4 posts in 2 days. Look at PAH9 go. The following post is indirectly related to my Brendan Ryan post. You also should check out Andy’s pet peeve and my top 7 Cardinal prospects.

One of the common criticisms (especially among Cardinal fans) of DIPS pitching stats is that all batted balls are not created equal. Specifically, all GBs are not equal, all FBs are not equal etc. With that thought in mind I wanted to compare a couple of Cardinal pitchers to see if there was a discernable difference in their GBs. To level the playing field I only looked at RHB when Brendan Ryan was playing. I looked at Out+Error rate, making the assumption that the pitcher had no control over the error part. The following table summarizes the results across all hit angles (GBs only)

Out+Error Rate BIP
Carpenter 0.820 311
Wainwright 0.799 328
Lohse 0.731 186

And then across the SS area of responsibility (since that was who we held constant)

Out+Error Rate BIP
Carpenter 0.870 154
Wainwright 0.887 151
Lohse 0.818 88

And in chart form

So what can we attribute the differences to?

Ground ball quality – I would guess that harder hit ground balls would be more likely to make it up the middle (-7.5 in the chart) and through the hole (-27.5). The anecdotal evidence in the data above seems to agree. I’d guess that Lohse gives up the hardest hit balls of the 3.

Defense – Yes Brendan Ryan was in the field, but that isn’t to say that he played identically (both in reaction time and positioning) behind all 3 of these guys. As samples increase this effect would likely decrease.

Park effects – Infield speed isn’t constant across all parks (can adjust for these, but haven’t)

Stringer/Scorer Bias – Are these all groundballs? What is the difference between GBs and LDs? Is the hit location recorded accurately? Is there Hit/Error bias?

Luck – Bad hops, deflections etc.

The real question is what weight you put on each of those factors. I’m not sure we’ll get at the answer to that until we get Field F/X data (if we get field f/x data). For now I’d hesitate to weight the first one (which is what would be ideal to measure) as any more than 50% of the difference. There’s just too much other stuff that could be at play.

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.

Here’s a graphical depiction of how the Cards defenders have fared in UZR so far:

However MGL would slap me if he knew I just posted ~60 games worth of UZR, so here’s a chart that contains a back of the napkin redo of my preseason D projections accounting for this season’s info.

The x axis is in UZR/150; so basically true talent over ~ one season’s worth of games.

Looks like we’re not so certain that Colby is a plus defender after all.

Rally rocks. He recently incorporated defensive projections at BaseballProjection.com.

Here’s the Cardinals, sorted from best to worst, including some prospects of varying hotness.

Name POS  Runs
Ryan, Brendan SS 15
DeJesus, Antonio OF 11
Jay, Jonathan OF 10
Molina, Yadier C 9
Robinson, Shane OF 9
Pujols, Albert 1B 8
Rasmus, Colby CF 6
Jones, Daryl OF 4
Henley, Tyler OF 2
Rapoport, James CF 1
Luna, Aaron OF 1
Freese, David 3B 0
Brito, Javier DH 0
Craig, Allen OF 0
Thurston, Joe 3B -1
Ludwick, Ryan OF -1
Mather, Joe OF -1
Gotay, Ruben 3B -2
Shorey, Mark OF -2
Hamilton, Mark 1B -3
Buckman, Brandon 1B -3
Arburr, Matthew 1B -3
Stavinoha, Nick OF -3
Solano, Donovan 3B -4
Kozma, Peter SS -4
Brown, Andrew 1B -5
Descalso, Daniel 2B -5
Greene, Tyler SS -5
Schumaker, Skip 2B -6
Lugo, Julio 2B -6
Folli, Mike 3B -6
Pagnozzi, Matt C -6
Cruz, Tony C -7
Derba, Nick C -7
Sedbrook, Colt 2B -10
Rowlett, Casey 2B -10
Hoffpauir, Jarrett 2B -11
Anderson, Bryan C -12
Hill, Steven C -14

It’s optimism, but I’m all for optimism. Rally’s projections have Boog as the NL’s best defensive shortstop, trailed by Tulowitzki and Yunel Escobar.

I don’t know how Sean translates the numbers for catcher exactly, but this looks like another blow to what’s left of Bryan Anderson’s prospect status. And yet Pagnozzi isn’t as stellar as advertised, either. Maybe he’s just popular with pitchers, I don’t know.

Jon Jay, Chief Justice of the Outfield. He’s currently hitting .323/.418/.431 in the Venezuelan Winter League, whatever that might mean. A plus 10 in the corners means he could would be average in center field, which is good. I expect him to be a nice little 4th outfielder next season. Robinson and DeJesus both have the glove to play center, but their bat keeps them from being much more than replacement level.

There’s a couple of different defensive projections that are currently available for all to see.  You’ve got mine linked over on the right sidebar, and Jeff Z’s available through this link.  The beauty of the two sets of projections are that the respective methodologies are discussed in the articles presenting them, and the projections are fairly simple to compare (i.e. only one number really).  A second positive is that the methodologies only differ by one element, the FSR, as Jeff includes 4 yrs UZR (when available) and I include 3 yrs + the fans.  Since that’s the only difference, it makes drawing some insights/conclusions from analyzing the differences a little simpler, and that’s exactly what I’m going to step through here.  Jeff is doing the same over at BtB, so go check out his piece as well.

First, I’d like to get a feel for just how different the two projections were.  For that a simple distribution should do the trick.  The absolute difference is across the x axis and the count is on the y.

Clearly the majority of the differences are less that 4 runs and over half has a difference of 0 or 1.  Given that there are differences though, what positions are the differences coming from.  In this chart absolute difference is again across the x, but now percent (by position) is on the y.

At first glance it seems like the outfield becomes more prevalent the farther right you go…

So now that we have a decent idea of the magnitude of the differences, it’s time to dig into where the actual differences are.  Who is affected by adding in the FSR as a factor?  I’ll answer that question by examining two parameters: 1)Experience of the player and 2)Position / FSR rank combination.  This first table highlights the experience piece

Game Bin AVG ABS Diff STD DEV Count
<50 1.85 1.14 41
50-100 1.88 1.45 64
100-150 1.90 1.45 53
150-200 1.54 1.30 30
200-300 1.38 1.11 81
300-400 1.26 0.85 46

As one would expect the less experience the player has the bigger the difference between the two projections.  The FSR number are a larger percentage of the puzzle for less experienced players as I weighted it at 125 games no matter what the experience level of the player was.  [Update: I used my effective defensive games to bin the games, not actual games as Jeff did in his analysis]

Finally, which position / FSR rank combos gained the most by inclusion of the FSR

and lost the most

All told  it appears that the FSR does make a difference, but it’s usually only on the order of a couple of runs, which is well within the margin of error for UZR.  It has the potential to clear up the picture for players with limited major league experience, as it makes the “available data set” larger, so there is less regression to the mean.

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.

Hiatus over. I’ll be writing about the Cardinals here when I’m feeling the urge, and in pithy fashion.

Jeff Zimmerman at Beyond the Boxscore put together 2010 UZR projections for the spreadsheet mafia. Sweeeet. For those of you who like to see the math, you can go here to learn more.  We’ll just stick with the projections for our favorite local sports team.

STL

Albert roolz. He is projected to be the best defensive 1B, well ahead of the incumbent Gold Glover Adrian Gonzalez, for what it’s worth. Colby and his Boog-ness are near the top of the leader-boards for their respective positions as well.

I’m not quite ready to take the over on Skip’s projection quite yet, but he did show a lot of improvement over the course of the season.

Now here is a look at some of the free agents and speculated trade targets of the Cardinals for their vacancies in the left corners.

FA 3B

FA OF

Take that, Adam Dunn bandwagon. There’s no DH in the NL just in case any of you have forgot. Over the past two seasons Dunn has hit 78 homers, hit 205 RBI’s and drawn 238 walks, and yet has completely sabotaged his value as a player by being a terrible outfielder. His WAR those two seasons, including base-running: 2.2. OVER TWO SEASONS. 2.2. -13 runs actually seems optimistic for Dunn, considering his recent disastrous play, and that’s saying something.

Jermaine Dye….blah. Brad Hawpe. Blarf. I can’t believe anyone would say Bay is a more complete player than Holliday, but it’s being said by some uninformed folk out there who should know better.

I wouldn’t mind seeing what Dayton Moore would want for David DeJesus, and then seeing Moz Def couldn’t sign Figgins at a reasonable enough rate. That wouldn’t provide any ballyhooed “Albert Protection”, but those are two solid defenders who can get on base at a decent clip. There are more ways to win than just slugging.

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