Evaluating Pitcher Talent
The discussion of what statistics are useful in evaluating a pitcher came up in the game thread, again, last night. This issue comes up quite a bit around here, since I use a lot of non-conventional numbers, and new readers often don’t know what they mean, where to find them, or why they should bother. So, last night, I decided to write something of a primer on why I like to use the statistics that I use, what their usefulness is, and why I don’t really care about things like ERA, WHIP, or batting average against.
All the stats referenced, by the way, can be found at the Hardball Times, and detailed game logs using these numbers can be found at Fangraphs, which are two of the most awesome sites out there right now.
The mainstream tools for evaluating a pitcher’s success and abilities are won-loss record and earned run average, with fantasy baseball players often add WHIP (walks+hits per inning pitched) to the discussion, since it’s one of their categories. These statistics attempt to sum up pitcher effectiveness in total, giving an overview of the totality of his performance with just a few numbers.
I, personally, think they fail in that regard. ERA and WHIP group together a large string of individual events made by multiple players, making it extremely tough to separate out the credit for the pitcher, hitter, or defense. WHIP and ERA tell you there is no difference in an inning where three batters drive the ball to the fence and end up with three long flyouts or an inning where a pitcher strikes out the side. Clearly, they’re drastically different, but WHIP and ERA fail to account for the actual contributions of the pitcher. So, if the goal is to actually find out how well a pitcher threw, why not look at a micro level, instead of a macro level? That’s what I prefer to do.
For instance, what are the possible events in an at-bat that can occur?
A pitch can be thrown for a ball.
A pitch can be thrown for a strike.
A pitch can be swung at and missed.
The ball can be hit on the ground.
The ball can be hit on a line.
The ball can be hit in the air.
On any given pitch, those are the options. There are a few sub-categories under those options (outfield fly or infield fly, bunt grounder or normal grounder, etc…), but we can sum up every possible outcome of each pitch with those six options. Those outcomes might lead to wildly different events, but we’ll get to that later.
Which of these six outcomes are positive for the pitcher? Called strike, swinging strike, and groundball.
Which of these six outcomes are positive for the hitter? Called ball, line drive, and flyball.
If we can effectively determine which pitchers maximize their value in the “good outcomes†and minimize their harm in the “bad outcomesâ€, we can get a pretty firm grasp on who has pitching talent and who does not. Thankfully, Dave Studeman wrote a fantastic article called “Whats A Batted Ball Worth” in the 2006 Hardball Times Annual, and it includes the following run value chart. This chart will give a context to those good and bad outcome categories:
Line Drive: .356 – in other words, an average line drive is worth 35% of one run.
HBP: .342
Non-Intentional Walk: .315
Intentional Walk: .176
Outfield Fly: .035
Groundball: -.101
Bunts: -.103
Infield Fly: -.243
Strikeout: -.287
These run values were taken from real life play-by-play data, so this is an actual representation of events, not some theoretic formula. As you can see, a hit-by-pitch is a better event for the offense than a walk, even though they both simply put the batter on first base. Why? Because a hit-by-pitch is pretty much random, and can occur both at times when it is a critical situation and times when it isn’t. A walk, conversely, is far more likely to put a runner on first base in a run scoring situation, lowering it’s run value compared to the HBP.
As you can see, the difference between an outfield fly and a groundball isn’t huge, but its real, and it adds up over the course of the season. This is why, all things equal, a groundball pitcher is better than a flyball pitcher. All things are almost never equal, and flyball pitchers tend to have higher strikeout rates than groundball pitchers, but the theoretical best pitcher alive would be a groundball pitcher, not a flyball pitcher.
Also, bunting = bad.
So, now that we have some understanding of the possible outcomes and their relative value, instead of using statistics like ERA or WHIP that leave out critical information, our best bet is to try to quantify the six potential outcomes, and the events that result from those outcomes as best as we can.
BB% (Walks per Total Batters Faced) does a nice job evaluating how often a pitcher throws the ball in the strike zone. The average walkrate is 8% for a major league pitcher, though the DH makes the AL a higher walk league than the NL. Anything under 5% is tremendous, and anything over 11% is a problem. The Hardball Times publishes BB% and K% in a slightly different manner, calling it BB/G or K/G to make it scale more like the per nine innings numbers people are used to seeing. BB/G (and BB%, its derivitive) is more effective than BB/9 because it accounts for the actual amount of batters faced rather than using a proxy like innings pitched. It’s just more accurate.
K% (Strikeouts per Total Batters Faced) does a decent job evaluating how often a pitcher induces swings and misses or called strikes. 16% is league average, with 20% being terrific and 12% being a problem.
GB% (Groundballs per Balls In Play) does a very good job of telling us how often a pitcher induces a groundball. 42% is league average, and anything over 50% is terrific, with the best sinkerball pitchers posting rates in the 60-65% range, while anything below 35% can be a problem if its not offset with a high strikeout rate.
LD% (Line Drives per Balls In Play) does a very good job of telling us how often a pitcher gives up line drives. 20% is league average, 17% is good, and 23% is a serious problem. Because of the way line drives have been scored by Baseball Info Solutions the past couple of years, this number is hard to use for year to year analysis, and right now, it’s not a very effective tool. We don’t use it very often.
FB% (Flyballs per Balls In Play) does a very good job of telling us how often a pitcher gives up flyballs that leave the infield, and is basically the corollary to GB%. 36% is league average, while 32% is good and 40% could be a problem.
So we have five statistics that cover each of the six possible outcomes pretty effectively. Not perfect, but they do a credible job. They aren’t park adjusted (and yes, parks have an effect on things you might not expect, such as walk rates, strikeout rates, and groundball rates), but they’re pretty close for the majority of cases.
Thanks to the work of guys like Voros McCracken, Tom Tippett, Keith Woolner, and Dave Studeman, we also now know that the result of a particular ball in play is also not very consistent, and is due more to the actions of the hitter than the pitcher. So, when evaluating pitcher’s talent, we need to adjust for outlier type performances on converting outs on balls in play. If a pitcher has a lot of flyballs that are being caught on the warning track, or groundballs that are going right to infielders, that’s not likely to continue, and we shouldn’t assume that it will.
Not all balls in play are created equal, however, and so when we’re adjusting for outs on balls in play, we need to make sure we’re adjusting back to the type of ball in play the pitcher is giving up, since we’ve noted that they certainly do have control over their groundball or flyball tendencies.
An outfield fly becomes an out 77.7% of the time. A groundball becomes an out 74.8% of the time. A line drive becomes an out only 26.4% of the time, which is why it’s the worst possible outcome for a pitcher. An infield fly becomes an out 98.8% of the time. Because of this, flyball pitchers will post more outs on balls in play than groundball pitchers, and it won’t be a fluke. However, the non-outs that flyball pitchers give up are more harmful, and thus, the quality of the hits against flyball pitchers outweighs the relative lack of quantity. This is shown in the run value chart above, where an average groundball is a positive event for the pitcher and the outfield flyball is not.
Infield flies are automatic outs, essentially, so it’s best to separate them from outfield flies for analysis like this. Since evidence has shown that pitchers don’t have a strong year to year control over their infield fly percentage, however, when evaluating true talent levels, it’s best to assume something like a normal infield fly percentage for a pitcher, rather than the one he’s posting at the moment.
Two other big factors that we’ve identified that can have a great effect on run scoring are home run rates and stranding runners. In general, flyball pitchers give up more home runs than groundball pitchers, which is why a groundball is a positive event for the pitcher and a flyball is not.
We’ve seen very little evidence that major league pitchers have significant control over how often their flyballs go over the wall, so occassionally you’ll see a wild swing in performance that is not indicative of a players true talent level, simply because a pitcher is having more or less flyballs go over the wall than should be expected. Felix Hernandez in April and May of this year was a great example of a guy who allowed a lot of home runs per flyball, and that rate has steadily dropped as the season wore on. The average major league pitcher gives up home runs in about 11-12% of his outfield flies – significant variation from that is probably not an indicator of talent for a major league quality pitcher.
Stranding runners is also a big key, and a bit of a different animal. Naturally, good pitchers will strand more runners than bad pitchers. Since they’re good pitchers, they’re more likely to create an out in any situation, including with men on base, than if they weren’t a good pitcher. While the league average Left on Base Percentage is 70%, the bad pitchers often live in the low-to-mid-60% range, and the good pitchers live in the mid-to-high-70% range.
However, it’s not uncommon for bad pitchers to have flukily high strand rates that significantly lower than ERAs, and vice versa. Jarrod Washburn’s 2005 ERA was almost completely due to his high strand rate, as he posted the highest LOB% in the American League. That hasn’t held true in 2006, and we’ve seen his ERA rise a full run because of it. So, when you find a pitcher who is stranding runners at an unexpected rate when compared to his talent derived by BB%, K%, and GB%, it is prudent to expect that rate to regress back towards a more normal rate in the future.
So, looking at this breakdown, we see value in BB%, K%, GB%, HR/FB%, and LOB%. Those five statistics will tell you almost everything you need to know about what goes into why a pitcher is performing like he is, and all these statistics are easily available at The Hardball Times. There’s nothing that ERA or WHIP will tell you that those component statistics do not, but ERA and WHIP certainly leave a lot of the underlying information out.
However, it is understandable that people want one number that sums up pitcher performance. If you really prefer to not look through the prism of BB/K/GB/HR-FB/LOB percentages, you can always use FIP, or Fielding Indpendent Pitching (which I often call Fielding Independent ERA, since its scaled to look like ERA), which gives you an expected ERA for a pitcher based on his walk, strikeout, and home run rates. FIP isn’t perfect, either – it assumes that HR/FB is indeed a skill, and it assumes that all pitchers are equal at stranding runners, neither of which are true, but it’s better than ERA for summing up a pitcher’s total contributions to run prevention.
If you want to get really crazy, you can even use Expected FIP, or xFIP, which substitutes the league average home run per fly ball rate for the pitcher’s actual home run rate, giving a more accurate picture of how we’ll expect a pitcher to perform going forward as his HR/FB rate regresses towards the mean.
As I said, both FIP and xFIP have flaws, especially when it comes to evaluating relief pitchers, but if you’re insistent on using one number to sum up a pitcher’s contribution to run prevention, those would be your best bet.
In this age of wonderful information, there’s just no reason to use ERA and WHIP for serious analysis of a pitcher’s ability. We have better tools at our disposal. We’re doing ourselves an injustice if we continue to lean on inferior information.
Dave – Can you quantify the problem with Adam Kennedy this year? He’s second in the AL with a LD% of 27.3 but his BA is in teh .260s and his OPS in under .700. You’d think someone hitting the ball on a line essentially once a game would have better numbers with the average value of a line drive so high. Yes, I know Adam has no power, but it still seems strange for a full-season sample.
And another one quickly while I think of it…is there any possibility that Woods can learn to cut down on the number of walks he issues? His only real noteworthy season was in Arkansas (AA) in 2004, when he Kd 60 and walked just 19 in 90 innings. He moved to AAA the same year, pitched 83 more innings, again struck out 60 against better hitters, but walked a ridiculous 42 batters. Does he just have no control or can he learn to target better?
If you don’t care about defense or park effects, sure. Did you know the Tigers defense is, by far, the best in baseball this year? It’s not even close. And Comerica is notoriously pitcher friendly.
Okay, I admit this is a fairly stupid nit to pick in this particular discussion, but I don’t think that the Tigers’ defense is by far the best in baseball this year. They have, by far, the best defensive efficiency. However, looking at defensive efficency totals so far this season, I don’t think it’s a coincidence that four of the five top teams play in notorious pitcher’s parks with large outfields. (As for the fifth, St. Louis, I’m unsure about how the park factor has played out this year in the new Busch Stadium.) After adjusting for park factors, I’d guess that Detroit comes back towards the pack a fair bit.
The reason this is a stupid point to belabor is that for the purposes of singling out pitching performance we want to adjust for defense and park factors, and unadjusted defensive efficiency is perfectly legit for that purpose. But in judging defenses, I think park effects aren’t emphasized enough, so I decided to mention it anyway.
It’s not defensive efficiency that makes the Tigers so good – they’re making the plays that no one else does, and its not the outfield. If you go to the THT’s Team pages, you’ll see a column for +/- fielding by air and ground. Basically, this shows how many extra plays a team is making on groundballs and flyballs than would be expected base on ball in play types.
The Tigers are +65 on the ground. +65! The next best team in the AL is Toronto at +16. Brandon Inge and Placido Polanco are both tremendous, elite defensive infielders. Pudge is still very good at getting out on balls in front of the plate. Shelton and Guillen are average defenders, and they’re the wink links. Even the Tigers reserve infielders, Omar Infante and Neifi Perez, can pick it.
The Tigers defense isn’t a park effect illusion. Their infield defense is something else.
Dave – Can you quantify the problem with Adam Kennedy this year? He’s second in the AL with a LD% of 27.3 but his BA is in teh .260s and his OPS in under .700. You’d think someone hitting the ball on a line essentially once a game would have better numbers with the average value of a line drive so high. Yes, I know Adam has no power, but it still seems strange for a full-season sample.
J.C. Bradbury developed Predicted OPS (called PrOPS), which is basically an expected BA/OBP/SLG line based on ball in play types. Kennedy’s PrOPS is .728, 40 points higher than his actual line, and his Projected BA is .291. So, you’re right, we’d expect a normal hitter with that kind of LD% to post better numbers.
However, PrOPS overrates slow players, and Kennedy’s injury problems have cost him a lot of his speed, and it assumes that all line drives are created equal. Considering Kennedies total lack of power, that’s probably not a good assumption. More likely, his line drives are easier to catch than, say, Albert Pujols’ line drives.
Kennedy may be experiencing a bit of bad luck, but even if he is, his lack of power still makes him a marginal major league hitter.
Dave – any idea which pitchers get the most swing-and-misses? And, do these pitchers generally match up with the scouts’ view of who has the best stuff?
Dave – any idea which pitchers get the most swing-and-misses? And, do these pitchers generally match up with the scouts’ view of who has the best stuff?
No, this is something I really want to get my hands on, because I have a lot of theories about called strikes versus swinging strikes. But that data just isn’t publically avialable right now in any kind of sample that would lend itself to a thorough analysis.
How random is BABIP for hitters? For example, Jason Giambi is posting the similar power/walks number as his glory years in Oakland, but he’s only hitting .260 or so, and he is near the bottom in BABIP. Where as Scott Podsednik has a good BABIP but a similar batting average. Is the reason that Giambi can get away with a lower BABIP because of his power, or is it random?
BABIP is much less random for hitters than it is for pitchers, but it’s still subject to fluctuation. A lot of power hitters are going to post low BABIPs because when they hit the ball well, it leaves the yard, so those get removed from the denominator as its not a ball in play.
I personally don’t see BABIP adding a lot of information to our knowledge about hitters. It’s useful for pitchers, but not really for offensive evaluations.
This is off the top of my head, but as poster #28 mentions, isn’t there a potential problem with how balls in play are classifed as one of three discrete event types?
It seems a fair number of struck balls sit on a category cusp – a scorching one-hopper to the third baseman or a soft drive that falls in front of an outfielder. A differnce of three or four judged one way or another over six innings might amount to a significant difference, it seems.
And biases in what various observers consider to be line drive/ground ball/fly ball would tend to be consistent and magnify differences between pitchers, since each observer would tend to follow one particular pitching staff.
Maybe all of this is dealt with on the sites mentioned.
BIS does a good job recording the data. There’s no reason to be worried about the integrity of the information.
60 – sidroo raises a question I’ve wondered about. Who is collecting this data? If it’s a stringer who does it at each ballpark, you could get subtle “park effects” on these numbers based on, for instance, whether the Safeco field stringer tends to classify hard hit balls through the infield as line drives more often than an observer at another park. Or whatever.
it leaves the yard, so those get removed from the denominator as its not a ball in play.
Yeah that would make it pretty useless for hitters. I just saw it listed as a sortable for hitters, so… thanks for taking the question though. This beats the hell out of an ESPN chat.
60 – sidroo raises a question I’ve wondered about. Who is collecting this data? If it’s a stringer who does it at each ballpark, you could get subtle “park effects†on these numbers based on, for instance, whether the Safeco field stringer tends to classify hard hit balls through the infield as line drives more often than an observer at another park. Or whatever.
It isn’t a stringer. Baseball Info Solutions trains their employees to ensure accuracy, and this is what they do for a business. They sell this info to major league teams – accuracy is their currency.
The data is good.
When they give a run value for bunting, is that bunting for a hit? Or bunting to advance a runner?
Fantastic post, Dave. I’ve given all my baseball buds the link to check it out, and it will be extremely useful in any discussion I ever have with anyone online about baseball.
Well done.
When they give a run value for bunting, is that bunting for a hit? Or bunting to advance a runner?
Both. It’s the act of squaring around and intentionally trying to hit the ball 20 feet.
Bunting is also necessarily skill-related. Some guys are really good bunters and do help create runs by bunting. But that’s not what these are designed to measure. League-wide, bunting is bad.
The real question I have after reading this is how in the world can major league teams still be ignorant to this kind of analysis?
It seems like the Mariners are willfully ignorant of this kind of in depth thought and it really hurts the team. Why don’t they invest in some statisticians?
What will it take to get them to open their eyes to more reliable ways of judging talent? Surely its not just an old boys network of ‘baseball guys’ who are sitting in some wood-paneled room smoking cigars and talking about how they should trade for someone because, “the kid’s got heart, damnit!” Right?
I got a tour of the White Sox corporate offices at Comiskey (aka US Cellular) a few years back. It happened to be on a draft day early evening, and it really was a smoke-filled room (as in literally billowing into the hallway) of scouts and old baseball guys.
During the same tour, they also went out of their way to discuss a little of their baseball-related technology. One system (I think it had a name, and is in use by most if not all major league teams) stored years of indexed video, such that if you wanted to see all Maglio Ordonez at-bats against Jamie Moyer during night games at home (for example), you could.
Nice job, David. You really should try writing for those THT guys.
Regarding BIS, they not only have stringers at ballparks, but they have every game doublechecked by QA guys. Each game is essentially scored twice, as I understand it. That said, the difference between a line drive and flyball is ephemeral to me.
For FIP, we calculate basic FIP (without the add-on) and then calculate what the league-specific add-on is based on actual league ERA, then use that for all pitchers in that league.
Best USSM thread ever?
Surely its not just an old boys network of ‘baseball guys’ who are sitting in some wood-paneled room smoking cigars and talking about how they should trade for someone because, “the kid’s got heart, damnit!†Right?
Well, and sometimes that works. But the teams that avail themselves of all information — scouts and stats both, as Dave has always suggested — will tend to separate themselves from those that don’t. Unfortunately the old boy network is strong, and so is tradition, and “baseball men” tend to surround themselves with other baseball men… so it may take a decade or two as they die off. Baseball is probably the worst sport in this regard for various reasons — not least because it doesn’t have a huge and highly-visible college analog where maverick ideas have a chance to be tried and succeed. Any short term failures by the “whiz kids” — and there will be some, of course — tend to mask the benefits of the approach (just ask Beane about the playoffs, or DePodesta about, well, anything) so it takes all the longer. It’s also worth noting that this kind of data is much harder to come by for minor league players, and of debatable worth when it is available, so that limits its applicability to trades and free-agent acquisitions (though of course that’s where most of your budget goes).
Best USSM thread ever?
I can’t think of a better one. But it starts with the post, and Dave’s work here is among the top two or three articles I’ve ever seen on the site (and among the top baseball-related articles I’ve ever read on the web).
On a related note: I expect this article is going to get linked from all over, and lots of people will be telling others to read it, so it will probably become one of the most-read pages on the site. If ever there was a page on USSM that should have either some google adwords and/or a (more) prominent link to buy USSM swag, this is it. Articles of this quality improve my enjoyment of the game, and that’s a rare and special thing — and worth a lot. USSM deserves support in general, but it’s articles like this that make me hope they get more revenue so they can keep the thing going as more than a sheer act of love. In a better world Dave would get paid a lot to write like this, but I fear in this world that will only happen in a way that forces the rest of us pay to read him.
And it would be nice if USSM had the resources so its server didn’t fall over the next time, say, Willie Bloomquist is DFA’d.
The real question I have after reading this is how in the world can major league teams still be ignorant to this kind of analysis?
There’s not a major league team that doesn’t have someone on staff who understands these concepts.
It seems like the Mariners are willfully ignorant of this kind of in depth thought and it really hurts the team. Why don’t they invest in some statisticians?
Looks can be deceiving. The M’s have Mat Olkin on payroll, and Mat certainly knows more about baseball than I do. None of this would even cause him to blink an eye.
What will it take to get them to open their eyes to more reliable ways of judging talent? Surely its not just an old boys network of ‘baseball guys’ who are sitting in some wood-paneled room smoking cigars and talking about how they should trade for someone because, “the kid’s got heart, damnit!†Right?
No, it’s not. The Mariners, especially, are nothing like that.
Nice job, David. You really should try writing for those THT guys.
I find that they have too many guys named Dave as it is.
Best USSM thread ever?
I don’t know that this can compete with traffic in Idaho.
Looks can be deceiving. The M’s have Mat Olkin on payroll, and Mat certainly knows more about baseball than I do. None of this would even cause him to blink an eye.
But do they listen to him? I mean the Washburn signing seems like they pretty much paid for ERA, not any of the better indicators, or am I missing something?
But do they listen to him? I mean the Washburn signing seems like they pretty much paid for ERA, not any of the better indicators, or am I missing something?
They listen to him. They may not act on what he says at all times, and there are certainly other voices in the front office, but that doesn’t mean the M’s are “willfully ignorant” of this kind of analysis.
It just means they value it less than we do.
The reason I like this post so much is that it’s extremely clear. Dave isn’t always as easy to follow as this (though he’s far, far, lightyears far from the worst in this regard) — sometimes he blazes past the obvious into the world of acronyms too quickly, which is understandable. But once in a while it’s good to sit down and start from scratch, and painstakingly mark out exactly what you know, step by step, and what the conclusions are. This is valuable even for people who know it all already, or think they do. And for newcomers it’s invaluable. Bravo, Mr. Dave.
The followup discussion has been top-notch, too.
But none of this analysis can explain that little grin Felix tried but failed to supress in the eighth when I think it was Kennedy swung at a pitch that fell off the table.
#71–One system (I think it had a name, and is in use by most if not all major league teams) stored years of indexed video, such that if you wanted to see all Maglio Ordonez at-bats against Jamie Moyer during night games at home (for example), you could.
hey! the Mariners have one of those, it’s called Carl Hamilton 🙂
actually it was this spring that Carl was the 1st recipient of the Professional Baseball Video Coordinators Association Award for Excellence. The award was also named the Carl Hamilton Award…
Bookmarked and forwarded to a number of friends. Thanks!
Dave-
[First time posting, so please be gentle if I inadvertently violate local etiquette.]
I was wondering if “damage control†is a real pitching skill, whether it has been systematically assessed, and if it is comprehensibly explainable by the factors you’ve discussed. I assume that strikeouts and ground balls are good outcomes for a pitcher in trouble, but it seems to me that some pitchers are more than just lucky at getting these when most important. For years I’ve heard M’s broadcasters say that certain pitchers are “good at damage control†while others “can’t stop the bleeding.†Is the ability to “minimize the damage†a real pitching skill that carries over from season to season, or upon close examination does it regress to the mean?
I’d think such a skill would be somehow evident in how the runs scored in scoring innings were distributed (fraction of runs scored in one run innings, fraction of runs scored in two run innings, etc.). I’d also guess that pitchers would get better at this as they matured, but would gradually regress as they lost their “stuffâ€. They’d learn what pitch to throw with experience and be able to make it, but eventually be unable to make quality pitches on demand.
Obviously, I know my guesses don’t constitute data, but I don’t have any idea how to assess this myself. I’d appreciate your thoughts on the matter.
Since this is my first time posting, I’d like to thank you for the many hours of enjoyment I’ve had at this site, and for helping me understand the game I love so much better.
I think it’s rare, but it exists in some extremely intelligent pitchers. Tom Glavine, for one, has consistently posted really low HR rates with runners on base, but his walk rate goes up, and with the bases empty, the walks go down and the homers go up. The evidence suggests that when Glavine gets in trouble, he nibbles and refuses to let hitters get a pitch they can clear the bases with. When he’s only risking one run, however, he attacks. As such, he’s managed to keep runners from scoring as often as we would expect a normal pitcher with his skill to do.
But there aren’t many examples like Glavine. So, I’m going to say its possible, but it’s not something we’d expect to see in a pitcher.
Good summary Dave!
***
For FIP, you adjust the constant to reflect the league. Since 1994, it’s been around 3.20. Pre-1994, it’s been 3.00. FIP is a quick equation, and therefore, try to keep it as simple as possible.
***
“Why? Because a hit-by-pitch correlates pretty well with “struggling pitcherâ€, and so more struggles are likely to follow.”
This part is not true. The fact is that a walk is given out more with 1b open, or 2 outs, than otherwise. That is, a walk is more non-random than a hit batter. If you look at each of the 24 base/out states (and given a large enough sample), the run values of the walk and HBP will be virtually the same, for each state. But, since the frequency of each state will be much different, this will account for the difference between a walk and a hit batter.
Where do they show K% and BB% on the THT site and fangraphs….all i see is K/9 and BB/9……..not total batters faced.
Nate Silver has specifically complained that Glavine’s approach breaks PECOTA.
I’m skeptical. What are the actual results that prove the assertion? It seems to me that all of this misses the forest for the trees. Of course there’s a difference between a line drive out and fly ball out. WHIP and ERA don’t claim otherwise. They remain the most useful measure of trends and results across an entire season played out in all kinds of weather and with usable splits. I’ll bet that pitchers with a danger-zone high percentage of line drives and fly balls have high ERAs and high WHIPs, too. Those two numbers take less labor to derive, though, and I’ll stick with them until someone provides real data that shows me they don’t work.
You could start with the fact that FIP is a better predictor of ERA than (past) ERA itself is. In other words, these component statistics have less year-to-year noise than ERA, which is saddled by dependence on luck/defense/opposition.
Actually, WHIP and ERA do claim otherwise. They don’t distinguish between types of outs at all. For example, a pitcher who gets 3 strikeouts in an inning and a pitcher who induces 3 lineouts in an inning both have a WHIP of zero for that inning. The types of outs make no difference to the calculation of WHIP (or ERA).
This is an assertion that you’ll need to provide some supporting evidence for.
You should note that nobody refutes the idea that ERA and WHIP provide a picture of what has occurred. The issue is that what has occurred is coloured by the actions of the defense and by the quality of the opposing batters. In the context of evaluating pitchers, it makes sense to try to remove the influences of the defense/opposition, and that’s what the component statistics do.
Sure, and you can print out a document with an 80s-style dot matrix printer. But the picture is so much clearer if you use a modern printer.
There are several links to the Hardball Times in the post and comments. It’s not exactly a lot of labour to look up the numbers. And if you want to calculate them yourself from the raw numbers, it isn’t hard either: BB% = (# walks)/(# batters faced), and so on.
The Jarrod Washburn example in the post is a good example of how ERA can break down (high strand rate in this case) but unfortunately I can’t find anything which shows the poor year-to-year correlation of ERA. Anyone?
Oh dear.
I thought from his FIP that at least he wouldn’t be a really bad pitcher, but from what you’ve said it sounds like his chances of collapsing are actually pretty high. Which means that 4th year (if not the others) could turn out to be a real pain.
Why is there never good news?
I guess that at least Silva could be OK for a while if his command lasts, whereas Kuroda might be dreadful from the start.
That silver linings looking thinner all the time though.
Yeah!