Before the season, CHONE projected the Cardinals were on course to win 91 games and enjoy a cake walk in a relatively easy division.  Someone will run off with this and say, “see, this is why projections are worthless”.  But before you go off an anti-metrics rant, let’s remember what projections are. In a nutshell, among other things, projections  use regression to the mean, age adjustments and weighted averages to derive their results. Projections are “50th percentile” projections; there are always players who are exceptions to the rule due to good or bad luck, or some sort of overhaul to their swing mechanics (hello, Jose Bautista!) or injury. It’s not divination.

It’s common for a team to have several players under or overshoot their projection. It’s just that in the Cardinals case, the under achievers have been particularly damning to the team. For this post, let’s just take a look at the hitters. The cut off is 100 plate appearances. I took the players individual CHONE projections and then adjusted them for their plate appearances and not their projected PA’s. Then I did a little color scaling for your eyes, because I’m a nerd like that.

  • The infield trio of death of Brendan Ryan, Skip Schumaker and Felipe Lopez has really hurt this team. All three were not expected to be some sort of offensive force behind Pujols-Holliday-Rasmus, but neither were they expected to hit for sub-.300 wOBAs, either. (Schumaker is at .301, being fair). I thought Flip was a good Freese-insurance signing, and instead he pooped the bed. Ryan and Schumaker picked a bad time to do the same thing.
  • Molina and Pujols also both were one WAR apiece worse than projected. Molina’s bat backslid after two good seasons, and Pujols has been a little off from his normal numinous standard. And yet he’s still an MVP candidate.
  • The sheer waste of roster space of the likes of Feliz, Winn, Miles and Stavinoha has been pretty frustrating to watch.
  • The most pleasant surprise has been Matt Holliday, who is doing his best to show that he earned the ginormous contract he got this past winter.

All told, the Cardinal’s hitters are five wins worse than we would have expected. Combine that with losing Brad Penny for the year, and Kyle Lohse being hurt and then horrible, you have a recipe for a pretty disappointing year. It’s really not that hard to figure out.

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

Experience SD
None/Low 0.038
Med 0.030
A Lot 0.025

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.

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