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ramssuperbowl99


Joined: 15 Apr 2005
Posts: 22846
PostPosted: Thu Jul 17, 2014 11:20 pm    Post subject: Stats with Rams: Pitching Reply with quote

So based on some of the feedback, one of the things that was determined to potentially be useful was an understanding of what stats people look at when making a judgment about a player. I figured the easiest was to break this up would be pitching, offense, and defense/baserunning, so here we go.

To start, where should you get these crazy numbers? Fangraphs is my number 1 resource for almost all things statistics at the MLB level. Baseballreference is a good resource as well, specifically if you’re more interested in nuanced things like “how many times has a guy struck out the side in an inning?” But in terms of getting the data that paints an overall picture of performance, Fangraphs has everything you need and a whole lot more.

Probably the easiest way to do this would be to use split screens and have the post up on one with a Fangraphs page for a pitcher (any pitcher, but hopefully one with a few years’ worth of data) on the other. I’m going to use a few specific examples, but will list out the specifics, so no worries if you aren’t on Clayton Kershaw’s or Madison Bumgarner’s Fangraphs page. With that of the way, let’s get going.

Step 1: Get your bearings
The first thing I do whenever I check a player’s page is do a quick scroll through the whole thing just to get a sense of who this guy is. Vague, yes. But when I pull up Clayton Kershaw’s page, check out the ‘Dashboard’ at the top, and I see he’s been worth 3.7 WAR in less than 100 IP, has an ERA, FIP, and xFIP under 2, and is getting a whole lot of grounders, I’ve already got an impression of him. More interestingly though, start scrolling down to the section titled ‘Pitch Type’, because this is going to be the foundation for everything we’re going to look at. It’s a pretty self-explanatory section in that it shows the percentage of the time the pitch is thrown with the velocity in parenthesis. At the top, the different pitches are listed, with XX% denoting pitches that the system can’t figure out.

The reason I asked you to get a guy with multiple years of data is because the first thing I do is get a sense for what has changed throughout the guys’ career (but more specifically this season versus maybe the last 2 or so).

In Kershaw’s case, a few things are pretty apparent:
1. He’s throwing his fastball less than he ever has, but is holding his velocity pretty steady.
2. He’s throwing his slider more, and at harder velocity than he ever has.
3. He’s throwing his curveball a little more, and at similar velocity.

You can confirm that using the ‘Pitch f/x pitch type’ and ‘Pitch f/x velocity’ sections right below, but don’t generally need to look at it too closely because the values match up quite well. Let’s keep going all the way down to ‘Plate Discipline’ (we can come back to pitch values at a later date, as they are more supplemental than anything else).

This is probably one of the bread and butter sections in terms of what I focus on, so you’ll be coming back here again. In the meantime, you see another big list of percentages.

First up, O-Swing% is something you definitely want to get used to. It is the percentage of balls thrown outside the strike zone that the hitter swings at. As you’d expect, higher is better and league average is 30.5% so far this year. Among qualified pitchers, Masahiro Tanaka leads with 38.5% and Shelby Miller trails with 24.2%. Kershaw is tipping the scales at 38.5% (he technically hasn’t qualified yet), a full 5 percentage points higher than he’s ever posted in his career so far.

Next up, Z-Swing% is the percentage of balls thrown in the strike zone that the hitter swings at. Here, lower is better, and league average is about 65%. Phil Hughes isn’t fooling many people at 73%, and CJ Wilson is out in front with 58%. Kershaw is just a little higher than league average at 66.2%, which brings me to an important point – while it’s good to have hitters swing at balls and take strikes, you can be an overwhelmingly dominant pitcher without posting elite numbers here, and just because you’re posting elite Z-Swing% numbers doesn’t mean you’re an elite pitcher. The key is to look at trends.

Swing% is there as a reference point for how often hitters are offering, but in general I don’t focus on that nearly as much as O-Swing%. In terms of importance, O-Swing% is substantially more valuable in my book than Z-Swing% or overall Swing%.

Continuing on, you see the same O- Z- and overall pattern, but with Contact %. This is the percentage of the time the batter makes contact when he swings at a pitch outside the zone, inside the zone, and overall, respectively. Learn to love these sections. The average rates are 65.7%, 87%, and 79.5%. If looking at Barry Bonds’ OBP once in a while makes you laugh, you might find it funny that Aroldis Chapman is posting numbers of 48.0%, 61.4%, and 55.8%.

Unlike Swing% stats, where they aren’t always predictive in terms of separating guys, these are useful. The guys with the worst O-Contact% right now are Kevin Correia, Bartolo Colon, Josh Collmenter, Mark Buehrle, Chris Tillman, and Chris Young. So basically, a whole lot of bad and 2 guys who had hot starts, but should come crashing back to earth any day now. Another common denominator in that group? Not much stuff. On the flip side, Garrett Richards, Tyson Ross, Ervin Santana, Yu Darvish, Tanaka, Felix Hernandez, Corey Kluber, and Stephen Stasburg round out the leaders. That’s a whole lot of good performance, and a whole lot of good stuff. And if you didn’t know who Tyson Ross was, look him up and he might surprise you a little. You can do similar experiments with the Z-Contact% and overall Contact%.

Next up? Zone%. It’s a bit more of a niche thing, but is basically the percentage of strikes. That’s not always useful on its own, but definitely has a role. If you have a starter who is still learning to throw strikes or harness raw stuff, this can be useful. Wily Peralta of the Brewers, for example, has seen his Zone% increase from 41.9% to 44.3% year over year, and this has likely been part of the reason his other peripherals and surface results (ERA, etc.) have been better.

But when I look at Zone%, I like to look at it in the context of the stat right next to it, F-Strike%, which is first strike percentage, and is exactly what it sounds like. Moving back to Kershaw, he’s throwing 3% more strikes overall, and 5% more first pitch strikes.

The last stat in this column is my favorite – SwStr%. This is the percentage of times a batter swings and misses. Always look at this in the context of the players K%, as this is the fundamental basis for it. With Kershaw, his K% has exploded this season from 25.6% to 34.4%. (By the way, use K% and BB% when possible instead of K/9 and BB/9, K/9 and BB/9 penalize pitchers who don’t allow many baserunners.) If you were only looking at surface stats, you’d probably prime him for regression back to the mean, right? But his swinging strike percentage has also exploded this year, going up from 11.4% to 15.2%. This means that he’s getting more strikeouts, but they are based on guys just being unable to hit him. He’s not getting them through borderline calls or fooling guys, which is much more difficult to sustain long term. And when you look at his swing rates (slightly above average) and contact rates (really good), the picture becomes clear – he’s not fooling guys, he’s overpowering them.

If you keep scrolling, you can see some fielding stats. We’ll look at those later, and really I don’t look at fielding too much for pitchers anyway.

Next up, you’ve got the ‘value’ page. This has 2 WAR stats that you should key on – RA9 WAR and WAR. RA9 WAR uses Kershaw’s RA/9 (NOT ERA) times an innings multiplier to calculate how many wins he’s been worth. Regular WAR uses a slight variation of FIP (the major difference being that the WAR calculation considers infield fly balls similar to strikeouts, since they are virtually always turned into outs) times an innings scale to do the same thing.

Step 2: Let the data tell the story
So that was a not-so-quick walkthrough of a whole lot, but we’ve already learned a lot about Kershaw. First, we’ve learned that his results are really good. Second, we’ve learned that he’s throwing a lot of strikes, not allowing a lot of contact/missing a lot of bats, and relying on that slider more and throwing it harder in the past.

Often times when you talk baseball with someone who’s more SABR-inclined, they talk about regression to the mean. What that basically means is that if a guy pitches the same as he did previously, the results should match what he’s done previously (or, since we like bigger samples sizes as much as we can get them, what guys who have pitched like him before have done previously).

But first things first, we need to ask ourselves, ‘is he pitching the same as he had previously?’ With Kershaw, the answer is an overwhelming, “No!” His pitch usage has changed, his slider is different than it was, and the results are bearing that out. Does that mean we should expect no regression? Probably not. After all, he’s been a big leaguer for a while now and had settled in pretty good with some fairly consistent numbers. But we know that 2014 Kershaw hasn't pitched like 2013 or any other Kershaw so far this year, so we shouldn't just blindly regress him back to what he was.

Rather than gush on and on about Kershaw’s slider, I’m going to change gears here and move on Madison Bumgarner’s page, because he’s been pitching fairly consistently in terms of what we’ve looked at already. Long story short, FB, SL, CB and CH usage all are virtually unchanged, and swinging/contact% are all pretty consistent as well. The only noteworthy difference is a slight uptick in Zone% and a corresponding bump in F-Strike%. In short, Bumgarner is pitching pretty similarly as he did last year, so we would expect pretty similar results.

But we don’t really see results that are all that similar if we go by surface stats, as a 3.47 2014 ERA doesn’t seem much at all like that shiny 2.77 ERA he had last year. What gives?

Well, in short, a few things that are beyond his control.

Step 3: What the hell is luck, anyway?
If there is one phrase that makes people averse to advanced stats lose it, it’s ‘he’s getting lucky’. Sadly enough, the entire problem here is that the information is being lost in translation, as we look at luck differently than someone like Joe Morgan.

When I say ‘luck’, I do NOT mean to take anything away from anyone’s accomplishments. Mark Buehrle has had a great start to the year and he deserves some credit for that. But based on what we know, moving forward, we don’t expect him to keep this up. That doesn’t mean he’s going to get worse suddenly (though maybe he does lose command). Maybe hitters start making an adjustment to his pitches and he gets rocked for a few starts before adjusting back. Maybe a few extra grounders start finding holes in the infield. Maybe a couple balls that were fly outs early in the year leave the yard.

At the end of the day, the bottom line is that we aren’t going to expect Mark Buehrle to have an ERA of 2.6 if he pitched another 1,000 IP exactly like he has now. There is a gap between his individual performance and the results he has obtained. That’s ‘luck’.

The next logical question is, ‘where does that gap in performance and results come from’? Well, if you go by ERA for results, a few things are often big red flags.

Let’s start with the one everyone is probably familiar with: BABIP. BABIP, or batting average on balls in play, is exactly that. Start with all of the ABs, take out strikeouts and home runs (anything the defense can’t catch), and what you’ve got left is BABIP. It’s counter-intuitive to almost everything we’ve watched in baseball, but pitchers have fairly limited control over it. League average BABIP has hovered around .300 for a while now, and is currently at .296 (and should continue to slowly decline as teams emphasize defense and get better at shifting and the like).

Best case scenario, you are looking at a major league pitcher with lots of innings worth of data. Then, it’s pretty easy – if they are pitching like they had previously, regress the BABIP to their career average weighing the more recent data first. The posterchild for low BABIPs is Mariano Rivera (career .263). If you tried to regress his BABIP to .296, that’d be foolish. After all, we know his cutter is the essence of the gods and no one can hit it hard, and he’s demonstrated that year after year.

What if you don’t have a nice career average? Regress to league average until you know otherwise.

Let’s get back to Madison Bumgarner. Last season, his BABIP was .251, despite a career BABIP around .290 at the time. This year, it’s .326. He’s been pretty much pitching the same, so we can probably conclude that his real BABIP would be right around that .290 mark that is both the average of those 2 and right around his career average. (One noteworthy thing here, you would expect Bumgarner to post a BABIP around .290 for the rest of the year, not end the year with a season long BABIP of .290. In other words, don’t project him to get extra lucky moving forward because he got unlucky to start the year. This is called the Gambler’s Fallacy, and you can google it for more info.)

Another common red flag is LOB%, which stands for Left On Base%. At Fangraphs, this is not calculated through box scores, but instead is an expected (formula here: http://www.fangraphs.com/library/pitching/lob/) value based on the number of times the pitcher lets guys on base, runs, and HRs. Therefore, it’s technically possible to have a LOB% over 100% if you were a pitcher who only ever allowed solo home runs despite allowing hits and walks (of course, that’s not very sustainable).

The league average LOB% is 72.9%, and just like BABIP, you can either regress to a career average (once again, weigh recent data more than older data) or league average, depending on the number of innings you have to work with. Unlike BABIP, we actually have a vague idea of what a guy can post, as pitchers have a little bit of control over this, though it isn’t much. The better the pitcher, the slightly higher the LOB% you can expect. To bring up Rivera again, his LOB% of 80.5% over his career is astoundingly high, but his cutter was really good. Among qualified pitchers, Josh Beckett leads at 86.0% and Tyler Skaggs brings up the rear at 61.8%.

Flashing back to Bumgarner, his LOB% is down from 75.7% to 71.0% this year. So, in other words, he went from a slightly above average LOB% (probably around where we would expect him to be given that he’s a very good pitcher) to a below average LOB%. I would expect that moving forward his LOB% would hover around the 75% mark, and I think that will have a lot to do with some of those unlucky hits he’s allowed previously finding gloves with runners on base.

Last, but not least, HR/FB% is something you should always be concerned about when it comes to potential red flags on performance. Like LOB%, pitchers do have some control over their HR/FB%, but not complete control. This year, the league average is 9.8%. Anibal Sanchez, Adam Wainwright, Garrett Richards, and Felix Hernandez lead the pack with rates between 3-5%, while Brandon McCarthy, Marco Estrada, Wade Miley, and Wily Peralta have values between 15.5 and 19%.

The names alone let you know there is a skill component (and Mariano’s career 6.5% rate helps validate that as well), but it’s not total. Thankfully, you can regress to either the career average, weighing more recent data more heavily, or league average and likely get close.

Step 4: Okay, so ERA isn’t it. What now?
What I basically just described was all the ways stuff that is at least partially outside a pitcher’s control can be involved in runs scoring, and therefore ERA. So we need a way to cut that out, and since we’re all good and lazy, we should probably scale whatever we come up with so that it looks like ERA.

Better yet, since we know there is at least partial control over HR/FB%, but not complete control, we should do 2 separate regressions. The first would be where the pitcher controls his HR/FB%, and the second is where they don’t. Enter FIP and xFIP.

FIP is an abbreviation for Fielding Independent Pitching, and it cuts out the BABIP and LOB% completely while leaving in the HRs. The net result is that you get credit for strikeouts, are penalized for walks, and are really penalized for HRs. In short, FIP = ((13*HR)+(3*(BB+HBP))-(2*K))/IP + constant. The numbers are based on historical run data (indicating that HRs are 13/3 more valuable than walks) and the constant is just the difference between the league average FIP and league average ERA.

xFIP is expected Fielding Independent Pitching, and it is like FIP, but doesn’t give you credit for HR/FB% prevention. What it does instead is give you credit for how many ground balls you get (as ground balls are virtually never home runs) by taking (fly balls*the league average HR/FB%) and substituting that in for the HR term in the FIP equation above. Like FIP, it is scaled to ERA, so you already know that 2 is otherworldly, 3 is good, 4 is mediocre, and 5 is bad.

So the million dollar question is, which do you use? Well, refer to step 2 and let the data tell the story. If you have a guy who over the course of his career has been excellent at limiting his HR/FB%, use his FIP. If he’s league average with it or you aren’t sure yet, use xFIP (which is proven to be a little bit more accurate for the entire league’s population).

There are other stats that are in the same class of stats as FIP and xFIP, such as tERA, SIERRA (the most accurate of them, but also the most complicated, as it adds quadratic terms to help compensate for double plays, among other things), and more, but really the value added doesn’t quite match the extra work. Provided you understand xFIP and FIP, you can still interpret SIERRA, as they are all scaled to ERA.

Takeaways:
If you follow these types of numbers ,you’re going to have a great idea of what a pitcher is throwing, how his stuff is in terms of missing bats, and idea of his control (and maybe even somewhat command), a great idea of how his performance has been, and a good idea of where his performance is going. Better yet, you can identify trends and confirm them using fundamental things like swinging strike % or strike %. Maybe most powerfully, you can start to separate a pitcher’s performance from that of his defense.

The single biggest thing is that no pitcher is the same, and you should let the data tell you how to handle it. Is he pitching the same? Great, you’ve got a large sample to work with and to regress to when needed. Is he pitching differently? Then you can analyze how he’s pitching differently and start to come up with educated guesses on how that will impact his results, both past and future.

One Last Thing
Any feedback? If you're experienced, anything to add? If you're newer, was this helpful? I'm open to format and/or content changes.
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Title Town USA wrote:
Don't question Rams. He runs this place. He is The Man.


Last edited by ramssuperbowl99 on Fri Jul 18, 2014 7:51 am; edited 5 times in total
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green24


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PostPosted: Thu Jul 17, 2014 11:23 pm    Post subject: Reply with quote

High-quality stuff here (haven't read through the entire thing yet).

I like using these stats (meaning swing% and others in that category) to analyze the growth of young pitchers. As the fan of a team built around young pitching, this is very crucial. A deep breakdown of Zack Wheeler's FanGraphs page illustrated his improvements from last season.
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aceinthehouse wrote:
I'm so confident in the Skins this season (offensively), I think they challenge the Patriots scoring record.
Call it a hunch. Idea


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biggio7


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PostPosted: Thu Jul 17, 2014 11:26 pm    Post subject: Reply with quote

So many numbers and math. Brick wall jk

Awesome stuff. I'm going to read through this tomorrow. I do have trouble with some of the sabermaterics stats. Which stat lines are more subjective than ones that are just all math and can actually be calculated? Does that question make sense? Sorry if it doesn't.
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green24


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PostPosted: Thu Jul 17, 2014 11:28 pm    Post subject: Reply with quote

biggio7 wrote:
So many numbers and math. Brick wall jk

Awesome stuff. I'm going to read through this tomorrow. I do have trouble with some of the sabermaterics stats. Which stat lines are more subjective than ones that are just all math and can actually be calculated? Does that question make sense? Sorry if it doesn't.

None are really subjective, but I think I get what you're saying. You want to know which are more imperfect and not just analyzing facts. The answer to that would be metrics like DRS and UZR.
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aceinthehouse wrote:
I'm so confident in the Skins this season (offensively), I think they challenge the Patriots scoring record.
Call it a hunch. Idea
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ramssuperbowl99


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PostPosted: Thu Jul 17, 2014 11:30 pm    Post subject: Reply with quote

biggio7 wrote:
So many numbers and math. Brick wall jk

Awesome stuff. I'm going to read through this tomorrow. I do have trouble with some of the sabermaterics stats. Which stat lines are more subjective than ones that are just all math and can actually be calculated? Does that question make sense? Sorry if it doesn't.
The only formulas more complicated than a middle school level I mention are SIERA (which you don't need to use really) and WAR.

The only reason WAR is complicated is because it requires the calculation of a league average player and then a replacement player. It's not hard or overwhelmingly subjective, it just requires a lot of computation to get that baseline.

The vast majority of the stats I mention are percentages, and really more 'scouting' than 'SABR'.
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green24


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PostPosted: Thu Jul 17, 2014 11:38 pm    Post subject: Reply with quote

ramssuperbowl99 wrote:
biggio7 wrote:
So many numbers and math. Brick wall jk

Awesome stuff. I'm going to read through this tomorrow. I do have trouble with some of the sabermaterics stats. Which stat lines are more subjective than ones that are just all math and can actually be calculated? Does that question make sense? Sorry if it doesn't.
The only formulas more complicated than a middle school level I mention are SIERA (which you don't need to use really) and WAR.

The only reason WAR is complicated is because it requires the calculation of a league average player and then a replacement player. It's not hard or overwhelmingly subjective, it just requires a lot of computation to get that baseline.

The vast majority of the stats I mention are percentages, and really more 'scouting' than 'SABR'.

Middle school math might be giving it too much credit. Then again the intelligence level of the average American isn't too high. In the time since I opened this thread, I heard a Yankee fan call onto the radio to say that David Phelps is better than Zack Wheeler and a more valuable asset than him now and going forward because he has a higher career winnin percentage. I mean you can't make this [inappropriate/removed] up. People would prefer to be uninformed than to use a brain cell or two.
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aceinthehouse wrote:
I'm so confident in the Skins this season (offensively), I think they challenge the Patriots scoring record.
Call it a hunch. Idea
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biggio7


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PostPosted: Thu Jul 17, 2014 11:38 pm    Post subject: Reply with quote

green24 wrote:
biggio7 wrote:
So many numbers and math. Brick wall jk

Awesome stuff. I'm going to read through this tomorrow. I do have trouble with some of the sabermaterics stats. Which stat lines are more subjective than ones that are just all math and can actually be calculated? Does that question make sense? Sorry if it doesn't.

None are really subjective, but I think I get what you're saying. You want to know which are more imperfect and not just analyzing facts. The answer to that would be metrics like DRS and UZR.


That's what I was wondering cause I know there are some like that. I just wasn't sure which ones. I'm fine with the new analysis going on in baseball. I just hate when people do use the "stats" that are subjective in arguments.
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ramssuperbowl99


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PostPosted: Thu Jul 17, 2014 11:40 pm    Post subject: Reply with quote

green24 wrote:
I like using these stats (meaning swing% and others in that category) to analyze the growth of young pitchers. As the fan of a team built around young pitching, this is very crucial. A deep breakdown of Zack Wheeler's FanGraphs page illustrated his improvements from last season.
It's less fun, but it works the other way as well. I had some blurbs on Verlander and pitch values, but scrapped them because I don't find pitch values overly informative as a stand alone thing.
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green24


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PostPosted: Thu Jul 17, 2014 11:44 pm    Post subject: Reply with quote

ramssuperbowl99 wrote:
green24 wrote:
I like using these stats (meaning swing% and others in that category) to analyze the growth of young pitchers. As the fan of a team built around young pitching, this is very crucial. A deep breakdown of Zack Wheeler's FanGraphs page illustrated his improvements from last season.
It's less fun, but it works the other way as well. I had some blurbs on Verlander and pitch values, but scrapped them because I don't find pitch values overly informative as a stand alone thing.

I remember you or someone else posting the ridiculous changes in his pitch frequency a couple days ago.
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aceinthehouse wrote:
I'm so confident in the Skins this season (offensively), I think they challenge the Patriots scoring record.
Call it a hunch. Idea
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mse326


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PostPosted: Thu Jul 17, 2014 11:58 pm    Post subject: Reply with quote

Quote:
But first things first, we need to ask ourselves, ‘is he pitching the same as he had previously?’ With Kershaw, the answer is an overwhelming, “No!” His pitch usage has changed, his slider is different than it was, and the results are bearing that out. Does that mean we should expect no regression? Probably not. After all, he’s been a big leaguer for a while now and had settled in pretty good with some fairly consistent numbers. But we know he’s better, so we need to take that into account.


I have a slight problem with this statement. We know that he is throwing pitches at a different rate and we know velocities. We know that his O-Swing% increased and the various contact% has decreased. But there is still an assumption as to why.

Throwing a slider more and harder does not mean it's better. In fact many times with breaking pitches throwing it harder makes it worse from a movement standpoint. So I wouldn't say we KNOW he's better. We know those stats are better, but we don't know if there is some luck or more importantly since we are dealing with half a season SSS issues.

It is certainly more likely than not you are correct, but that is a far cry from knowledge.

Which basically brings us to maybe the most important things we as stat geeks need to say when we try to educate people about these statistics. While stats are useful and have come a long way in showing what is and isn't valuable and to what degree, at best you can only get a really good sense of the player. You still need to watch the player to get a completely accurate picture. You also need to know stuff that no stat sheet can tell you like mechanical changes. The results may show up in the stats but without knowing the change occurred you can't factor it in when interpreting the stats.

That has always been one of things that the stat or more specifically Sabr-adverse crowd throw out. You're just stat people looking at box scores, blah, blah, blah. You have to watch the game, yada, yada, yada. It is really a red herring, but it's one that we need to put to bed or nothing can progress.
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mse326


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PostPosted: Fri Jul 18, 2014 12:04 am    Post subject: Reply with quote

biggio7 wrote:
green24 wrote:
biggio7 wrote:
So many numbers and math. Brick wall jk

Awesome stuff. I'm going to read through this tomorrow. I do have trouble with some of the sabermaterics stats. Which stat lines are more subjective than ones that are just all math and can actually be calculated? Does that question make sense? Sorry if it doesn't.

None are really subjective, but I think I get what you're saying. You want to know which are more imperfect and not just analyzing facts. The answer to that would be metrics like DRS and UZR.


That's what I was wondering cause I know there are some like that. I just wasn't sure which ones. I'm fine with the new analysis going on in baseball. I just hate when people do use the "stats" that are subjective in arguments.


As opposed to the eye test which is objective? Even the subjective stats will tell how they are calculated. For the most part it is the defensive ones, and that is unavoidable. There is no way to get an objective fielding stat. For the fielding stats basically they break the field into zones and through data assign values as to how tough the play is (that is really simplified though and I'm sure rams will go into it further when he does his defensive write up. The new prevalence of shifts are starting to screw with them, however, so they need to be used with a grain of salt.
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thelawoffices


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PostPosted: Fri Jul 18, 2014 1:53 am    Post subject: Reply with quote

All I got out of this post was that Garrett Richards is awesome.

Suck it, Mesa. Wink
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And she flips out like wtf I don't like that and I'm like you don't like rodents?

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redsoxsuck05


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PostPosted: Fri Jul 18, 2014 1:57 am    Post subject: Reply with quote

Good work, rams.

As I said, there should be a stickied thread with all of these links.
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PostPosted: Fri Jul 18, 2014 2:02 am    Post subject: Reply with quote

thelawoffices wrote:
All I got out of this post was that Garrett Richards is awesome.

Suck it, Mesa. Wink


Swing and a whiff on that one. Laughing

Tbf, though, I always did say that he had good stuff, he just finally figured out how to use it. That and his fastball used to be ridiculously flat. Now it moves like a Yu Darvish pitch somehow. Laughing

Anyways, good stuff, Rams. If you're doing any others I look forward to them. Hopefully everyone gives this a read through.

And Greenie, I was the one who posted Verlander's pitch frequency.
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ramssuperbowl99


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PostPosted: Fri Jul 18, 2014 7:28 am    Post subject: Reply with quote

mse326 wrote:
Quote:
But first things first, we need to ask ourselves, ‘is he pitching the same as he had previously?’ With Kershaw, the answer is an overwhelming, “No!” His pitch usage has changed, his slider is different than it was, and the results are bearing that out. Does that mean we should expect no regression? Probably not. After all, he’s been a big leaguer for a while now and had settled in pretty good with some fairly consistent numbers. But we know he’s better, so we need to take that into account.


I have a slight problem with this statement. We know that he is throwing pitches at a different rate and we know velocities. We know that his O-Swing% increased and the various contact% has decreased. But there is still an assumption as to why.

Throwing a slider more and harder does not mean it's better. In fact many times with breaking pitches throwing it harder makes it worse from a movement standpoint. So I wouldn't say we KNOW he's better. We know those stats are better, but we don't know if there is some luck or more importantly since we are dealing with half a season SSS issues.

It is certainly more likely than not you are correct, but that is a far cry from knowledge.
Good point.

I updated the statement to: "But we know that 2014 Kershaw hasn't pitched like 2013 or any other Kershaw so far this year, so we shouldn't just blindly regress him back to what he was."

You're right. It's half a season and while the results are certainly encouraging, it's probably too soon to just come out and say he's better. More importantly, it's not really relevant to the point I'm trying to make, because it doesn't matter whether the adjustments make Kershaw better or worse or don't change his ERA, what's important is that he's pitching differently. And that means we need to evaluate how we regress him differently.

If there's one thing I wanted to hammer home, it's that I want people to be able to think about how they are handling data and letting it drive the decision-making.

mse326 wrote:
Which basically brings us to maybe the most important things we as stat geeks need to say when we try to educate people about these statistics. While stats are useful and have come a long way in showing what is and isn't valuable and to what degree, at best you can only get a really good sense of the player. You still need to watch the player to get a completely accurate picture. You also need to know stuff that no stat sheet can tell you like mechanical changes. The results may show up in the stats but without knowing the change occurred you can't factor it in when interpreting the stats.

That has always been one of things that the stat or more specifically Sabr-adverse crowd throw out. You're just stat people looking at box scores, blah, blah, blah. You have to watch the game, yada, yada, yada. It is really a red herring, but it's one that we need to put to bed or nothing can progress.
Definitely. Bringing it back to Kershaw, when you watch him, it's clear that his command is better this year. He's getting into a lot of early 2-strike counts, and the increase in his slider usage is derived from that.

That's not necessarily something that pops out loud and clear from these stats. But if you watch him and you wonder 'is his command better?', you can start looking for patterns in things like F-Strike% to help validate what your eyes are seeing. The next step in something like that is starting to see if you can't glean info out of some interviews for even further corroboration - I probably find myself googling stuff like "Clayton Kershaw 2014 command slider" and seeing if there are quotes from him that are popping up fairly often.

Stat geeks and scouts need to understands that we're all on the same team. Good front offices are harmonizing what their scouts are telling them with the raw data they are gathering from the guys running pitch f/x, projection systems, and other models, and using that as the basis for decision-making. There's no reason we can't do the same thing.
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