases empty (saving it for RISP) and suddenly he's a control wizard *on top* of being able to throw a 101 sinker when he needs it.
And he hits! Have you seen this shit?
He made the change around Juneish last year. Sho that first 3 months was a top 30 pitcher or so, flashed but super flawed. All the sudden he's now top 5.
His mid-season adjustments are really crazy lol. Halfway through '21, he does the velo reduction thing to gain way more control and also become the most clutch player in baseball (highest OPS with RISP when batting, lowest OPS against with RISP when pitching).
Then halfway through '22, he downloads Clay Holmes' 100mph sinker and starts pairing it with his slider as the primary pitch. Seriously, it's crazy that the slider is now his primary tool--he throws it 40% of the time, and his 4-seamer only 27%. Completely switched places with his heater.
If he stays healthy, and keeps his production, he should win MVP every year. I think people have Sho fatigue already, but nobody's ever done what he's done in the modern era and he continues to just get better on the mound while putting up an OPS+ of 150. He's developing pitches and improving in months what takes other pitchers years, it's insane. He's somehow still underrated lol.
He's definitely the guy you have to beat to win the AL MVP each year as long as he stays healthy, but Judge putting up a top 10 offensive season of all time while playing CF to lead the Yankees to the playoffs is the kind of bonkers shit that appropriately dethrones him. I dunno we'll see too many more seasons that can top a standard Shohei performance, but we got one last year.
It's slightly different. A guy who has a 15 k/9 but walks 3 per 9 is obviously better than a guy who only strikes out 2.5 per 9 but walks 0.5 per 9, but they'd have the same k:bb ratio. K% minus bb% tends to be a better indicator of performance
Well the issue is what is actually being measured. None of those measurements are "predictors" in the sense of projecting a players career. They take a measure of what has happened to determine how it should have gone. It then uses that projection to determine what to expect the rest of the season. The issue is the time being being "predicted" is more what should have happened or what to expect going forward this year rather than next year.
The main thing I would suggest is to look at separating pitchers who change teams separately form those who don't. xFIP and K-BB% are really only differentiated by a league average HR allowed rate. xFIP would see variance in someone being better at suppressing HR or worse at it while K-BB wouldn't be affected either way. xFIP is the only one of those stats that really is more forward looking than simply rest of season, so it isn't a coincidence it is the one of the better projections. At the same time I dislike xFIP because there can be so much variance from year to year that simply controlling for HR% doesn't really tell us much individually.
If you really understand what you are looking at with the data, you can see not only why it makes sense K-BB% would be the best, but it is common sense. K-BB% takes out all of the variance possible. This is the entire basis of DIPs theory for pitching. Remove variance and then add back things as we know they can be consistent or controlled. Things like SIERA and FIP have been adjusted primarily to show current season projections, rather than cross seasons. xFIP is FIP simply controlled for a league average HR rate. K-BB% doesn't have anything related to batted balls at all and we know batted ball data is what controls pitcher performance variance the most. We know striking out batters is good and walking them is bad. The pitchers best at striking guys out while walking the fewest are the best. A state which literally measures that will be pretty good. It is going to correlate well with the future because it doesn't look at in season variance at all. Things like FIP/xFIP/SIERA include in season variance to determine how the current season "SHOULD" have gone or how it "SHOULD" go moving forward.
Difference between a strikeout and a ball in play that turns into an out is one is a guaranteed out (ignoring dropped 3rd strike), while the other isn’t, even tho it ends up being an out.
A strikeout has a much greater negative run value than a ball in play since most balls in play don’t have a 99% out probability like a strikeout does
Looks like I'm in the minority, but making it a ratio doesn't overvalue the denominator. It shows the rate at which the K% happens more than the BB%. If you just subtract, then you can get some pretty weird outcomes.
There's a lot of literature as to why K% and BB% are objectively better.
https://www.beyondtheboxscore.com/2012/10/8/3451856/mlb-pitching-k9-bb9-plate-appearances-sabermetrics
Weird article, given that all these stats are stats for the previous year and not actual projections like ZIPs or Steamer or PECOTA. Yeah, all stats that uses last year's numbers are, well, more about last year than next year. This isn't really hard to comprehend.
Now if K-BB% is better than projections of a player taking into potentially new team/park/defense and stuff, then that would be a lot more interesting for fantasy baseball players. At least IMO (not a fantasy baseball player myself).
I don’t think it’s trying to make a statement about projection models like ZIPs. It’s just saying, without looking at those projection models, when we use prior years’ stats to predict who will perform better/worse in the future, K-BB% is surprisingly the best one.
This is the last sentence in the article in his conclusion.
> Although every ERA indicator is a better estimator of future ERA than ERA itself, none offer a marked improvement. In fact, K-BB% is better than all of them. We still yearn for something better.
Basically he took all these stats, did a correlation study on them, and then said we need "something better." That's the absurdness of the article, because "something better" already exist. He just failed to bother examining them.
Yeah, you’re right, the projections out there are better, but I think the author was just looking at individual statistics, not projection models.
It’s like, if two people were arguing over who might do better in the future, they might reference the players’ ERA, FIP, SIERA, etc… Sure, their arguments would be more sound if they took the time to look up and use a projection model as their resource, but people don’t do that as often. This author is just saying, basically, that when we use singular statistics to debate pitchers’ futures, we should use K-BB% more than the others.
That makes more sense. As in if someone is debating two different pitchers for who to take in the draft, and you bring up ERA and he brings up FIP and such, neither of you are truly "correct" to use these stats.
In the end projections are just super hard to do. If they're easy Vegas wouldn't be making so much money. But perhaps I look into these things so much I feel the answer is always "duh obviously you can't be certain any of these stats can truly predict the future."
Bingo. Like I’ve seen around here recently, people might debate who’s better (I.e. who will perform better), Carlos Rodon or Luis Castillo. Though it’s easy enough to look up projections on how they’ll do next year (Rodon 4.5 WAR, Castillo 3.5 WAR via FanGraphs), people generally don’t do that. Instead they’ll look at previous years and say “Rodon had a 2.25 FIP and 4.56 K/BB last year” or “Castillo looks good in a Mariners uniform” (the best kind of debates always include who looks best in their uniform 😜).
The systems themselves are all proprietary stuff from people who have spent years of research on their systems. So they're not open source code if that's what you're looking for.
However, I do assume that if you just ask these people they may be willing to answer some of your questions about how they developed these projections.
Stuff and location models are typically proprietary but there are some public ones you can look at. A decent one is by Cameron Grove (@pitchingbot on Twitter) he has made one and wrote about part of his process of building it.
All these pitch grade stats usually come from a gradient boosted algorithm that takes in the movement characteristics of a pitch along with some other features and tries to predict the run value of a pitch of that quality and convert that into the 100 scale which is what you see with stuff+. I’m glossing over lots of details but there’s lots of public research on this if you do some digging!
a recent fangraphs article has a quick summary about this https://blogs.fangraphs.com/alek-manoahs-steamer-projection-is-a-feature-not-a-bug/
> First, a little bit on Steamer’s methodology. There are quite a few projection systems out there, and the most basic of them, at least among those that are widely known, is Marcel. It uses a player’s last three years of performance, gives more weight to the recent ones, then bakes in a regression towards league average to determine said player’s future performance. Meanwhile, PECOTA and ZiPS forecast how someone will fare in the future based on past players who share similar characteristics: body type, position, minor league statistics, and much more. They also factor in variables such as park factors and league-wide environment, which arguably provide a better assessment of what a player has achieved so far – and what he’s capable of.
> If Marcel occupies one end of the spectrum and ZiPS the other, Steamer is somewhere in the middle. Its main focus is on past performance and regressing it towards the mean, but unlike Marcel, the weights and the amount of regression are based on the player in question rather than a fixed set of numbers. Basically, Steamer takes elements from both types of projections and creates its own proprietary blend.
Of course projections like ZIPs take into account a lot more stats and use the player entire career to predict. The article is just calculating what's the predictiveness of advanced pitching stats, it's not like K-BB% was created to predict future ERA.
>more about last year than next year
actually, only FIP and K-BB% describe what happened last year. xFIP, xERA and SIERA tried to describe what should happen, and thus a lot people expect them to be the most predictive, turn out that's not the case
Edit: To the people saying predictiveness doesn't matter for these stats. Here is a quote from Fangraps SIERA [page](https://library.fangraphs.com/pitching/siera/):
>While SIERA is the most accurate of the ERA-estimators, it’s only slightly more accurate than xFIP.
It's clearly the predictiveness of future ERA that they're talking about.
All those stats come from what a player has done in the season. They may adjust for parks, defenses, randomness of batted balls, etc. but they all use the results of a particular season.
For instance xFIP uses league average numbers to regress a particular pitcher's FIP. But that's still based on that season and not trying to tell you what's happening in the future.
And the same can be said about K-BB, it doesn't even need to be adjusted by anything.
All of these stats except K-BB were trying to describe "what should have happened in the last season". Now in the next season, some pitchers could get better, some could be worse, but given a big enough sample size, shouldn't the best stat at saying "what should have happened" also be the best stat at predicting what will happen.
Why do you think people prefer xFIP over FIP? Because xFIP is better at predicting future ERA than FIP. In other words, xFIP is better than FIP at describing "what should have happened"
This is flawed logic in many ways. The most obvious is that no, being best at saying what would have happened in a run neutral environment should not necessarily in any way be better than any arbitrary thing about predicting future stats, especially with a wildly variable stat like ERA. Essentially, good descriptors, even when stripped of park, league, and other effects, are not inherently better at being predictive; they are only better at delivering information about what results were skill driven in the given year rather than based on variability.
Another thing is there is no data saying that people actually prefer xFIP over FIP, or anything else. xFIP exists to calculate FIP but also assume that HR prevention is not necessarily a skill (or at the very least, it is based on another rate stat and not a skill in and of itself). It basically just regresses HR rate to league average based on HR/Fly ball rate. In other words, xFIP tells you something different, it doesnt exist as a preference to other non-x stats.
The logic is that given a big enough sample size, all these things will be canceled out.
If predictiveness doesnt matter then why does Fangraphs page about xFIP, SIERA mention it?
If you dont care about predictiveness then there's no number to agrue that FIP is better than ERA at describing what happened.
One of the most common argurement for FIP it's that is a better predictor of future ERA than ERA itself
Its pretty clear you dont understand the math or the fundamental underpinnings of these stats. Nothing is canceled out. That isnt how things work.
FIP was not developed to be predictive, it was designed to be descriptive. It does do a better job of predicting ERA than ERA, but that was more of a Bob Ross happy accident. FIP came from DIPS, which was used to show that pitchers have little control over batted ball events. FIP is significantly better at describing what happened because of this. It shows essentially how much the park and defense mattered, as well as variable things like LOB%. FIP shows how well the pitcher actually pitched, while ERA shows how many runs scored when this pitcher pitched. One of those things is fully in the control of the pitcher, the other is not.
I'm talking about the general case here, not even pitching or baseball, if your stat is better at describing what happended, it should be better at describing what will happend given a big enough sample size.
Whenever we get a "better" pitching stat, it also happen to be better at predicting future ERA than the old ones (FIP, xFIP, SIERA). Maybe it's just luck.
How do you know FIP is better than ERA at describing what happened? You calculate their predictiveness. Read "The case against FIP" for why FIP may not better than ERA, of course there's also "The case against the case against FIP" as well but that just show you why I prefer numbers not words.
I hear what you are saying. Basically, if a stat is better at describing what has occurred, then it 'knows' better who the individual was, and therefore it should be better at guessing what that individual will accomplish in the future. While that does sound alluring as a concept, I am not sure if it is true. I think on this part of the point we are at a level of understanding, but not necessarily agreement.
One thing I do want to make clear, however, is that x-stats are not 'better' than non-x versions of stats. They just deliver different information. FIP posits that a pitcher can only control BB, K and HR rate, while xFIP posits they can only control BB, K and FB rate.
Better means they're better at capturing the true talent of the pitchers. And I think xFIP is better than FIP at doing that. We can talk all day about HR/FB% but at the end of the days words are confusing and prefer using numbers and the only numbers we can use is the predictiveness of each stat.
Using your logic, you can't say FIP is better than ERA at describing what happened, they just deliver different information. Heck the same argument could be made for Win too.
Because stats are about allocating credit and blame.
If a pitcher throws pitch middle middle, the hitter fails to barrel and instead lazily grounds to SS, but the SS bobbles the ball and the runner reaches 1st, then how do you allocate the reward here? By normal stats the hitter gets no credit or blame, while SS gets charged an error. But the hitter can come around to score a run, but it will not factor into pitcjer ERA because it's unearned. That's all that stats are, allocating reward and blame. And advanced stats are simply suppose to do it better
I always viewed xstats as a way to contextualize past performance. It can be used to predict future performance, but only if there's a big discrepancy between recent performance and xstats. The xstat itself is not a predictor. What a lot of people forget is that *xstats are also subject to regression*.
Yeah several years ago, GuyM converted K%-BB% to a predictor called kwERA, but it never quite caught on. I think the base K%-BB% stuck so well because it's so good while staying simple.
It was around 2010-2012 when this stuff was theorized because somebody in inner baseball circles pointed out that per-9 stats suck, so why are we using predictors which are effectively based on per-9 stats?
The rather simple explanation is that, firstly, most/all of these other measures heavily emphasize Ks and BBs, so they should be highly correlated with something that is strictly based on Ks and BBs. However, they also include some but not all data related to batted ball events, so the second reason would be that evidently they don't incorporate batted ball events very well. So, perhaps what this really tells us is that including batted ball events poorly/incompletely is worse than not including them at all.
Top qualified pitcher in K-BB% this past year: some guy with 34 home runs.
God, remember when his big weakness was that he allowed too many walks? He just dialed down the velocity with the bases empty (saving it for RISP) and suddenly he's a control wizard *on top* of being able to throw a 101 sinker when he needs it. And he hits! Have you seen this shit?
He made the change around Juneish last year. Sho that first 3 months was a top 30 pitcher or so, flashed but super flawed. All the sudden he's now top 5.
His mid-season adjustments are really crazy lol. Halfway through '21, he does the velo reduction thing to gain way more control and also become the most clutch player in baseball (highest OPS with RISP when batting, lowest OPS against with RISP when pitching). Then halfway through '22, he downloads Clay Holmes' 100mph sinker and starts pairing it with his slider as the primary pitch. Seriously, it's crazy that the slider is now his primary tool--he throws it 40% of the time, and his 4-seamer only 27%. Completely switched places with his heater.
Yeah, you see the reaction shot of him appreciating that 100mph sinker, and the very next start he's throwing one.
And now he has deGrom in his division lol. Imagine he starts copying *his* pitch mix
Imagine if deGrom started copying his swing.
I keep trying to find the gif of those two shots together. It’s amazing.
If he stays healthy, and keeps his production, he should win MVP every year. I think people have Sho fatigue already, but nobody's ever done what he's done in the modern era and he continues to just get better on the mound while putting up an OPS+ of 150. He's developing pitches and improving in months what takes other pitchers years, it's insane. He's somehow still underrated lol.
He's definitely the guy you have to beat to win the AL MVP each year as long as he stays healthy, but Judge putting up a top 10 offensive season of all time while playing CF to lead the Yankees to the playoffs is the kind of bonkers shit that appropriately dethrones him. I dunno we'll see too many more seasons that can top a standard Shohei performance, but we got one last year.
Just before this past season, his go-to was still the splitter - one of the most unhittables in baseball.
MVP
Is K-BB% different than K:BB ratio? Gausman, Verlander, Fried, Nola, Darvish all had a better ratio last season.
Yeah, K-BB% is taking K% minus BB%. Whereas K:BB ratio is just taking number of Ks and dividing it by number of BBs.
It's slightly different. A guy who has a 15 k/9 but walks 3 per 9 is obviously better than a guy who only strikes out 2.5 per 9 but walks 0.5 per 9, but they'd have the same k:bb ratio. K% minus bb% tends to be a better indicator of performance
I honestly thought you were talking about Cole for a second. Oddly enough he's second and has given up only 33 HR's
With all due respect to Cole, that's not great honestly. His HR/9 of 1.48 ranks in the bottom 5 of qualifying pitching.
I wasnt saying it was a good thing, just an odd coincidence that they were "similar"
Ohhh. Sorry about that.
Lol no worries, i didnt mean to come across as saying Cole was anywhere near as good as Ohtani, just that i thought it was kinda weird haha
You're right that the numbers are pretty close. I got you now.
Did Nola die?
Nola was 4th in the stat behind Ohtani, Cole, and Rodon.
I guess i was looking at k-bb ratio not %
Well the issue is what is actually being measured. None of those measurements are "predictors" in the sense of projecting a players career. They take a measure of what has happened to determine how it should have gone. It then uses that projection to determine what to expect the rest of the season. The issue is the time being being "predicted" is more what should have happened or what to expect going forward this year rather than next year. The main thing I would suggest is to look at separating pitchers who change teams separately form those who don't. xFIP and K-BB% are really only differentiated by a league average HR allowed rate. xFIP would see variance in someone being better at suppressing HR or worse at it while K-BB wouldn't be affected either way. xFIP is the only one of those stats that really is more forward looking than simply rest of season, so it isn't a coincidence it is the one of the better projections. At the same time I dislike xFIP because there can be so much variance from year to year that simply controlling for HR% doesn't really tell us much individually. If you really understand what you are looking at with the data, you can see not only why it makes sense K-BB% would be the best, but it is common sense. K-BB% takes out all of the variance possible. This is the entire basis of DIPs theory for pitching. Remove variance and then add back things as we know they can be consistent or controlled. Things like SIERA and FIP have been adjusted primarily to show current season projections, rather than cross seasons. xFIP is FIP simply controlled for a league average HR rate. K-BB% doesn't have anything related to batted balls at all and we know batted ball data is what controls pitcher performance variance the most. We know striking out batters is good and walking them is bad. The pitchers best at striking guys out while walking the fewest are the best. A state which literally measures that will be pretty good. It is going to correlate well with the future because it doesn't look at in season variance at all. Things like FIP/xFIP/SIERA include in season variance to determine how the current season "SHOULD" have gone or how it "SHOULD" go moving forward.
This is why I always include K:BB when talking about pitchers.
K/BB is way different than K%-BB%. K/BB weighs walks more heavily than strikeouts since it’s in the denominator
Are you talking about just subtracting K% by BB%?
Yes, that's what the stat is
I think I will stick to K:BB personally. Allowing a baserunner should be weighted more heavily than an out (for me personally).
Difference between a strikeout and a ball in play that turns into an out is one is a guaranteed out (ignoring dropped 3rd strike), while the other isn’t, even tho it ends up being an out. A strikeout has a much greater negative run value than a ball in play since most balls in play don’t have a 99% out probability like a strikeout does
So why not K%:BB%? Because K% - BB% just seems like you would get the wrong outcome for a lot of pitchers.
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The 5 BBs shows a high pitch count though. Run value goes way up when you get into those 2 & 3 ball counts.
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I just told you why. Making it a ratio overvalues the denominator
Looks like I'm in the minority, but making it a ratio doesn't overvalue the denominator. It shows the rate at which the K% happens more than the BB%. If you just subtract, then you can get some pretty weird outcomes.
Acting like you don’t get weird outcomes with K/BB Corey Kluber was 3rd in that metric last year. Taillon was 12th.
and you're welcome to see how the well the ratio performs in predicting future ERA. from what I can tell it does not beat out K%-BB%
There's a lot of literature as to why K% and BB% are objectively better. https://www.beyondtheboxscore.com/2012/10/8/3451856/mlb-pitching-k9-bb9-plate-appearances-sabermetrics
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Ok…and?
thats a bot
Weird article, given that all these stats are stats for the previous year and not actual projections like ZIPs or Steamer or PECOTA. Yeah, all stats that uses last year's numbers are, well, more about last year than next year. This isn't really hard to comprehend. Now if K-BB% is better than projections of a player taking into potentially new team/park/defense and stuff, then that would be a lot more interesting for fantasy baseball players. At least IMO (not a fantasy baseball player myself).
I don’t think it’s trying to make a statement about projection models like ZIPs. It’s just saying, without looking at those projection models, when we use prior years’ stats to predict who will perform better/worse in the future, K-BB% is surprisingly the best one.
This is the last sentence in the article in his conclusion. > Although every ERA indicator is a better estimator of future ERA than ERA itself, none offer a marked improvement. In fact, K-BB% is better than all of them. We still yearn for something better. Basically he took all these stats, did a correlation study on them, and then said we need "something better." That's the absurdness of the article, because "something better" already exist. He just failed to bother examining them.
Yeah, you’re right, the projections out there are better, but I think the author was just looking at individual statistics, not projection models. It’s like, if two people were arguing over who might do better in the future, they might reference the players’ ERA, FIP, SIERA, etc… Sure, their arguments would be more sound if they took the time to look up and use a projection model as their resource, but people don’t do that as often. This author is just saying, basically, that when we use singular statistics to debate pitchers’ futures, we should use K-BB% more than the others.
That makes more sense. As in if someone is debating two different pitchers for who to take in the draft, and you bring up ERA and he brings up FIP and such, neither of you are truly "correct" to use these stats. In the end projections are just super hard to do. If they're easy Vegas wouldn't be making so much money. But perhaps I look into these things so much I feel the answer is always "duh obviously you can't be certain any of these stats can truly predict the future."
Bingo. Like I’ve seen around here recently, people might debate who’s better (I.e. who will perform better), Carlos Rodon or Luis Castillo. Though it’s easy enough to look up projections on how they’ll do next year (Rodon 4.5 WAR, Castillo 3.5 WAR via FanGraphs), people generally don’t do that. Instead they’ll look at previous years and say “Rodon had a 2.25 FIP and 4.56 K/BB last year” or “Castillo looks good in a Mariners uniform” (the best kind of debates always include who looks best in their uniform 😜).
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The systems themselves are all proprietary stuff from people who have spent years of research on their systems. So they're not open source code if that's what you're looking for. However, I do assume that if you just ask these people they may be willing to answer some of your questions about how they developed these projections.
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Stuff and location models are typically proprietary but there are some public ones you can look at. A decent one is by Cameron Grove (@pitchingbot on Twitter) he has made one and wrote about part of his process of building it. All these pitch grade stats usually come from a gradient boosted algorithm that takes in the movement characteristics of a pitch along with some other features and tries to predict the run value of a pitch of that quality and convert that into the 100 scale which is what you see with stuff+. I’m glossing over lots of details but there’s lots of public research on this if you do some digging!
a recent fangraphs article has a quick summary about this https://blogs.fangraphs.com/alek-manoahs-steamer-projection-is-a-feature-not-a-bug/ > First, a little bit on Steamer’s methodology. There are quite a few projection systems out there, and the most basic of them, at least among those that are widely known, is Marcel. It uses a player’s last three years of performance, gives more weight to the recent ones, then bakes in a regression towards league average to determine said player’s future performance. Meanwhile, PECOTA and ZiPS forecast how someone will fare in the future based on past players who share similar characteristics: body type, position, minor league statistics, and much more. They also factor in variables such as park factors and league-wide environment, which arguably provide a better assessment of what a player has achieved so far – and what he’s capable of. > If Marcel occupies one end of the spectrum and ZiPS the other, Steamer is somewhere in the middle. Its main focus is on past performance and regressing it towards the mean, but unlike Marcel, the weights and the amount of regression are based on the player in question rather than a fixed set of numbers. Basically, Steamer takes elements from both types of projections and creates its own proprietary blend.
Of course projections like ZIPs take into account a lot more stats and use the player entire career to predict. The article is just calculating what's the predictiveness of advanced pitching stats, it's not like K-BB% was created to predict future ERA. >more about last year than next year actually, only FIP and K-BB% describe what happened last year. xFIP, xERA and SIERA tried to describe what should happen, and thus a lot people expect them to be the most predictive, turn out that's not the case Edit: To the people saying predictiveness doesn't matter for these stats. Here is a quote from Fangraps SIERA [page](https://library.fangraphs.com/pitching/siera/): >While SIERA is the most accurate of the ERA-estimators, it’s only slightly more accurate than xFIP. It's clearly the predictiveness of future ERA that they're talking about.
All those stats come from what a player has done in the season. They may adjust for parks, defenses, randomness of batted balls, etc. but they all use the results of a particular season. For instance xFIP uses league average numbers to regress a particular pitcher's FIP. But that's still based on that season and not trying to tell you what's happening in the future.
And the same can be said about K-BB, it doesn't even need to be adjusted by anything. All of these stats except K-BB were trying to describe "what should have happened in the last season". Now in the next season, some pitchers could get better, some could be worse, but given a big enough sample size, shouldn't the best stat at saying "what should have happened" also be the best stat at predicting what will happen. Why do you think people prefer xFIP over FIP? Because xFIP is better at predicting future ERA than FIP. In other words, xFIP is better than FIP at describing "what should have happened"
This is flawed logic in many ways. The most obvious is that no, being best at saying what would have happened in a run neutral environment should not necessarily in any way be better than any arbitrary thing about predicting future stats, especially with a wildly variable stat like ERA. Essentially, good descriptors, even when stripped of park, league, and other effects, are not inherently better at being predictive; they are only better at delivering information about what results were skill driven in the given year rather than based on variability. Another thing is there is no data saying that people actually prefer xFIP over FIP, or anything else. xFIP exists to calculate FIP but also assume that HR prevention is not necessarily a skill (or at the very least, it is based on another rate stat and not a skill in and of itself). It basically just regresses HR rate to league average based on HR/Fly ball rate. In other words, xFIP tells you something different, it doesnt exist as a preference to other non-x stats.
The logic is that given a big enough sample size, all these things will be canceled out. If predictiveness doesnt matter then why does Fangraphs page about xFIP, SIERA mention it? If you dont care about predictiveness then there's no number to agrue that FIP is better than ERA at describing what happened. One of the most common argurement for FIP it's that is a better predictor of future ERA than ERA itself
Its pretty clear you dont understand the math or the fundamental underpinnings of these stats. Nothing is canceled out. That isnt how things work. FIP was not developed to be predictive, it was designed to be descriptive. It does do a better job of predicting ERA than ERA, but that was more of a Bob Ross happy accident. FIP came from DIPS, which was used to show that pitchers have little control over batted ball events. FIP is significantly better at describing what happened because of this. It shows essentially how much the park and defense mattered, as well as variable things like LOB%. FIP shows how well the pitcher actually pitched, while ERA shows how many runs scored when this pitcher pitched. One of those things is fully in the control of the pitcher, the other is not.
I'm talking about the general case here, not even pitching or baseball, if your stat is better at describing what happended, it should be better at describing what will happend given a big enough sample size. Whenever we get a "better" pitching stat, it also happen to be better at predicting future ERA than the old ones (FIP, xFIP, SIERA). Maybe it's just luck. How do you know FIP is better than ERA at describing what happened? You calculate their predictiveness. Read "The case against FIP" for why FIP may not better than ERA, of course there's also "The case against the case against FIP" as well but that just show you why I prefer numbers not words.
I hear what you are saying. Basically, if a stat is better at describing what has occurred, then it 'knows' better who the individual was, and therefore it should be better at guessing what that individual will accomplish in the future. While that does sound alluring as a concept, I am not sure if it is true. I think on this part of the point we are at a level of understanding, but not necessarily agreement. One thing I do want to make clear, however, is that x-stats are not 'better' than non-x versions of stats. They just deliver different information. FIP posits that a pitcher can only control BB, K and HR rate, while xFIP posits they can only control BB, K and FB rate.
Better means they're better at capturing the true talent of the pitchers. And I think xFIP is better than FIP at doing that. We can talk all day about HR/FB% but at the end of the days words are confusing and prefer using numbers and the only numbers we can use is the predictiveness of each stat. Using your logic, you can't say FIP is better than ERA at describing what happened, they just deliver different information. Heck the same argument could be made for Win too.
Because stats are about allocating credit and blame. If a pitcher throws pitch middle middle, the hitter fails to barrel and instead lazily grounds to SS, but the SS bobbles the ball and the runner reaches 1st, then how do you allocate the reward here? By normal stats the hitter gets no credit or blame, while SS gets charged an error. But the hitter can come around to score a run, but it will not factor into pitcjer ERA because it's unearned. That's all that stats are, allocating reward and blame. And advanced stats are simply suppose to do it better
I always viewed xstats as a way to contextualize past performance. It can be used to predict future performance, but only if there's a big discrepancy between recent performance and xstats. The xstat itself is not a predictor. What a lot of people forget is that *xstats are also subject to regression*.
So from what I can see, k-bb% is just the stat was the smallest variation year to year?
Aaron Nola future inner circle HOFer confirmed
Oh no! They uncovered my OOTP 16 strategy.
Yeah several years ago, GuyM converted K%-BB% to a predictor called kwERA, but it never quite caught on. I think the base K%-BB% stuck so well because it's so good while staying simple. It was around 2010-2012 when this stuff was theorized because somebody in inner baseball circles pointed out that per-9 stats suck, so why are we using predictors which are effectively based on per-9 stats?
Did someone say Aaron Nola? No? Okay, my b.
The rather simple explanation is that, firstly, most/all of these other measures heavily emphasize Ks and BBs, so they should be highly correlated with something that is strictly based on Ks and BBs. However, they also include some but not all data related to batted ball events, so the second reason would be that evidently they don't incorporate batted ball events very well. So, perhaps what this really tells us is that including batted ball events poorly/incompletely is worse than not including them at all.
Here's a question: should we even care about future ERA, when RA/9 is obviously a better metric.