I’ve run my defensive projections for 2011 using UZR as the primary input data source.  For a general feel for the methodology read this post.  A couple changes from last year

  1. Instead of using the average FSR for bins of players I’m using the FSR converted to runs as found on Fangraphs as my scouting component number.
  2. I took into account multiple positions this year.  I used the Fangraphs standard position adjustments to convert all UZR values from all positions to a player’s primary position.

As mentioned, this set of data is run with UZR as the primary data input.  Over the next few weeks/month I’ll be running the same process with DRS as an input, TZ as an input, and an average as an input.  I’ll post those files as they become available.

Click here for an excel download
Click here for the google docs version

Quick Edit:  Didn’t explicitly say this anywhere.  The numbers in the files are Runs/150 Games.

GOTW goes up a day early….

From my yet to be published defensive projections (this week probably)

But we’re supposed to be better this year defensively right?

We all know Brendan Ryan is good at defense.  The advanced metrics tell us that and our eyes agree.   Where does he really gain his value though?  Is it going up the middle?  In the hole?  Something else?  In an attempt to gain insight on that I created the following chart.  It has hit angle along the bottom and out rate along the vertical axis.  The far left of the chart (~ -30) can be thought of as deep in the hole, while 0 would be up the middle.

I used data from 2008-2010 where a right handed hitter was at the plate and Carpenter, Wainwright, Lohse, or Garcia were on the mound.  These choices allowed for a decent sample size, while trying to introduce as few other variables as possible.  My take away from the chart is that Ryan has been better at balls deep in the hole and at those right at him than the other players to play SS for the Cards during the time period.  The point where Ryan dips below the other guys is where he has made a relative high number of errors (6 on 77 BIP) where the other players have made less errors in that zone.  In total, Ryan’s overall out rate is about 5% higher than the other players to have played the position (to include Cesar Izturis who is no defensive slouch).

The next logical step (or maybe it should have been the first logical step) would be to take the above chart and add a line for what a league average shortstop looks like.  That’s how the advanced metrics behave (more or less).  We’ll do that next time.

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)

Name Pos UZR/150
Travis Ishikawa 1B 5.6
Chase Utley 2B 10.8
Omar Vizquel SS 9.3
Evan Longoria 3B 11.9
Carl Crawford LF 10.9
Franklin Gutierrez CF 12.2
Jayson Werth RF 11.2

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

Name Pos UZR/150
Jason Giambi 1B -5.6
Alberto Callaspo 2B -5.7
Yuniesky Betancourt SS -10.1
Edwin Encarnacion 3B -8.9
Adam Dunn LF -14.9
Vernon Wells CF -10.1
Brad Hawpe RF -19.1

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

Name Pos UZR/150
Brendan Ryan SS 7.2
Colby Rasmus CF 5.5
Albert Pujols 1B 5.0
Ryan Ludwick RF 1.0
Skip Schumaker 2B -5.1
Julio Lugo SS -5.9

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.

Inspired by all of the quality new graphs coming out of BtB, I thought I’d keep up my trend of putting some new charts out there.  There seems to be many graphs dealing with offense and overall value, but not many defense specific graphs.  That being said, here’s my feeble attempt at displaying two of the better defensive shortstops out there.

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