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Dialed In — Monday, October 30, 20062006 National League Gold Gloves - As I see itDefensive data has been and is being refined pretty well these days. With more and more play-by-play data making it to the mainstream, all of us are stretching the boundaries of what we require from black-box analysts. With the exception of some park factors, we are discovering that Zone Rating provides a pretty good picture of defense. Taking the zone rating and accounting for league averages, based on tens of thousands of defensive innings played, we can closely assess the number of runs saved by a defensive players as compared to his peers.
To be sure, even this data could be refined to account for parks better - Fenway’s Green Monster is a tremendous issue - and handedness of batters - NOT handedness of pitchers - to tune the picture a bit better, but the data you will read will be very close to any refined data. Very close. The basic methodology for this work is here. You can also read more on where we are headed with Park Factors by reading Rally’s latest work.
I have tweaked this for chances per inning from the original data, so the chances assumed here may be slightly higher/lower, but if you did the same work from the referenced article, you’d find your results would be within a run or two of what I post. And really, the most important thing I do here is provide you with the tools to evaluate defense on your own, without me doing the math. Please note, after this article, I will post some others’ work that even refines what I have done, with a comparison to what I have done. It should be exciting for you - it is for me. Most importantly, it broadens the network of individuals accurately creating the defensive evaluations, as well as allows for everyday updates. Yes, I said *every day*.
Now on with the show. Here are the leaders and trailers at every position for the National League, with some commentary where necessary. The American League results are here. In general I draw the Gold Glove qualification line at a significant number of innings - usually around 650. It would be unusual for someone playing only 650 innings to lead the league in anything, but I’m willing to give it a look.
Catcher First LAST TEAM GP INN RSpt RS/150 Yadier Molina StL 127 1037.3 8 10 Henry Blanco ChC 69 526.0 5 13 Miguel Olivo Fla 124 971.3 5 7 Ronny Paulino Pit 124 1047.0 4 6 Chris Snyder Ari 60 495.0 4 11 Yorvit Torrealba Col 63 530.0 4 10 David Ross Cin 75 621.7 3 7 Damian Miller Mil 98 840.0 3 5 Mike Lieberthal Phi 60 484.0 3 8 Russell Martin LA 117 1015.0 3 3 Jason LaRue Cin 63 512.3 2 6 Brian Schneider Was 123 990.3 2 3 Johnny Estrada Ari 108 925.7 2 3 Brad Ausmus Hou 138 1125.7 0 0 Eliezer Alfonzo SF 84 700.3 0 -1 Josh Bard SD 71 495.7 -2 -5 Brian McCann Atl 124 1016.3 -3 -4 Paul LoDuca NYM 118 1027.0 -4 -5 Michael Barrett ChC 102 852.0 -7 -11 Mike Piazza SD 99 718.0 -13 -24 Yadier Molina was a good defensive catcher, and amazingly, the good hitting catchers - McCann, LoDuca, Barrett and Piazza are all bringing up the rear. Maybe the old cliche rings true. Molina led in CS%, and will likely win the conventional GG award.
First Base First LastName TEAM GP INN RSpt RS/150 Scott Hatteberg Cin 131 1089.7 9 11 Adrian Gonzalez SD 155 1341.0 5 5 Nomar Garciprra LA 118 1017.0 3 5 Carlos Delgado NYM 141 1246.3 3 3 Adam LaRoche Atl 142 1153.3 2 3 Albert Pujols StL 143 1244.3 0 1 Conor Jackson Ari 129 1079.7 0 0 Mike Jacobs Fla 124 972.0 -1 -1 Nick Johnson Was 147 1252.3 -1 -1 Todd Helton Col 145 1272.3 -2 -2 Ryan Howard Phi 159 1412.0 -3 -3 Lance Berkman Hou 112 923.0 -4 -5 Prince Fielder Mil 152 1319.3 -9 -9 Scott Hatteberg must be able to “really pick it”. There’s very little variation here. Nine runs seperate #2 from #11. That says to me, skill-wise, that the difference could easily be in the chances. I don’t know who will win the real GG, but I would give it to Hatteberg. Yes, I know PUjols is a really good fielder - sure, but this season, he just didn’t make a bunch of plays above and beyond.
Second Base First LastName TEAM GP INN RSpt RS/150 Jose Valentin NYM 94 782.3 12 21 Jamey Carroll Col 109 895.7 11 17 Craig Biggio Hou 129 1062.0 5 6 Chase Utley Phi 156 1367.3 3 3 Brandon Phillips Cin 142 1216.3 2 2 Josh Barfield SD 147 1259.0 1 1 Aaron Miles StL 88 650.7 1 2 Ray Durham SF 133 1139.7 -2 -3 Orlando Hudson Ari 157 1349.0 -4 -4 Dan Uggla Fla 151 1304.3 -4 -4 Jose Vidro Was 107 902.7 -5 -8 Jeff Kent LA 108 888.7 -8 -12 Rickie Weeks Mil 92 794.0 -8 -14 Marcus Giles Atl 134 1150.7 -9 -11 Jose Castillo Pit 145 1235.0 -18 -20 I really hope Harvey shows up to tell us how much Weeks improved over the last couple of months. He was at -8 RSpt at teh ASB. He held his ground, so he may have turned a corner for next year. As for the GG, I am certainly wary of awarding it to someone with less than 800 innings, so I would probably go with Jamey Carroll - plus he has the Colorado BIP issue to deal with.
Third Base First LastName TEAM GP INN RSpt RS/150 Pedro Feliz SF 159 1372.3 11 11 Morgan Ensberg Hou 117 975.0 9 12 Scott Rolen StL 142 1216.7 5 6 Freddy Sanchez Pit 99 822.7 5 8 Ryan Zimmrmn Was 157 1368.3 1 1 David Bell Mil/Phi 143 1216.0 0 0 Chad Tracy Ari 147 1278.0 0 0 Aramis Ramirez ChC 156 1353.0 -4 -4 Chipper Jones Atl 105 888.3 -4 -6 Garrett Atkins Col 157 1381.3 -5 -5 Edwin Encarnc'n Cin 111 931.3 -10 -14 David Wright NYM 153 1365.3 -10 -10 Miguel Cabrera Fla 157 1334.0 -12 -12 Pero Feliz played as many innings as anyone else, and played them better than anyone else. He should win the GG. Given the history of the award, Rolen will win. Not on this list, but really good in 600 innings was Corey Koskie for Milwaukee. He was a +9 RSpt, and a +19 for RS/150.
Shortstop First LastName TEAM GP INN RSpt RS/150 Adam Everett Hou 149 1292.3 27 28 Omar Vizquel SF 152 1281.3 11 12 Jose Reyes NYM 149 1320.3 10 10 David Eckstein StL 120 1029.0 7 9 Khalil Greene SD 113 998.7 5 7 Craig Counsell Ari 88 737.7 4 8 Clint Barmes Col 125 1073.7 4 5 Ronny Cedeno ChC 134 1130.7 2 2 Jack Wilson Pit 131 1130.0 1 2 Bill Hall Mil 127 1090.3 -1 -1 Rafael Furcal LA 156 1371.0 -4 -4 Royce Clayton Cin/Was 129 1055.0 -5 -7 Edgar Renteria Atl 146 1265.3 -6 -6 Jimmy Rollins Phi 157 1378.0 -6 -5 Felipe Lopez Cin/Was 155 1337.0 -17 -17 Hanley Ramirez Fla 154 1323.3 -17 -18 If Ozzie Smith was as good as Adam Everett, he was incredible. Everett is on the verge of saving hte most runs on defense over the last 20 years. He’s truly incredible at outpacing his peers. Hanley Ramirez can be forgiven - hopefully he’ll learn and develop. There’s Eckstein, ugly arm and all, performing well. He did look very good in the NLCS, making several stops on balls I was sure would be hits.
Left Field First LastName TEAM GP INN RSpt RS/150 Dave Roberts SD 116 970.0 14 19 Ryan Langrhns Atl 104 706.0 11 20 Matt Murton ChC 133 1049.0 6 8 Matt Diaz Atl 95 587.3 6 14 Alfonso Soriano Was 158 1374.7 5 5 Matt Holliday Col 153 1334.3 3 3 Andre Ethier LA 109 896.7 2 4 Luis Gonzalez Ari 150 1315.0 2 2 Cliff Floyd NYM 92 768.3 1 1 Barry Bonds SF 116 875.0 0 0 Pat Burrell Phi 126 988.7 0 0 Carlos Lee Mil 98 835.3 -3 -5 Jason Bay Pit 157 1373.0 -6 -5 Adam Dunn Cin 156 1321.0 -12 -12 Josh Willinghm Fla 132 1070.7 -15 -19 Preston Wilson Hou/StL 102 873.0 -20 -31 Dave Roberts? I didn’t see that coming. He is a CF playing LF, so I can buy it. We’re expecting a park factor for Wilson in Houston, and tehre is some effect in Florida, but Willingham is a catcher, so he probably isn’t very good. Then Adam Dunn…
Center Field First LastName TEAM GP INN RSpt RS/150 Juan Pierre ChC 162 1426.0 16 15 Carlos Beltran NYM 136 1184.0 9 10 Eric Byrnes Ari 123 1051.0 8 11 Mike Cameron SD 141 1244.0 8 9 Chris Duffy Pit 77 672.7 6 12 Jim Edmonds StL 99 792.3 6 9 Willy Taveras Hou 138 1117.7 5 6 Steve Finley SF 130 973.3 3 5 Cory Sullivan Col 114 841.0 -1 -2 Kenny Lofton LA 120 961.0 -2 -3 Brady Clark Mil 114 911.7 -3 -4 Aaron Rowand Phi 107 901.7 -3 -5 Andruw Jones Atl 153 1317.3 -9 -9 Ken GriffeyJr Cin 100 870.3 -11 -18 Juan Pierre? That’s a good-sized lead as well. Beltran, with this defense, is the best selection for MVP. Griffey hasn’t played well in CF for a few years, and while not as bad as last year, he still isn’t good. I’d play him in LF. And if BIS still has Andruw as the top CF, they should re-consider their algorithms.
Right Field First LastName TEAM GP INN RSpt RS/150 Brian Giles SD 158 1399.7 12 11 Juan Encrncion StL 125 983.3 9 13 J.D. Drew LA 135 1118.0 5 6 Austin Kearns Cin/Was 144 1229.7 5 6 Jeremy Hermida Fla 85 684.7 4 8 Jacque Jones ChC 143 1205.0 4 4 Randy Winn SF 89 653.7 2 4 Bobby Abreu Phi 97 846.0 1 1 Xavier Nady NYM/Pit 99 855.7 0 1 Jeff Francoeur Atl 162 1422.7 -1 -1 Geoff Jenkins Mil 133 1101.0 -2 -2 Shawn Green NYM/Ari 131 1121.3 -5 -6 Moises Alou SF 81 647.7 -6 -12 Jason Lane Hou 89 679.3 -8 -16 Brad Hawpe Col 145 1198.7 -9 -10 Jeromy Burnitz Pit 84 643.0 -12 -25 Brian Giles was a good CF a few years back. With an OF of Cameron, Roberts and Giles, I am not surprised the Padres OF defense is this good. Jeromy Burnitz should retire.
I have to talk about Endy Chavez. His defense this year was incredible, and most people got to see it in Game 7 of the NLCS. Fortunately you are going to see that play a few million times over the rest of your life. He played incredibly all season, and in all three outfield positions. He totaled 264.3 innings in CF at +5 RSpt, 239.3 innings at +6 in LF, and +5 RSpt in 312.6 innings in right field. That’s +16 in 816.3 innings. His RS/150 is upwards of 25 runs. He deserves the Gold Glove over Dave Roberts. |
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What I'm suggesting is that the system *may* be wrong by some *unknown* number of plays, based on how closely the optimal positioning for average fielders given the distribution of balls in play against a team matches the optimal positioning for average fielders based on the average distribution of balls in play.
-- MWE
It probably doesn't make much difference in the infield. Its a bigger deal in the outfield, where many flyballs could be caught be either fielder. If you penalize an outfielder for getting called off on a ball, you wind up with weird ratings.
But your ratings will also be weird if the REASON the fielder didn't catch the ball is because he couldn't get there. That speaks a lot more about a fielders 'ability' than being called off of a ball you can get, yet the data will score you the same. Same result, NOT same measure of fielding ability.
Play B: Difficult fly ball, Fielder Y cannot get there even though it in his zone, Fielder X ranges out of his zone and makes the play.
Which is more common? I don't have the play by play microdata or the time to wade through and identify such plays, but my experience watching games tells me that Play A is far more common.
I see what you are saying. Lets say Barry Bonds really plays left field in his Lazy-Boy. He catches 5 balls per year hit right at him. Gary Pettis and Willie Mays Hayes in their prime cover the other two spots so well they prevent anything from falling in Bonds' zone. Bonds' ZR is a perfect 5/5, but he's a terrible outfielder.
In reality though, a terrible OF is going to have tons of hits drop in around him, and there's nothing the other OF's can do about it. If they catch a ball in his zone, it is most likely a flyball that hangs up forever and either player could catch.
Can somebody remind me how the zones of responsibility are determined? I seem to recall that if the
average SS, say, fields 50% of the balls in a particular zone, then that zone is considered
his responsibility. Is that correct?
I did see you are using STATS, Inc's grid (22 vectors). The obvious downfall to that is, the larger the park, the larger the vector (if they are still using the same system they used a few years ago. Do you know?). BIS data is not reliable I often hear, but I don't know to be true.
My last concern would be, and forgive me if you have answered this somewhere else, are you not measuring player-vs-player instead of player-in-a-specific-park-opportunity?
As a side note, perhaps you could include a 3-year historical baseline reference point (league average) and show the player's 3-year value against it as well?
Until we come up with an absolute positioning system on every ball in play, a square footage vector breakdown for every park in baseball, and the ability to measure a host of variables like height and speed of the ball in play, up the middle or spread eagle defensive positioning, and the like, ZR is about as uncomplicated as it gets. I still think Wayne Britton's idea of having the threads coated with a trackable substance makes sense (joking here).
Good work Chris.
average SS, say, fields 50% of the balls in a particular zone, then that zone is considered
his responsibility. Is that correct?
John,
yes, that is correct. I have written a synopsis here.
Always be sure to read the discussion. I have done a ton of defensive work, but I always miss something, or someone else always has watched and can offer new viewpoints.
thanks for your kind words.
My last concern would be, and forgive me if you have answered this somewhere else, are you not measuring player-vs-player instead of player-in-a-specific-park-opportunity?
I'm not sure I can read this clearly. We (and I say we, because I'm not the only one working through this) do need to figure park factors. Someone like Manny Ramirez is clearly portrayed as a worse fielder than he is. I simply haven't had enough granular data to work on that. Joe Arthur has made some interesting obs on Fenway PFs, and we're starting to get the right ideas. Not working with the raw data makes it difficult.
So, the player is half in a specific park. But it is player vs player, and some players have a disadvantage.
As a side note, perhaps you could include a 3-year historical baseline reference point (league average) and show the player's 3-year value against it as well?
this would be a smart thing to do. I am preparing a comparison of my quicker and dirtier method to some better techniques created by SG and Kyle C. We have enough data now that we shold be able to do this.
Have you reviewed the work on 20-year data also in this blog? there's a bunch of data in there, and more to come now that the Mets season is over.
And yes, AFAIK, STATS is using the same system (but is using smaller (5ft) zones). I have also heard that the BIS data is missing some pieces, and when asked I advised the team use MLB's data due to that. there was some question about the professional aspect, but I don't think there is any real difference between scorers. Geeks take this stuff really seriously.
I have looked at some retrosheet data to try to answer the first question. Here's what I did:
I looked at the retrosheet data from 1991-1992: this data has complete batted ball type and hit location data (the data for these years actually comes from STATS). Of course, retrosheet employs the Project Scoresheet zone grid, which has bigger zones. However, I think it can be used to answer this specific question. The 3B zones on the STATS grid are CDEF, then there is a single zone that is a gap (zone G), then the SS is responsible for the next 5 zones (HIJKL).
Now the PS zones of 6S+6+6D correspond to the STATs zones HIJK (approximately). In other words, the right edges of the STATS and PS zones for SS are same. So, if I look at how often a 3B fields a ball in 6+6S+6D on a PS grid, this will be the same as the number of times a 3B fields a ball in the STATs SS zones (neglecting the 5th SS zone (L) way over near 2nd base).
What I find is a total of 15441 ground balls hit in 6+6S+6D (or HIJK). Of those, the SS made outs on 12970, while the 3B made outs on 365 of them. Or, in other words, the 3B is "stealing" balls from the SS about 3% of the time. Doesn't sound like a very big deal to me.
The 3B actually cuts off the slow choppers pretty often: in PS zone 6S, the 3B fields 27% of the outs made, while in zone 6, the 3B fields less than 2% of the outs.
Of course, these numbers represent the average. A rangy 3B playing well of the line could increase the rate of "incursion" into the SS zone. A more careful analysis of the retrosheet data might shed some light on this, but it wouldn't be easy.
Very good point about shifts. I can try to address the extreme shift issue by only considering balls hit by right-handed batters. The assumption is if there is a rhb at the plate, the 3B will not be playing in the SS zone.
Of course, for the vast majority of (the excluded) left-handed batters, there is no extreme shift, but there may be some subtle shifting away from the 3B line, which is probably not generally the case when a rhb is up.
Anyway, if I remove lhb, I find (still looking in the 6+6S+6D/HIJK zone) 11818 total ground balls. The SS fielded 10181 and the 3B fielded 229 of them. So the fraction of outs made by the 3B is now 2.2% (compare to 2.7% when all batters are included).
The 3B out fraction is down to 21% on the slow balls (fielded in zone 6S) and 1.4% on the balls fielded in zone 6.
In any case, even with the right-handers there are still a bit more than 2% of the outs in the SS zone made by the 3B. I consider 2% a small number. Your metric (or any other) will have other sources of uncertainty that are far larger than 2%.
I don't normally ask someone to do a piece of work that I can do myself, but I don't have time to do it right now. So let me ask:
Are there differences in the distribution of these by team? I realize that we are talking small sample sizes here, but if there are some teams that have more of them than others that might provide a clue into the extent to which there are positioning/distributional differences.
-- MWE
He is in no way "above average" at anything with the glove. He may have been at one point in his career, but not anymore. At best you can get through a game without noticing him, but he won't make any plays that aren't routine.
so there is a large uncertainty -- around 0.5-1% if binomial statistics holds -- on each team's result. Anyway, here is a list of the fraction of outs in the 6-zone (6+6S+6D) fielded by the 3B:
LAN 5.20%KCA 4.10%
SFN 3.74%
HOU 3.73%
CIN 3.40%
SEA 3.21%
SLN 3.21%
ATL 3.21%
OAK 3.09%
SDN 2.93%
BOS 2.90%
PHI 2.89%
NYN 2.87%
NYA 2.78%
BAL 2.77%
TOR 2.69%
PIT 2.48%
CLE 2.38%
MIL 2.24%
TEX 2.04%
MIN 1.95%
CAL 1.61%
CHN 1.57%
CHA 1.54%
DET 1.49%
MON 1.46%
Chris, how was Rich Aurilia's defense this year at his various positions?
Amen.
Is that Retrosheet data available easily on the website, or is there some kind of database you had to download and parse?
The hit location info needs to be parsed out of the event files using BEVENT or CWEVENT, and put into a DB for analysis, but it's a relatively straightforward process.
-- MWE
Player Pos INN RSptAurilia, Rich 1B 329.2 -2
Aurilia, Rich 2B 41 1
Aurilia, Rich 3B 356 5
Aurilia, Rich SS 198.2 0
Thanks!
There is some work involved in getting a working database setup going with the retrosheet pbp data,
but once you're over the hump, you can get info out pretty fast.
And here it is:
Park Factors
I think I'll start a thread for this one as well.
Why are your park factors expressed as /400 chances? Is that supposed to be approximately one season? Dial's stats say that corner LF gets about 350 chances per 162, or ~325 chances per 150 games. Wouldn't it be better to do LF PF as per 325 chances (150 g) and RF and CF with their corresponding numbers?
Thanks.
Yes, you are going to get the databases as son as we get them manageable (or you can check out Kyle's in his Forum link.)
You linked to his previous post, which wasn't quite the Park Factors, but something like them. His current post now has actual park factors based on matched pairs.
Thanks GGC (and to the appropriate Kyle).
The .750 / -25 are just examples off the top of my head, not his actual ZR.
And thanks Darren for answering Chris's question.
Hudson's FRAA remains about the same in Arizona as it always was in Toronto, and I confess to some subjective doubt that Valentin or Biggio were better defensively than Hudson this past year. This may have something to do with the fact that "age 37" and "age 40" are not theoretical concepts to me...
I hope to/plan to publish three year marks later.
Valentin was always a strong fielder. His rating isn't surprising to me at all. Biggio's is.
a) Is this significant in any way? (Clearly ZR doesn't like him, and TFB Awards in the BJ Handbook put him at 5th), or
b) Has anyone else ever accomplished this feat?
In 2005, Izturis and Robles were average and below-average on RF.
In 2006, Renteria was very much below average on RF.
Dewan changed it this year to "about five feet" in the outfield. Infield vectors are the same, and according to the description in Fielding Bible, they appear to be similar to STATS' Zones. However, the explanation is rather lacking.
The major difference (other than the explanation of your system is clear) between your system and Dewan's is that your system treats all outs as equal while Dewan assigns a value based on the the percentage of outs in that vector -- the same for outs not made. As for Jones, it appears Dewan's system must credit him for more "difficulut outs" than other CFers.
agreed. And that's granular data, and I don't know if Dewan uses an actual play value or an average play value (the actual play value is the wrong thing to use).
I appreciate your input on the zones. It's possible other descriptions are more cocksure than they should be.
Dewan uses an average play value.
BTW, any thoughts on why your system ranks Pujols with 0 RSpt. while Dewan's ranks him +19 Plus/Minus (best in the majors)?
Also could be the zones just don't match up. I can't tell how different they are since Dewan has not (to my knowledge) published his zones.
There are 2006 Top 10 leaderboards for each position in the new Bill James Handbook.
Yes, those are Pujols' 2006 numbers. They are from the 2007 Bill James Handbook, which shipped last week. Unfortunately, Dewan's system is only presents in Leaderboard fashion in the Handbook. Here is a quick look at how Dial and Dewan's rankings matched up to the GGs:
http://journals.aol.com/bads85/ManyGoFewUnderstand/entries/2006/11/06/nl-gold-gloves-versus-pbp-data-metrics/1292
I've ordered mine but have not received it yet. Doesn't make me too happy.
Ugh. I hope they plan distributing that data in some fashion.
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