As far as NRI’s go, this one is worth a mini-fist pump, even if it’s at the price of looking like a nerd. Gotay is some decent F.A.T. Freely Available Talent, that is.

Gotay played 3B for the Diamondbacks’ Triple-A affiliate and hit .272/.429/.450. He had more walks (102!) than strikeouts (69) and hits (101). Gotay also showed some decent power — 42% of his hits went for extra bags. His MLE line was a nifty .258/.391/.402. He’s primarily played 2B in the majors; and is a -8 UZR per 150 games. For what it’s worth, he was a +1 as a 3B for Reno according to his Total Zone stats on MLSplits.

I adjusted his CHONE projection to St. Louis, it shakes out to a .363 OBA/.381 SLG. Not shabby at all.

I’m not saying he’s the savior at 3B, but he’s free extra depth and is David Freese’s age, the David Freese who is currently on the top of the 3B depth chart. At worst he makes Memphis a little more interesting.

Hope everyone enjoyed their bird and stuffing. Let’s take a look around the interwebs, catch up on a little news.

• David Appelman is a genius, and I’m not just kissing up because he lets me occasionally embarrass myself at FanGraphs. He’s now introduced Fan Projections. You can go in, pick your favorite team and enter your projections. This leads to more registered users for FanGraphs and better yet, fewer flaky voters. Smart. CHONE or ZiPS might not know Brendan Ryan might not know helpful information such as Brendan Ryan is going to start 85-90% of the games at SS next year. I take pride that my suspicion that Kyle Lohse’s arm is about to fall off is going to go into his projection.
• The handlebar mustachioed one is back, per the team’s official Twitter feed. Yawn. In his two seasons with the Cardinals, LaRue performance has been worth $3.5M according to FanGraph’s Win Values, and this assumes he’s been neutral on defense, which he probably has been. Maybe a smidge better than average. He’s earned around $2M. SMALL WIN!!!!111ONE!
• Some might see this as an indictment against Bryan Anderson. No, the verdict against Anderson came when Matt Pagnozzi moved ahead of him on the depth chart. But, Anderson is just 23 years old. The vultures are circling around Anderson’s prospect status; I’m not ready to bury him yet.
• I’m going to start wringing my hands about Albert’s contract extension yet. I am going to start to wring my hands a little if I keep hearing Miguel Tejada rumors. Keeping the commentary to a minimum: Batting average, Miguel Tejada: Overrated. Defense, Adrian Beltre: Underrated.
• One voter has done a 180 on his stance on Mark McGwire and the HoF. I really like Davidoff, but in pleading Mac’s case, I came away feeling a little worse about the BBWAA ever putting McQuiet in the Hall. Talk to us, Mark!

Hey all, Jeff Z found a few errors in my spreadsheet so I’ve fixed them and there is a new link on the sidebar (11-25 projections).  Sorry for the errors, it affected some outfielders (at least those were most notable).

Just wanted to put a couple of charts up that give a pretty decent view of Albert’s offense through the years.  First, his wOBA by components graph (note these were computed with each year’s coefficients)

That’s pretty consistent, with only one year that tickles .400 or below.  And for another look, the same chart from a percentage view

This gives a better view into the relative way he’s amassed his offensive production.  Wow he got a lot of his value from the HR in 2006…  anyway, just thought these graphs were interesting given the occasion of his 3rd MVP.

My plan is to get my simulation back out now that I have my defensive projections to input in there, so look for some comparisons of different potential roster constructions.  If you have any you’d specifically like to see drop them in the comments and I’ll see what I can do.

A few weeks ago I posted a set of defensive projections for SS based on regressing a 3 year average UZR to a population based on the Fan’s Scouting Report created by tangotiger.  After some discussion over  at The Book Blog, I altered my methodology a little and have come up with a set of projections for all positions.

First a quick discussion about the methodology.  The projected values are a weighted average of the

1. Players 3 year weighted UZR (5/4/3 style)
2. The UZR mean of the “scouting population” to which the player belongs (more on this in a minute)
3. The league average (i.e. 0).

The weights are

1. Effective defensive games over the three year sample (also weighted, so not just the sum)
2. 125 games
3. 125 games

which basically means the larger the 3 year sample, the less impact the “regressions” have, which falls under the basic premise of the more data you have the less you need to regress.

The scouting population is determined by where the player ranks in Tango’s Fans Scouting Report (FSR).  I took the last three years of FSR data and found the average UZR/150s for various bins of players (currently done by ordinal ranking, but will likely transition to binning by overall score once 2009 numbers are computed by Tango ).  I then crossed that data with were the specific player ranked in the 2009 voting, with that number becoming the scouting regressing factor.

For those that read my previous post on it, Method 2 was the methodology adopted (as MGL pointed out that it was the correct method).  Anyway on to the results.  First the leaders (with a minimum of 60 effective DGs)

You’ll note that the projections are for UZR/150 so you’d need to utilize an expected playing time to convert these to runs.  For example, I find it highly unlikely that Omar Vizquel will get enough playing time to save ~9 runs, but clearly if he played 75 DGs then he’d save ~4-5 runs.

Now for the laggards

For those that want to make the argument that Dunn won’t be playing left field again, second to last went to Delmon Young. For those making the same argument about Giambi, second to last there was Billy Butler. I’m posting the results spreadsheet on google docs with the link over on the sidebar, so feel free to download it and use it for whatever you want. The sheet contains the position the projection is for, the projection itself, 3 year UZR/150, and the effective DGs.

Finally, since this is a Cardinals blog, I wouldn’t leave you without giving you the key returning Cardinal players

A couple of final caveats about the projections. I know there are players missing, and there are definitely player/position combos missing. As a first pass I only projected the position that they had been identified with in the FSR. I plan to remedy that, but it’ll have to wait until the next iteration. Also, I didn’t apply an aging factor, which is clearly not a good way to go about projecting. In his BtB piece Jeff mentioned a -0.7 UZR, but I want to give some thought about how to apply that to UZR/150. Hopefully the next iteration will have some aging factor applied, up until then, apply whatever you see fit. Anyway, download away, and let me know if you have questions/problems.

A couple of weeks before I shut down my old site to move over here, I joined the United Cardinal Bloggers alliance.  One of their year end activities is the Cardinal Blogger Awards.  Instead of publishing my ballot over at my old site, I figure I’d publish it here as it also serves as a good year end wrap up post…  Of course these are only my views and Erik probably has his own opinion on these issues.

My votes after the jump

1. Cardinal Player of the Year

• Matt Holliday
• Albert Pujols
• Brendan Ryan
• Write-in: __________

Albert Pujols.  I’ll make my argument graphically since that’s my style.  Numbers are WAR via Fangraphs.

2. Cardinal Pitcher of the Year

• Chris Carpenter
• Ryan Franklin
• Adam Wainwright
• Write-in: __________

I struggled just as much as the Cy Young voters did in distinguishing between the two co-aces.  Carpenters rate stats were clearly superior, and AWs innings bridged that gap in terms of value.  In the end I went with the superior quality in choosing Chris Carpenter.  Yes innings matter, but WAR tries to capture that difference, and the two were very close (to the point of well within the measurement error margin) with most WAR metrics.

3. Game of the Year

• July 24 at Phillies
• September 6 at Brewers
• September 26 at Rockies
• Write-in: ____________

September 26.  Yay clinching.

4. Surprise Player of the Year

• Ryan Franklin
• Brendan Ryan
• Skip Schumaker
• Write-in: _____________

Brendan Ryan.  Who would have pegged him for over 3 WAR?  Who would have guessed he even would have garnered enough playing time to accumulate 3 WAR?

5. Disappointing Player of the Year

• Rick Ankiel
• Khalil Greene
• Kyle Lohse
• Write-in: _____________

This one’s tough as all three projected to contribute and none really did.  I went back and dug out my trusty 2009 CHONE projections and calculated some expected WARs.  Lohse projected to about 2.5 WAR (and actually accumulated 0.8).  Ankiel projected to about 1.7 WAR if you prorate his 2009 projection with his actual 2009 playing time (actual 0.1).  Greene projected to be a below average offensive player, the real disappointment was his D.  Doing some quick math, the gap between projected and actual is about the same for Lohse and Ank, so I’m going to go with Ankiel.  The tipping point was if he performs as expected maybe we still have Brett Wallace and Clayton Mortensen in the system.

6. Cardinal Rookie of the Year

• Mitchell Boggs
• Blake Hawksworth
• Colby Rasmus
• Write-in: ____________

Rasmus. 2.3 WAR (actually closer to 2.5 is you include baserunning) isn’t too shabby from a rookie.

7. Off-Season Acquisition of the Year

• Khalil Greene
• Trever Miller
• Dennys Reyes
• Write-in: ____________

Miller as he was the better of the two LOOGYs and both LOOGYs contributed more than Greene.  I will say it’s nice going into next year knowing we won’t have to throw 5-6 minor league FA LOOGYs up against the wall and see what falls out.  It’s also nice to know we’ll be paying both of our LOOOGYs together about the same the Cubs will be paying John Grabow.

8. Midseason Acquisition of the Year

• Mark DeRosa
• Matt Holliday
• John Smoltz
• Write-in: _________

Holliday.  See above chart.  2.7 WAR in his time with the birds, good for 4^th among Cardinals position players.

9. Most Anticipated Cardinal

• Allen Craig
• Daryl Jones
• Shelby Miller
• Write-in: __________

Shelby Miller.  Most highly touted pitching prospect since the “winner” of number 5 above.  Hopefully this one turns out a little better on the mound.

10. Best Individual Blog*

• Write-in: __________

Fungoes.  Clearly I’m a numbers guy, so Fungoes is right up my alley.

11. Best Team Blog^

• Write-in: __________

Viva El Birdos.  The standard.  Even with new faces rotating in, they still produce quality analysis day in day out.  Future Redbirds deserves a mention here on the prospect side of the house as well (and no I’m not just sucking up to my new partner in crime here).

12. Best Media Blog

• Bird Land
• Cardinal Beat
• Obviously, You’re Not a Golfer
• Write-in: __________

Bird Land.  D. Goold is the best the PDs got when it comes to Cardinals coverage.

13. Best UCB Project

• Progressive Game Blog
• Roundtables
• Top 7 Prospects
• Write-in: ____________

The roundtables.  I enjoyed reading the varying opinions and perspectives.

No real opinion on 14 or 15.

16. Rookie Blog of the Year

• The McBrayer-Baseball Blog
• St. Louis Sports Scene
• Write-in: ____________

Clearly I’ll abstain here, but would like to thank Dan for the nomination.  When the nominations were sent out, I had no idea I’d be moving over here to work with Erik.  Hopefully I can justify the nomination with the stuff I produce over here.

From the comments section at a blog post at The Hardball Times:

If he (Carpenter) had been on the ballots in place of Vazquez and Haren, and in the exact same placements as Vazquez and Haren, Carpenter would only have received four more points – and that would still have been two fewer than Lincecum.

In other words, if Law or Carroll put Carpenter on their ballots in either the two or three spot, Lincecum still wins. Carroll’s and Law’s votes, in the end, didn’t keep Carpenter from winning the Cy.

Not sure if passing on that piece of information will help Keith Law from getting stalked by angry Cardinal fans, but I’m just throwing it out there.

Another hat tip to Tango.

Nothing like a little hot stove stoking to get the "Carpenter, Wainwright were robbed" taste out of your mouth. Ken Davidoff give us his rankings and predictions on where the players will go and for how much they will sign for.

Relevant Cardinal info:

1. Matt Holliday, OF

The most impactful player available. Prediction: Cardinals, seven years, $120 million

7. Adrian Beltre, 3B

Poor offensive numbers distract from his excellent defense. Prediction: Cardinals, one year, $6 million

Davidoff can’t be expected to follow every beat of course, so we know it’ll be Holliday and Freese playing third, or a cheapy fix (Xavier Nady) in LF and a more established player like Adrian Beltre, I would presume at 3B.

Tip o’ cap to Tango.

Keith Law is awesome. He made a 2nd place vote for Javier Vazquez in the Cy Young, defended his decision with referring to sabermetrics (and innings pitched) and then ripped the fanbase, tweeting* " Do the Cardinals sell a pacifier with the team logo on it?" I like it.

*you can’t type the word tweeting and still feel cool.

The thing is, Law’s ballot is defensible, if you subscribe to these new-fangled sabermetrics, and more and more people are. I know, they are ruining the game.

Back in my day, we used to calculate a pitcher’s ERA with an abacus! And back in my day, we didn’t have safety standards for toys. We got rusty nails and big bags of broken glass! And that’s the way it was, and we liked it! We loved it! Win-Loss record and throwing underhanded! Those were the days, by-gum!

Look, folks. The nerd-leaven is slowly leavening the lump of the BBWAA. They’re penetrating the bureaucracy! Get used to it. Zack Greinke had 16 wins. Lincecum had 15. And yet both were far and away the best pitchers in their respective leagues, and it really wasn’t even that close.

Pitching Runs Created:

Lincecum – 137
Carpenter – 119
Wainwright – 126
Vazquez – 120
Haren – 121

FanGraphs‘ Wins Above Replacement, which uses FIP to determine runs above replacement:

Lincecum – 8.2
Carpenter – 5.6
Wainwright – 5.7
Vazquez – 6.6 (hello, Keith Law)
Haren – 6.1

StatCorner’s WAR, which uses tRA, which is FIP on steroids):

Lincecum – 8.3
Carpenter – 6.6
Wainwright – 5.7
Vazquez – 5.8
Haren – 6.3

One dude who was really, really, really good no matter how you slice it; and several pitchers who were very good, but had little separating them, no matter what advanced stats you look at. There is no holy grail stat, but you can see some harmony here.

This vote looks like progress, but the fact that there was still so much separating 2-3 from 4-5 when there was so little separating their performance tells me there’s still more progress to be made in the evil subterfuge of the basement dwellers into the MSM.

Whiny baby Cardinal fans and grumpy old man Cardinal fans, click on the links, educate yourself. You’re just making yourself look stupid attacking things you haven’t taken the time to understand.

Edit: Sorry, I’m a cranky cuss today. I am not saying there is a “wrong” vote per se’…well maybe I am. I just have grown a little weary of some of the anti-stat jabbing. Ignorance in this age of baseball information is forgivable, but making fun of things you don’t understand is just lame. But in the grand scheme of life, whoo-pa-dee-doo.

At the end of my last post I cautioned that the CHONE projections (and it applies to ALL projections) were a point solution (albeit the best guess at a point solution) and had to be thought of is concert with some error bars.  In an attempt to get my arms around the ramifications of that point I created a quick little monte carlo simulation in excel/vba to produce some distributions for offensive runs above average.  I’ll quickly outline the basic methodology used, to include my input set, and then I’ll present some initial results.

Since I knew I was going to be using CHONE projections I went back and collected the archived projections from 2009.  I did a quick comparison between the projected wOBAs and the actual wOBAs from this year for various levels of prior experience to get insight into what the standard deviation should be for the distributions that will feed the simulation.  The generalized results are in the table below

I ran that information through the simulation in combination with this years CHONE projections and found what I though to be a spread that was too wide (both on an aggregate team basis and an individual level).  I have very little to base this off of other than gut feel and combing back through the Fangraph archives of team totals from seasons past.

To address the variances on the individual level (which in turn addressed the aggregate solution) I went from using a normal distribution to a truncated normal by placing upper and lower bounds on the simulated wOBA (I implemented this using a re-draw method vice a rounding method).  The upper and lower bounds were influenced by reviewing the 2009 data and looking for caps that existed for various projected production levels (i.e. a projected 0.300 wOBA never produced beyond a certain actual wOBA). That still did not give results on the aggregate that passed the “smell test”, so I cut the standard deviations in half.  This last move was rather arbitrary on my part, and I plan to do some robustness testing along with analyzing a larger data set than just 2009.  That being said, the simulation was “done” and is at a state where I didn’t mind putting results out for all to see.

For today I have three scenarios to display reults from

1. Signing Matt Holliday and going with internal options at the rest of the positions
2. Going entirely with internal options (basically Freese at 3rd and Craig in left)
3. Same as 2 except downward adjusting the Freese and Craig Projections as they “feel” a bit high

After the jump I’ll have more input data and the results.

The following tables spell out the inputs exactly for all three cases.  First for 1.

Now the changes for 2 (all omitted data is the same)

and finally for 3

And now some visual results for the three runs. First a comparison of the with and without Holliday runs.

Clearly the distribution with Holliday is pushed to the right (more runs on average), and while it’s hidden the farthest left it goes is -35.  The “With” distribution is a little more narrow because Holliday takes at bats from players that have a higher s.d. (Craig, Mather and Jay).

And now for the comparison between different projections for the two young starters.

Making the two upcoming rookies league average hitters instead of their above average projection takes the offensive runs above average total being centered somewhere in the low 20s to being centered in the upper teens.  And finally, what is probably the most useful chart, the cumulative distribution function (CDF for you stat folks) for the three simulations.

Using these curves we can quickly calculate probabilities the team producing certain values of runs above average.  The greater gain of something like this will be when I incorporate the defensive projections and the pitching projections to come up with distributions and curves for the entire teams WAR.  Then I can throw those probabilities up against some of the work Nick has done to come up with a set of playoff odds before the season commences.  Anyway, I’m sure I’ll be breaking this out fairly frequently after I get the defensive projections done, and CHONE delivers the pitching projections.  Also, once I get it cleaned up a little I can make it available to anyone else that might want to take it for a spin.