SB Nation User Blog: vivaelpujols

SIGN JOHN SMOLTZ

vivaelpujols — Mon, 21 Dec 2009 07:14:02 -0000

SIGN JOHN SMOLTZ

A very nice little quick hit from Shawn Hoffman, a BPro writer. With Rich Harden picked up, Smoltz looks to be the best (and probably cheapest) option left on the market.

Testing Pythag

vivaelpujols — Thu, 10 Dec 2009 01:13:56 -0000

Testing Pythag

Brackenthebox takes a look at how many wins are gained by adding and subtracting 10 runs to teams with various levels of runs scored and runs allowed over at Viva El Birdos. His conclusion:

For good teams, however (ones that score more than they allow), a run saved is actually more valuable than a run scored (blue region). In contrast, for bad teams, a run saved is less valuable.

I've asked him to test PythagenPat also, to see if there is a difference.

Pujols vs. Wandy

vivaelpujols — Sun, 29 Nov 2009 06:01:39 -0000

More photos » by Morry Gash - AP

MVP!

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Wandy. Despite being fruity and somewhat hilarious when uttered (especially if say it like "w-and-y" , that name has brought much anger and exclamations of, "f'ing gdm!" to game threads around VEB. The primary reason for that is how much he has dominated Albert Pujols over his career. Using the invaluable Baseball Reference, we can see that Pujols is just 5 for 31 against Wandy, with 2 doubles, 6 walks and a .523 OPS. Interestingly enough, he's only struck out once.

Fortunately, a large portion of Pujols' plate appearances vs. Wandy have come in the past 3 years. That means we get a chance to explore the matchup more in depth using Pitch f/x data. Overall, there were 96 pitches with full pitch f/x information on them, which isn't much, but probably enough to see some interesting results.

First, let's look at how Wandy's been approaching Pujols:

Despite the small amount of pitches, we can see a pretty distinct pattern of how he's been attacking him. Lot's of curveballs down and in, fastballs on the outer half, and changeups down and away.

Now, let's check out what Albert's been doing with those pitches:

There are a few interesting things to notice from this chart. One is that he has not had a swinging strike against Wandy in the 97 pitches between them capture by Pitch f/x. Even with Pujols' amazing contact ability, that's very surprising given that Wandy is one of the better strikeout pitchers in the league. When you consider how much Pujols has struggled vs. Wandy, it's interesting that none of that is related to him being overpowered.

Another thing to notice is the inconsistency of the strike zone at the bottom end. There are at least 5 pitches called strikes that are in the same exact location as pitches that were called balls. That effect seems to be favoring Pujols actually, as he's gotten a lot of close pitches to go his way. Also, most of his outs are on fastballs on the high outer half. Unsurprisingly, most of those are fly ball or pop outs.

I would dig deeper, but there is really only so much you can do with 96 pitches. Really, the main reason Pujols has struggled against Wandy is that he hasn't been squaring him up much. He's only hit 9% line drives against Wandy compared to over 20% against all other players. That's manifested itself in very few hits, and thus lower production.

However, Pujols has still shown a great eye and contact ability, so there is no reason to think that Wandy really has his number. Expect El Hombre to hit much better against Wandy in the future.

How much does a pitcher gain from "pitching to his defense"

vivaelpujols — Tue, 24 Nov 2009 18:10:06 -0000

How much does a pitcher gain from "pitching to his defense"

In response to the argument made that Chris Carpenter, er... I mean, a hypothetical pitcher, was able to outperform his FIP so much by pitching to his defense, Kincaid, at 3d-Baseball looked at just how much is to be gained by adjusting your strategy to pitching to a good defense. Shockingly, the effect is very minimal.

The Cy Young stats

vivaelpujols — Sat, 21 Nov 2009 06:18:51 -0000

So the NL Cy Young award recipient was announced the other day, and surprisingly, it didn't go to the guy with the most wins, or the best ERA for that matter. Instead, it went to Tim Lincecum, who lead the league in FanGraphs WAR by a pretty big margin.

Naturally, given the two candidates that he narrowly beat, this sparked a lot of debate about whether or not FanGraphs' WAR was the best way to go. From what I gathered, a lot of people thought that timing should be taken into account, and in the case of Carpenter, "pitching to your defense". There were even some rogue mentions of wins and pitching to the score.

Unfortunately, I don't know the right stat to figure out the Cy Young award winner. That's because each person has a different interpretation of what it means to be the Cy Young. What I can do is lay out all of the major stats so that you can make the most informed decision about how you want the Cy Young to be decided.

FIP

As you may or may not have heard, FIP has had a lot of popularity around the blogs as a way to evaluate pitchers. The reason is likely for it's simplicity, which makes the stat easy to calculate and relatively void of "noise" (I'll expand on that later). FIP breaks down each at bat into 4 possible outcomes: a walk, a strikeout, a home run and a ball in play. FIP assumes that pitchers have 100% control over what happens after the first three (99% of the time, a walk, strikeout or home run results in that), and they they have 0% control about what happens after when a ball is put in play.

The league average hit rate for balls in play is .300, and FIP assumes that will be the case for all pitchers, and that is the controversy with FIP. It's clear that some pitchers have more control over batting average on balls in play (BABIP) than others. Guys who get a bunch of ground balls and pop ups will have a lower BABIP. However, BABIP is inlfuenced by luck and defense much more than actual skill, so a lot of people like to ignore it when evaluating pitchers.

FIP also takes timing out of the equation, because it uses aggregate stats. If one pitcher does much better with runners on base than another, but his overall stats are the same, FIP will rate the two equally. If I could describe FIP in one sentence, it would be... how many runs per 9 innings a pitcher would have given up given neutral timing, and assuming an average distribution of balls in play. That doesn't mean it's better or worse than any other stat. It is what it is. If you think that BABIP and timing are just luck (which they really are for the most part) and shouldn't be credited towards the pitcher, this is the stat for you.

xFIP

This stat is the same thing as FIP, expect it substitutes HR's for .11*FB's. The reason for this? Well, it's been shown that the rate of fly balls that go for home runs (HR/FB ratio), is largely out of a pitchers control. Like BABIP, there is some skill involved in HR/FB ratio; however, it is dwarfed by the amount of luck involved in it. So xFIP strips away all the skill and luck, and simply assumes that the pitcher has 0% control over HR/FB ratio.

tRA

This stat is like FIP on speed. In addition to considering walks, strikeouts and home runs - it also considers the quality of balls put in play, by using the 4 batted ball classifications: line drives, ground balls, outfield fly balls and pop ups. While on the surface, that seems like a good thing because it gives more credit to pitchers for their ability to control BABIP, it also adds a lot more noise due to the subjective nature of batted ball classifations.

Unlike strikeouts, walks and home runs, batted ball stats can vary based on the source of the data. There are 3 main sources of batted ball data: BIS, STATS and Gameday. The first two are available for pay, while Gameday is freely available to everyone. Each of those sources gets batted ball data from stringers, who are typically paid about 10 dollars per game to watch each batted ball and classify it. BIS and STATS used to have their stringers record the data at the ballparks; however, I believe that they all do it from the TV now.

Anyway, you can see how that would be a problem. A soft liner dropping just in front of Colby Rasmus could easily be classified as a fly ball or a line drive depending on the source. Given the huge difference in terms of value between a fly ball and a line drive in tRA, the batted ball classifications can have a big impact.

In fact, given that we have tRA avaiable on two websites, StatCorner and FanGraphs, that each use different batted ball sources, Gameday and BIS respectively, there are large discrepancies in the tRA numbers for the same pitcher. For example, her are the tRA's by Statcorner and FanGraphs for each of the 5 major Cy Young candidates in the NL this year:

	StatCorner tRA	FanGraphs tRA
Tim Lincecum	2.52	2.83
Javier Vazquez	3.19	3.67
Dan Haren	3.25	4.12
Chris Carpenter	2.77	3.02
A.D.A.M.	3.47	3.56

As you can see, the difference in tRA is huge for some pitchers just based on the source of the batted ball data. So while tRA adds a lot more usefull information about a pitcher, it also adds a lot of noise that can obfuscate the pitchers actual performance.

ERA

I think we all know about this one. This assumes that the pitcher has 100% control over everything that happens (except, for errors). While this is the final results of how effective the pitcher has been, it also includes way to much things that are out of a pitchers control, like defense, HR/FB ratio, BABIP and timing. So while FIP, xFIP and tRA perhaps takes out too many factors that the pitcher has some control over, ERA takes out way too few.

WAR

WAR is simply a way to combine production with innings pitched to get an estimate of how many wins a player contributes to an average team over a replacement level player. When I say 'production', mean the estimate of how good the pitcher was when he pitched. This can be estimated using any of the 4 metrics I outlined above: tRA, FIP, xFIP or ERA, which should be adjusted for park, and if you want, quality of batters faced. WAR turns that number into an expected W% using a PythagenPat run estimator, subtracts that by the expected W% of a replacement level pitcher, and multiplies that by innings per 9.

You can see more about how WAR is calculated here:

http://www.insidethebook.com/ee/index.php/site/comments/how_to_calculate_war/

For those who care, the closed form equation for figuring out WAR is:

WAR =((((B/A)^((A+B)^0.275))/(((B/A)^((A+B)^0.275))+1))-0.39)*C/9

Where:

A = the pitcher's run or earned run average using one of the estimates above

B = the leaugue run or earned run average

C = innings pitched

So when you hear people say WAR, don't assume that they are talking about the FanGraphs version of it. WAR can be calculated many different ways, using different park factors, run estimators, defensive adjustment and quality of opponent adjustments. In my opinion, a well calibrated WAR calculation is the best possible way to combine production and playing time to get an estimate of a players value above his potential replacement. The only thing it doesn't take into account is "pitching to the score". If you believe that certain pitchers have control over when they give up their runs, and pitch given the game context, you might not like WAR so much.

WPA

WPA has a pretty simple defition. It measures total improvement in the odds of winning a game for each player. By that I mean, it measures how each player contributes to an average teams odds of winning the game, and sums up the results for each player. So for example, based off of empirical data, you can see that with 2 outs in the bottom of the 9th, with a runner on first base and the home team down by a run, an average team will win roughly 8.7% of the time.

Albert Pujols' big badass self walks to the plate and promptly hits a 484 foot home run. Now, in the bottom of the 9th with 2 outs and the home team up by a run, the home team surprisingly wins the game 100% of the time. So Pujols' WPA will be .913, or he improved the odds of his team winning the game by 91.3%.

The concept of WPA sounds great, it rewards each event based on the value it has depending on the context of each game. Even if you don't believe that situational hitting is a skill, you have to admit that it has value. However, there are numerous problems with WPA when it comes to evaluating performance:

1) It doesn't include adjustments for park

2) It gives full credit to pitchers for the contributions of their defenders

3) It doesn't give credit to pitchers for innings pitched

That last point is often overlooked when talking about WPA. WPA is the change in win probability for an average team. So if you assume that each player is taking the spot of a replacement level player, you have to include that adjustment in WPA. So a guy who puts up a 5.00 WPA in 50 innings isn't necessarily more valuable than a guy who puts up a 2.00 WPA in 200 innings, because his replacement in those 150 innings will have a negative WPA.

Wins

These are like WPA, except it also gives the pitcher full credit for how many runs his team scores, how long his manager let's him stay in the game and how well the relievers that pitcher after him perform. Really, there is nothing that wins add to the discussion, and they add a whole bunch of factors that obscure the pitchers actual performance.

Summing it up

Hopefully, I've presented the pro's and con's of each of these stats in an easy to understand way. If you've followed along, you'll notice that each of these stats has a certain give and take to them. I can't stress this enough. The simplest stats like FIP and xFIP don't tell as much about a pitchers performance as stats like tRA and WPA, however, they also are a lot cleaner and don't tell you a lot about stuff that is out of the pitchers control. The more information you add, the more noise and superfluous and obfuscating data you add.

It's up to you to choose the best possible combination based on your preferences of how we should value a pitchers performance. What you can't do, is pick who you want to win the award first, and then choose the stat - that's just being a dick.

Poll

Which stat is the best for picking the Cy Young?

FIP WAR

77 votes

xFIP WAR

50 votes

tRA WAR

82 votes

ERA WAR

146 votes

WPA

21 votes

Wins

119 votes

495 votes | Poll has closed

2010 Hot Stove Post #1: CHONE hitter projections

vivaelpujols — Sun, 15 Nov 2009 21:29:09 -0000

The CHONE projection system, created by Sean Smith of THT, FanGraphs and Anaheim Angels all the Way, we're recently released to the public. They are generally considered the best system out there, and has been the most accurate since 2005 in terms of projection team performance.

You can go to his website Baseball Projection, to view the projections for each player (it's organized by team):

http://www.baseballprojection.com/

Here is the link for the Cardinals projections. The key number to look at is R/150, which is how many runs above average each player projects to be per 150 games.

Pujols looks amazing of course, and Ludwick's is not bad either. Allen Craig actually projects to be the third best hitter, but he's right about average along with Rasmus, Freese, Mather, Skip and Yadi.

You can also view a list of all of the free agents here. Matt Holliday projects to be the best hitter, at +27 runs, which isn't surprising, with Jason Bay right behind him. Mike Cameron projects to be surprisingly low, at -4 runs. Johnny Damon actually projects pretty well though.

This will be the first official Hot Stove Post for 2010, and at the risk of sounding authoritative, it would nice if you guys put all your offseason thoughts in this post, so we don't have like 10 different FanPosts about the same thing. I will update this thread once the pitcher and defensive projections are released.

H/T, the fantastic Tommy Bennent.

The good and the bad of Ryan Franklin

vivaelpujols — Sun, 01 Nov 2009 06:11:50 -0000

More photos » by Mark J. Terrill - AP

Mark Loretta? REALLY?!?!?!?!!!!!

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This year Ryan Franklin was an enigma. Thanks to a nifty .269 BABIP (compared to a league average of .300) and an 85.7 strand rate (compared to a league average of 70%), his production was far better than you would expect based off of his peripheral statistics. His ERA was excellent at 1.92; however, his FIP was a good, not great, 3.31. And due to a super low HR/FB ratio, his xFIP was even higher, at 4.23. Simply put, his career year wasn't so great when you adjust for luck.

What makes that worse is that he seemingly fell apart down the stretch. His low BB rate rose, his low K rate plummeted and his low BABIP got a little bit less low. During that time, he had regressed the pitcher he once was. His command was worse, his stuff was not biting as much as it usually did and his goatee had finally completely taken over his face. Obviously, that's what happened... or was it?

I have a theory that Franklin actually didn't pitch much differently than he usually did, it's just his luck and timing were different. I'll be honest, this is part of a larger study I'm working on, so I'm using you guys as guinea pigs, but I'll think you'll enjoy it nonetheless

What I did was take all of Franklin's appearances this year (65 including post season and all star game), and sort by wOBA allowed. I took the 15 worst outings and the 15 best outings from him all year, at least as measured by wOBA, and pulled the aggregate PItch f/x data for each group. I then took the two groups and looked at several attributes of the pitches, to see if I could spot any differences. For fun, when I show you the graphs and data, I won't label them, so you could try to figure out which is which.

First, let's look at pitch movement, organized by vertical vs. horizontal spin deflection:

For those who don't know, each dot here is being compared to a theoretical pitch without spin. It's from the catchers point of view, so from a RHP, fastballs will have negative break (towards the third base side), while breaker balls have positive break (towards the first base side).

Based on his stuff (or at least his movement) can you tell which one was the bad Franklin and which one was the good? Well, you're guess is as good as mine (actually I know the answer because I'm just awesome like that). As far as stuff and pitch selection goes, there is not much separation between the two groups. The only noticeable differences is that he threw more changeups in group 2 and his cutters had a bit more drop in group 1.

Now, let's take a look at how he located the ball. First, we'll check out his pitch location to lefties, grouped by pitch type:

The images are a little bit small, but you can click them for a larger image. Against left handers in group 1 he threw a lot of pitchers on the out half of the plate, as well as lot's of offspeed pitches just below the plate. He also rarely went inside on the batter. Group 2 on the other hand, looks a little more spattered, although he was able to locate the cutter on the inner half of the strike zone more consistently than in Group 1.

Now let's check his location to righties:

This time group 2 looks a little bit better, at least in my opinion. There are more cutters on the inner half, and more offspeed pitches down. Although, the distributions are very similar indeed.

So what do you guys think? If you would like more non-performance information, just ask me in the comments section and I'll be happy to provide it for you. I've set up a poll. The guys who guess right will be given 5 internet dollars, while the guys who guess wrong will be killed... sorry, that's just how it works around these parts.

At any rate, my conjecture seems to have been validated. Based on those two pieces of non-performance information, stuff and location, it's very hard to tell which is the good and the bad Franklin. This makes me very skeptical of managers or coaches who profess to be able to "identify" the problem with a certain player when he is in a slump. Obviously, in some cases when an injury or something else is present, you can tell based off of how he looked (I did exactly that with Lohse a while back). However, in most slumps, random variation and luck are the most likely causes.

Poll

Which one was the bad Franklin?

Group 1

72 votes

Group 2

147 votes

http://images.dailyradar.com/media/uploads/ballhype/story_preview/2009/05/12/ryan_franklin_s_chin_happy_to_be_named_closer.jpg

29 votes

248 votes | Poll has closed

JC Bradbury on "Hot Stove Myths"

vivaelpujols — Fri, 13 Nov 2009 06:02:46 -0000

JC Bradbury on "Hot Stove Myths"

I'm not sure what the make of this. I'm pretty sure it's wrong, though.

More discussion at Tango's blog.

Links ahoy

vivaelpujols — Sat, 07 Nov 2009 12:05:04 -0000

More photos » by Morry Gash - AP

Brendan Ryan: the 2009 Cardinals honorary hacker of the year.

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I just saw "Men who Stare at Goats" last night. Weirdest movie ever. I laughed my ass off throughout the thing, but damned if I knew what it was actually about. Accordingly, I got home late last night, so I don't have time for a full post, but I have a couple of things that I wanted to link to that I thought you'd all enjoy.

We'll start off with a very cool modification of WAR from Alex Krolewski at Beyond the Boxscore, called PZR Win Values. Before you read that, let me give you a brief history of what WAR actually is. The AR part of WAR stands for Above Replacement, and generally represents some minor league scrub who each team has. Think guys like Brad Thompson or Blake Hawksworth. You would expect such a player to put up an ERA of over 5 if he was in the rotation.

The W part stands for Wins, and it represents how much production over that replacement level player you can expect from a given player. Wins are calculated mathematically (you can read the
gory math details here), and are devoid of context (pitching to the score doesn't get you any cookie points). They can be calculated using a plehtora of run estimators:

ERA... you should probably know this one.

FIP... attempts to take out some of the aspects of ERA that are much less under a pitchers control, such as defense and timing. It relies on home runs, walks and strikeouts to rate pitchers. Despite it's simplicity, or maybe because of it, it is the most used defense independent pitching stat out there. Kincaid, over at 3-D Baseball recently wrote a two part series further explaining how it works, that I encourage you all to read.

tRA... is like FIP on crack, in a good way. It includes the basic elements of FIP, but also adds a pitchers batted ball numbers to give them more credit for inducing weaker contact.

There are others, but those are the main three. You can see problems with each of them however. ERA is too reliant on things that are mainly out of a pitchers control, like defense, and thus doesn't measure pitcher skill very well. FIP and tRA eliminate defense, but also timing, which can be a problem. A pitcher who gives up a walk then a home run, is just as valuable by these estimates as a pitcher who gives up a home run followed by a walk.

Enter PZR. PZR is a modification of of UZR, which is a stat developed by Mitchel Lichtman, a former consultant to the St. Louis Cardinals, to credit fielders with their defensive contributions on balls in play. UZR takes many, many parameters into account including how hard the ball is hit, the batted ball tendencies of the pitcher on the mound, the location of each batted ball and many others. You can read more about it here and here.

Anyway, PZR takes UZR inputs and applies them to pitchers instead. So instead of measuring feilding prowess, it measures how hard to field each pitches balls in play are. Once you get those numbers, you can adjust each pitchers ERA accordingly. That makes an adjustment for defense, while not taking out the timing aspect of pitching. If you use PZR modified ERA to calculate WAR, you get some very interesting results, as Alex demonstrated in the link above.

If anyone has any questions about PZR or WAR, speak up, and I'm sure we'll be able to help.

Next, the Pittsburgh Pirates (I know, don't laugh, they're not that bad) are looking for a programmer/analyst to help their front office with front office stuff. If you have skills in SQL and other computery stuff, you should check out this link and see if you are interested in the job. Also, it's probably good if you live in Pittsburgh.

---------------------------

Next up, at the Hardball Times, Max Marchi looks at the best and worst bad ball hitters and pitchers in baseball. Using Pitch f/x data, he looked at which hitters swung at the highest percentage of pitches seen in the bad ball zone, and then which are most and least successful at hitting those pitches when they swing at them. He repeats the study for pitchers as well.

Suprisingly, no Cardinals show up in any of the leader boards, but using a variation of Max's method, I present to you the most hackiest Cardinals this year. I say variation, because I wasn't able to reproduce the code he wrote that figured out the bad ball zone based off of where pitches are called strikes. Instead, I simply looked at pitches that were at least a half a foot off the strike zone for each batter hand. Anyway, here are the Cardinal leaders this year, with a minimum of 200 pitches seen in the bad ball zone:

Name	Pitches seen in bad ball zone	Bad ball swinging %
Brendan Ryan	230	15.7%
Rick Ankiel	256	14.5%
Colby Rasmus	319	13.2%
Skip Schumaker	359	11.4%
Matt Holliday	648	10.7%

*with Oakland and Stl.

League average is around 11%, so the Cardinals don't seem to be so bad. Unfortunately, Pitch f/x data doesn't include which team each player is on, so there is no way to definitely check out which teams are the best and the worst. Here are the full leaderboards, bearing in mind that my method is extremely crude.

Addendum:

I discovered a small error in my query that screwed everything up for lefties, so you'll see that the leaderboards and the spreadsheet have been updated with the correct numbers. In case you missed it, I had Ankiel only swinging at 2 bad balls all year. Just for fun, here is Ankiel's swing plot this year:

Yeah....

He should really get someone to help him with those low pitches.

Goddamnit.

vivaelpujols — Mon, 02 Nov 2009 13:51:14 -0000

Goddamnit.