Took a quick look at the distribution of where Cards players are hitting the ball.  The following table summarizes the last 2 years

Left Center Right
AP 2010 48% 39% 13%
AP 2009 50% 34% 16%
RL 2010 42% 36% 22%
RL 2009 40% 34% 26%
SS 2010 24% 43% 33%
SS 2009 31% 38% 31%
MH 2010 29% 38% 33%
MH 2009 39% 32% 29%
CR 2010 27% 30% 43%
CR 2009 27% 32% 41%
YM 2010 34% 37% 29%
YM 2009 32% 37% 32%
BR 2010 33% 43% 24%
BR 2009 34% 38% 27%

The data is taken from the splits pages at Fangraphs. The only thing that really jumps out at me is Holliday pulling the ball much less than last year. As long as the shift is from pulling to up the middle he’ll likely be ok as his career wOBA is best to center. That being said additional balls to right probably won’t help as his wOBA to right is 0.040-0.050 points worse than to either left or center.

With two games already won, here are the updated series outcome probabilities

Have any of you out there transitioned a free wordpress blog (like this one) to your own domain?  If so, how’d that process go?  Did it affect your readership at all?  I ask because I’ve been thinking about moving over to our own domain so that I can make some of the cooler toys I develop (like the series sim) downloadable, or even a web-app.

Hey all, be sure to swing by The Book Blog and fill out Tango’s playing time survey.

Update:

Here is the link to the actual survey.

…Yawn.

There’s always talk in Cardinal nation about the Dave Duncan philosophy of pitching, and it was highlighted even more this year when Ks were prominent in the Cy Young vote.  I wanted to take a graphical look at the Cardinals as a pitching staff and see how the philosophy has manifested itself in the numbers.  The following graph has the NL rank in GB/FB ratio and K/Batters Faced rank for the Cardinals over the last 5 years

Clearly the Cards have almost always been in the top of the league in getting GBs (relative to FBs anyway), and in the bottom in getting Ks.  Now has there been any correlation with team success (warning small sample size alert!!!)

Over the last 5 years the rank in win percentage has tracked fairly well with the rank in GB/FB.  I haven’t done anything scientific here, so I can’t really draw any scientific conclusions.  That being said I find the graphs pretty interesting.

Earlier I looked at how Kyle McClellan might fare as a starting pitcher. I’m still not sure the answer is crystal clear. We’ve established his has the arsenal through looking at his Pitch F/x numbers. Translating his numbers from relief to starting, I think he’d range somewhere in the 4.4-4.8 FIP range. Per 150 innings – which I feel is a reasonable estimate; figure 30 starts, an average of 5 innings per – that comes out to 1 to 1.6 WAR. So it seems like a worthwhile endeavor.

The big question of course would be about his endurance. Mac underwent Tommy John surgery in 2005 and has only started four games since then, all of which were in the minors. The most innings McClellan has ever thrown is 128, all the way back in 2004. So I definitely understand the concern, it all boils down to conditioning. The Cardinals did have success with prepping Looper to handle an increased workload, so it can be done.

A reader brought up the issue of McClellan fading as the season went on. Sure seemed right to me, as far as I could recall, so I decided to look it up. Here’s a graph -

This is both his ’08-’09 seasons combined. July is when he seems to hit a real rough patch, but he comes back strong. As far as his stamina goes, I don’t think his month-by-month numbers prove he can’t hack it, per se. He’s reportedly been working very hard to get in the proper condition. He’s years removed from any major surgery, and his mechanics seem to be mostly clean, although I’m not the expert in the field by any means. I think there’s only one real way to tell, and that’s by finding out.

Jeremy at Baseball Analysts gives us hitter’s performance on swings based on where in the zone the pitch ended up.  Here are the key Cardinals performance on pitches “down the middle”.  The last column is runs per 100 swings.

Name Runs Swings Run/100
Albert Pujols 21.8 262 8.3
Skip Schumaker 17.3 240 7.2
Matt Holliday 15.4 282 5.5
Ryan Ludwick 2.9 298 1.0
Brendan Ryan 2.5 219 1.1
Colby Rasmus 0.6 233 0.3
Yadier Molina -8.7 290 -3.0

I’d think a good place to start would be to get the guys below Holliday to do better on hittable pitches.

It’s time to look at another “too-early” projected NL Central standings, this time we’re looking at CAIRO, named after the lovable utility infielder you and I are all too familiar with. While more changes are going to be made before now and the season, CAIRO was the best crystal ball when it came to picking the standings last season.

TM W L RS RA Div WC W+/- RS+/- RA+/-
Cardinals 92 70 762 676 42.50% 9.70% 1.1 32 36
Reds 86 77 755 721 22.20% 9.00% 7.5 82 -2
Cubs 84 78 749 726 17.30% 9.20% 0.7 42 54
Brewers 81 81 762 769 13.70% 7.50% 1.3 -23 -49
Pirates 70 92 730 839 1.90% 1.70% 8.2 94 71
Astros 69 93 663 771 2.50% 2.30% -4.9 20 1

W: Projected 2010 wins
L: Projected 2010 losses
RS: Projected 2010 runs scored
RA: Projected 2010 runs allowed
Div: Division win percentage
WC: Wild card win percentage
W+/-: 2010 projected wins minus 2009 actual wins
RS+/-: 2010 projected runs scored minus 2009 actual runs scored (positive means they are projected to score more)
RA+/-: 2010 projected runs allowed minus 2009 actual runs allowed (negative means they are projected to allow fewer)

Beware the Red Menace? I don’t have access to CAIRO’s individual projections, but we can make some educated guesses about why it projects the Reds to improve by 82 runs on offense next year.

  • Joey Votto will be awesome again.
  • Jay Bruce should improve. He was a -2 offensively last year, CHONE projects him for a +18 in 2010.
  • Willy Taveras was 27 runs below average last year with the bat, most of his playing time will be given to Drew Stubbs, who can’t possibly be worse.
  • Chris Dickerson should get most of the playing time in LF, and not Laynce Nix.
  • Scott Rolen is a nice improvement over Edwin Encarnacion.

Well shut my mouth. Neyer’s got a take on this as well.

As for the Cardinals, the offense will be better with Holliday (duh), but the pitching will likely regress to the mean.  Yep, it’s just hard to improve upon a season where your 1-2 starters finish 2nd and 3rd in the Cy Young voting. And Penny in 2010 isn’t likely to match Pineiro’s 2009, but neither is Pineiro for that matter. Sorry, I’m telling you stuff you already know.

As for the Cubs, they’re hanging in there. Soriano and Soto can’t possibly be worse, Ramirez will be healthy, Byrd in 2010 ought to be better than Bradley was in ’09. On the other hand, Randy Wells should fall back to earth some, and Rich Harden’s spot in the rotation is going to be replaced by Tom Gorzellany or *gasp* Carlos Silva.

Barring a disaster, this season *should be* a walk in the park for the Cardinals.

Over at BtB I did a post on clustering (grouping for the non-math inclined) hitters based on various stats (typically batted ball information combined with discipline information). I ended up discussing the clusters that resulted from using line drive rate (LD%), home run per fly ball (HR/FB%), and walk rate (BB%). The following is a table that summarizes the qualifying Cardinals

Name LD% HR/FB% BB% Cluster wOBA Cluster wOBA
Albert Pujols 16% 20% 17% 7 0.449 0.398
Matt Holliday 16% 13% 11% 6 0.390 0.363
Ryan Ludwick 19% 12% 8% 5 0.336 0.362
Colby Rasmus 20% 9% 7% 3 0.311 0.330
Skip Schumaker 22% 5% 9% 9 0.336 0.343
Yadier Molina 20% 5% 9% 9 0.337 0.343

So what does this mean? Basically if you average across people with similar LD%, HR/FB%, and BB% to the players listed you get the wOBA in the last column (click here to see the entire sets of clusters based off of various stat sets). Is this predictive? Are those that are below the cluster due for an improvement, while those above due for regression? As Tango points out, not necessarily. There’s clearly bias due to the data sets used to generate the clusters, so there’d have to be more work done on finding the correct data elements to include in the cluster. With that being said, it’s still interesting to see what types of players are clustered together.

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On an unrelated note, tomorrow I’ll be attending the St. Louis Chapter of SABR’s hot stove luncheon to include sitting on a panel that discusses Cardinal blogging. I’ll be sure to report back with any interesting tidbits from the day.

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Final unrelated note. This website amuses me.

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