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Monday, February 05, 2007

Dan Rosenheck’s WARP Data

WARP Methodology and Results

Thanks, Dan!

EDIT: Link updated 2/23/2009

John (You Can Call Me Grandma) Murphy Posted: February 05, 2007 at 08:59 PM | 763 comment(s) Login to Bookmark
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   701. AROM Posted: December 04, 2009 at 09:30 PM (#3403635)
Paul, I procrastinated too long to get that text file you posted with HOM ID's. Can you resend it to Rallymonkey (numeral for five) at comcast dot net?
   702. jimd Posted: December 05, 2009 at 12:24 AM (#3403886)
Dan,

Where can I find your complete WARP file? I can only find the 1987-2005 file.


I can't find pitcher data. Is it there?
   703. Juan V Posted: December 05, 2009 at 12:33 AM (#3403898)
The Rosenheck DB will be up shortly (FTP client says 17 minutes). It's the older version though, but I'm not sure when I'll get to update it for his latest baserunning fielding adjustments (some time this weekend or possibly not until early next week) and didn't want to wait.

I'll post it in the main entry once it's up to date.


New, revised numbers coming soon! *does happy dance*
   704. Joey Numbaz (Scruff) Posted: December 05, 2009 at 01:56 AM (#3403960)
The new revised numbers are already out, Dan posted them like a year ago. I just need to incorporate them into my database, which has some good queries on all the data, etc. . . . that's what I meant . . .
   705. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 05, 2009 at 01:56 AM (#3403961)
No, I haven't published the pitcher data because it's so preliminary (and because I lost my spreadsheet until recently!). I can email it to you if you'd like, jimd, but it's based on a long-outdated version of BP's DERA at this point.
   706. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 09, 2009 at 09:36 PM (#3408399)
Cross-posting an example Larry Walker's 1997 season from his player thread, as a work-through example of how I calculate WARP for the 1987-2005 period:

Walker had 64 unintentional walks, 14 intentional walks, 14 hit by pitch, 109 singles, 46 doubles, 4 triples, 49 home runs, 4 sacrifice flies, 90 strikeouts, and 270 fielded outs. According to Baseball Prospectus, his baserunning was 2.5 runs above average, and he hit into 2.17 fewer double plays than a league average batter would have given his opportunities.

OK, let's bust out the eXtrapolated Runs estimator. UIBB and HBP are worth 1/3 of a run, IBB are 1/4. 1B are 1/2, 2B are .72, 3B 1.04, and HR 1.44. SF are 0.37, as are net DP (before counting the effect of the extra out they consume). To make total league XR equal total league runs scored for the 1997 NL, fielded outs are worth -.102 runs, and strikeouts are worth -.111 runs. So Walker produced (64+14)/3 + (14/4) + (109/2) + (.72*46) + (4*1.04) + (49*1.44) + (4*.37) - (90*.111) - (270*.102) + 2.5 + (2.17*.37) = 159.1 runs.

The average team in the 1997 NL had 4,171 batting outs. Walker consumed 4 SF + 90 K + 270 fielded out - 2.17 Net DP = 361.83 of them, leaving 3,809.17 for his teammates. The 1997 NL scored .1788 runs per batting out, and Coors Field had a park factor of 122, so an average lineup in Coors would generate .1788*1.22 = .2181 runs per out. .2181 runs per out times 3,809.17 outs yields 830.8 runs for Walker's teammates. Adding on his 159.1 runs means that an average 1997 NL lineup in Coors, with 664 PA replaced by Larry Walker, would have scored 989.9 runs.

Now, on to the defense. Walker spent time at three positions: first base, center field, and right field. In 25 innings at first base, all three metrics that show a statistically significant correlation to an average of PBP stats during the period they are available (TotalZone, Chris Dial's Runs Saved in playing time, and Simple Fielding Runs) find his fielding exactly average. In 13 innings in center field, both RSpt and TotalZone give him +1 (SFR is not available for the outfield before 2003), so he's credited with one run above average there. And in 1,235 innings in right field, RSpt has him at -2.4, while TotalZone has him at +4.1. The equation that provides the best fit to the PBP average in RF is .68*RSpt + .23*TZ, which comes out to -0.7. However, repeating this procedure on all the RF in the league produces an average that is slightly above 0; to zero it out, we have to subtract .0013 runs per inning, bringing his final RF range figure down to -2.3 runs. Finally, Sean Smith finds his arm to be 4.8 runs above average. I regress this figure 13% to the mean, because that provides the best fit to the Smith/UZRarm average for the years where both are available, reducing it to 4.2 runs above average. So his total defensive contribution is 1 - 2.3 + 4.2 = 2.9 runs above average.

A 1997 NL average team in Coors would score 746*1.22 = 910.1 runs. Walker's defense takes away 2.9 runs, leaving the Average Team Plus Walker's opponents with 907.2 runs.

A team scoring 989.9 runs and allowing 907.2 runs in 162 games has a Pythagenpat exponent of 2.016, meaning that the Average Team Plus Walker would win 88.1 games.

The 2005 standard deviation (which I use as a base) was 5.6% lower than the 1997 NL regression-projected standard deviation, so we pull Walker 5.6% back to the mean, down to 87.7 wins.

My methodology for determining replacement level (explained at length in my WARP thread) finds that an average team with a replacement player in Walker's playing time and mix of positions would win 80.0 games in a league with the 2005 standard deviation. Thus, Walker was 87.7-80 = 7.7 standard deviation-adjusted wins above replacement (WARP2).
   707. jimd Posted: December 09, 2009 at 11:34 PM (#3408574)
I can email it to you if you'd like, jimd,

Thanks. It would be much appreciated.

but it's based on a long-outdated version of BP's DERA at this point.

I want to incorporate your numbers into my system for next year's election.
I need pitcher's data, even if it is somewhat out-of-date.
Whenever you update it, let us all know (via this page).

I also updated my email address (which had gone stale).

Thanks,
--JimD
   708. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 22, 2011 at 11:25 PM (#4022302)
Joe Dimino has requested that I update my WARP numbers to include the six seasons since I developed the stat. (Wow, it's been that long?!?!) The trickiest thing would be calculating FWAA. The problem is that back in the good old days you had play-by-play defensive stats from both databases, BIS and STATS, which to my knowledge are equally good. Now that MGL has switched to BIS, though, the only remaining stat I know of tapping the STATS database is Chris Dial's RSpt, which is based on brute Zone Rating rather than the more detailed STATS numbers. So what's the best way to approach this? I suppose I should do a historical regression of RSpt and Dewan on the STATS UZR/Dewan/PMR hybrid I used to use, and then use that equation except replacing Dewan with 50% Dewan and 50% BIS UZR? That will lead to more tightly clustered, lower-stdev FWAA for recent seasons than I had for 2003-05, however...
   709. DL from MN Posted: December 22, 2011 at 11:34 PM (#4022313)
If you're changing FWAA methodology I would suggest changing the numbers for all applicable years. It might be time for an revised version, not just an extension of the previous version.

Is there any financial incentive for BBTF to host your WAR database? It seems like the WAR calculators drive plenty of page hits.
   710. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 23, 2011 at 02:27 AM (#4022397)
There is no consistent FWAA methodology, DL from MN--or rather, there is, which is to make the best use of the available data. To rehash, I arbitrarily defined an average of, if I recall correctly, 45% UZR, 30% Dewan, and 25% PMR as the "true" fielding measure for 2003-05. (I figured that the STATS and BIS databases were equal, but since I had two good BIS metrics and only one good STATS metric I'd collectively give BIS a 55% weighting, with a slight preference for Dewan over PMR). I the ran regressions on the other defensive stats--RSpt, SFR, TZ, DRA, FRAA, and FWS--against this value at each position for the 2003-05 period. That let me know how much to weight each stat and how much to regress to the mean at each position for pre-2003 seasons in which some or all of the preferred PBP metrics are not available.

Strangely, with MGL's switch to BIS and the death (to my knowledge) of PMR, we now know less about defense than we did in the 2003-08 or 09 "golden age." Keeping a consistent FWAA methodology would simply entail repeating the 1987-2003 process for recent seasons to come up with a best guess of what the 45/30/25 weighted average would be likely to tell us. However, I'm afraid this is also impossible, as I don't have databases of both STATS-based and BIS-based UZR for the same season to see how they correlate. I'm not quite sure how to get around that one...

I'm happy to put it up as many places as possible. If anyone at BBTF wants to host it I would be delighted for them to do so! Speaking of which, I have to get the link at Tango's site back up...
   711. Joey Numbaz (Scruff) Posted: December 23, 2011 at 02:46 AM (#4022416)
If anyone wants Dan's numbers in a manipulable format, I've got them in an MS Access DB that works great for me.

I've also written these queries:

position crosstab - lists players and their WARP by position

League leader WAR by season

MVPs

Pennants Added (career total)

Total MVPs - (Honus Wagner wins with 13. Barry had 10, Hornsby and Ruth 9, Mantle 8, Cobb, Mays, ARod 7 - through 2005).

Seasonal WAR for everyone since 1893.

Also have the data table for all of Dan's WAR by component (with my war credit add for certain players) and the master table from the Lahman DB in there as well and an extra column designating HoMers (hasn't been updated for awhile though).
   712. fra paolo Posted: December 24, 2011 at 01:21 AM (#4022853)
Coincidentally, my reasons for leaving DanR WARP behind are touched on exactly by the sudden interest in FWAA. Basically, I'm not a fan of 'averaging' fielding results from a variety of systems. I'm not a fan of some of the systems used, so I think averaging them in fact mixes bad data with good data.
   713. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 24, 2011 at 02:07 AM (#4022871)
Fra paolo, do you have issues with Dewan's Plus/Minus, Pinto's Probabilistic Model of Range, or MGL's UZR? If not, then you should be fine with my 1987-2005 numbers, which are a statistical approximation of a weighted average of those stats. Pre-'87, I have to get around to doing studies on DRA, TZ and SFR alone for the Retrosheet era.
   714. Joey Numbaz (Scruff) Posted: December 24, 2011 at 04:00 AM (#4022896)
I don't see how anyone could have much confidence in any of the fielding metrics. Averaging them seems quite reasonable to me.
   715. fra paolo Posted: December 24, 2011 at 06:48 PM (#4023044)
Fra paolo, do you have issues with Dewan's Plus/Minus, Pinto's Probabilistic Model of Range, or MGL's UZR?

Yes. It's been ages since I read how PMR is constructed, but I am not a particular fan of either Dewan's or MGL's metrics.

Philosophically, I think the Wins Shares system is constructed correctly, as it starts at the team level and works its way down to the players. The systems you name, if I am recalling correctly, build up from individual plays, although there may be subsequent adjustments to match team total data. Plus Colin Wyers' work on observer bias has undermined the trust we can put in these metrics. While he overstates the significance of his discoveries, I don't doubt that the impact is sufficient for us to add a note of doubt in the reliability of zone-based metrics. Fundamentally, we are dealing with zones that are too small, and with data that starts as too individualized.

In fact, I am a bigger fan of Humphrys' work than any zone-based metric. We know who fielded the balls. We only have to worry about where the balls that didn't get fielded went.

EDIT: I also rather like Dial's 'brute' use of zone rating, because an elegant side-effect of his system, which I'm not sure he has noticed (I have tried to tell him, but I have trouble conveying my meaning), appears to be to expand the zones. So I think his numbers are better than Dewan's or MGL's.
   716. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 24, 2011 at 09:50 PM (#4023126)
fra paolo, that doesn't mean you'd endorse Fielding Win Shares, does it? With its artificial cap and limiting of 1B to a range of about +/- 4 runs?
   717. Joey Numbaz (Scruff) Posted: December 26, 2011 at 05:41 PM (#4023508)
Dewan is not a zone rating.

He looks at each ball and compares whether or not the play was made to the league average. It is not like zone rating in terms of making plays in and outside the assigned zone and it's artificial penalties.
   718. Mike Emeigh Posted: December 26, 2011 at 06:29 PM (#4023536)
I got Wizardry for Christmas, just started to read it. Think you absolutely need to look at it.

-- MWE
   719. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 26, 2011 at 10:40 PM (#4023657)
I have a copy, just have to get through it. That said, I've been in regular touch with Michael for years, while he was developing DRA and I was developing my WARP, so I'm pretty familiar with it.
   720. Mike Humphreys Posted: December 27, 2011 at 12:41 AM (#4023694)
MWE, thanks for mentioning Wizardry!

David, some things discussed in Wizardry that might be relevant to the discussion in this thread.

Agreed that we lost a great deal when we lost STATS-based UZR. Given all the troubles with batted ball data, highlighted I believe for the first time in my August 2007 articles at THT, it is crazy that we all rely on one non-open-source database for batted ball data ratings. See also my article in the 2012 Hardball Times Annual.

If you are able to do some studies of DRA and TZ versus UZR, you should know that TZ shares two major biases that will result in TZ correlating 'better' with UZR. The biases involve overrating (underrating) bad (good) fielders playing next to good (bad) fielders, and overrating the impact of errors and error-avoidance. Also, in my rush to get the book done, I did not take full advantage of play-by-play data for seasons since the 1950s. I'm working on new numbers that pick up on using Sean's idea of using a pitcher's career ground out to fly out to estimate fly balls and ground balls with less distortions based on the relative quality of a team's outfielders and infielders each year, while avoiding several distortions that TZ reintroduces. This is all discussed in chapter three.

I think that in time DRA/Wizardry will shift the consensus career valuations for a couple of dozen historically significant players by between five and fifteen career WAR. Jeter in particular is grotesquely overrated as a fielder by UZR and TZ. The version of DRA in Wizardry suggests he is a best a borderline Hall of Merit player; new version of DRA developed using post 1988 Retrosheet data show unambiguously that he is not a Hall of Merit player.
   721. Foghorn Leghorn Posted: December 27, 2011 at 02:38 AM (#4023723)
Jeter in particular is grotesquely overrated as a fielder by UZR and TZ. The version of DRA in Wizardry suggests he is a best a borderline Hall of Merit player; new version of DRA developed using post 1988 Retrosheet data show unambiguously that he is not a Hall of Merit player.
That is very interesting. He is remarkably poor.
   722. fra paolo Posted: December 27, 2011 at 05:41 AM (#4023789)
[Dewan] looks at each ball and compares whether or not the play was made to the league average.

I understand that Plus/Minus divides the fields into zones. That's why I used the term 'zone-based' rather than zone rating.

fra paolo, that doesn't mean you'd endorse Fielding Win Shares, does it? With its artificial cap and limiting of 1B to a range of about +/- 4 runs?

It depends what one means by 'endorse'. I find Fielding Win Shares useful, and knowing those issues you mention one can accommodate occasions when they might be a factor.

I've argued this in HoM threads before, but my impression of fielding is that one is mainly interested in extreme cases. Fielding differences are mostly a very narrow band, which exacerbates the effect of being exceptionally good or bad. A bad glove really hurts, relatively, and a good glove really helps.

I read 'Wizardry' in the summer. It definitely is a must-read for anyone who hangs out here.
   723. Mike Emeigh Posted: December 27, 2011 at 04:05 PM (#4023850)
That is very interesting. He is remarkably poor.


Yes, but his poor ratings are exacerbated by a relatively unusual ball-in-play distribution and defensive alignment.

The Yankees as a team, from 1998-2002, had fewer balls hit into play on the left side of the diamond, and more balls hit into play on the right side of the diamond, than you would expect (even when you take into account the distribution of left/right hitting against them). The Yankees therefore tended to overshift both their infielders and their outfielders toward the right side of the diamond, even in the late innings - Brosius played closer to the SS/3B hole, Jeter cheated up the middle, Bernie played more toward right-center. The upshot of this alignment is that Yankee fielders tended to be penalized more heavily for hits allowed in every system, because those hits allowed were usually in areas of the field where other teams would be making plays. The rewards that Yankee fielders got for making plays, on balance, didn't counter that effect primarily because they had fewer balls in play than did most teams (they had a lot of strikeout pitchers). So while the Yankees generally had decent team defense, their key up-the-middle fielders tended to look bad in most defensive systems.

Jeter IS a below-average fielder; even cheating up the middle he didn't make a lot of plays going toward second (which has always been the rap against him). But what struck me when I actually had a chance to look at the Yankee BIP data was that the Yankees really "didn't" allow an unusually high number of hits up the middle - and that they DID allow an unusually high number of hits on Jeter's other side. That's when I started watching video and realizing what was going on.

-- MWE
   724. fra paolo Posted: December 27, 2011 at 04:27 PM (#4023861)
The upshot of this alignment is that Yankee fielders tended to be penalized more heavily for hits allowed in every system, because those hits allowed were usually in areas of the field where other teams would be making plays.

More grist for my sceptical-of-zone-based-systems mill.
   725. Mike Emeigh Posted: December 27, 2011 at 04:44 PM (#4023867)
More grist for my sceptical-of-zone-based-systems mill.


Well, I don't want to overstate the case here - the net impact could not have been nearly large enough to raise Jeter to the level of Gold Glove quality defense, or even to the level of being above average defensively. There WERE plays not being made that an average shortstop would have made, even given Jeter's positioning - the "past a diving Jeter" meme isn't just an invention. But it strikes me as peculiar that a defense that was generally above-average overall could have fielders at key defensive positions that were as bad as the systems were showing Jeter and Bernie Williams to be.

-- MWE
   726. Mike Humphreys Posted: December 27, 2011 at 05:04 PM (#4023877)
Mike,

I may be able to report numbers in a day or two summarizing Yankee defense using updated DRA for all the Jeter years except 2000-02. The Yankees during that span were on average well below average on fielding. They had terrific pitching staffs.
   727. Mike Humphreys Posted: December 27, 2011 at 05:08 PM (#4023879)
Mike,

Also, do we really think the Yankees had this unique shift in batted ball distributions throughout Jeter's career? I did a new WOWY analysis for Jeter's pitchers, split by batter handedness. This would control for any bizarre but stable batted ball distributions. He was close to -500 plays on ground balls.
   728. Sweatpants Posted: December 27, 2011 at 05:28 PM (#4023885)
I think that in time DRA/Wizardry will shift the consensus career valuations for a couple of dozen historically significant players by between five and fifteen career WAR.
Who are the other players whom this will most affect?
   729. Mike Humphreys Posted: December 27, 2011 at 06:52 PM (#4023917)
Sweatpants, have you picked up Wizardry? It just made a very nice list: http://math-blog.com/2011/12/16/interesting-mathematics-books-2011/
   730. fra paolo Posted: December 27, 2011 at 08:06 PM (#4023951)
the net impact could not have been nearly large enough to raise Jeter to the level of Gold Glove quality

Of course. But some of what you discover here would be less of a problem if the zones were not too small. And working from raw data like RZR's OOZ would probably be more useful in assessing the actual quality of defence of a team like these Yankees.
   731. Mike Emeigh Posted: December 27, 2011 at 09:49 PM (#4024007)
From 1998-2000 the Yankees overall were quite good at preventing hits on balls in play; in 2001-2002, not so much, but overall for the period were generally above average (even discounting 1998, which was a season for the ages in all respects including defensively). Those were the only five seasons for which I had good-quality location data for BIP for the Yankees, so I don't know the extent to which the right-side skew has changed.

One issue with a WoWY analysis on Jeter (and for most regular players) is that except for 2003 (and last season) he hasn't missed much time at SS, and many of the innings that he has missed come near the end of blowouts, where pitching patterns and BIP distributions can be very different. You're talking, in a number of cases, pretty small sizes of "Wo" - Jeter played all but about 40 of the innings that Clemens pitched for the Yankees between 1998 and 2002, for example - and it's not clear that the "Wo" sample of innings is representative of the much larger "WY" sample.

-- MWE
   732. Mike Humphreys Posted: December 28, 2011 at 05:53 AM (#4024202)
The Jeter WOWY would compare Jeter against the scores of shortstops who also fielded behind Clemens when the latter was not pitching for the Yankees. Also, the WOWY I used eliminated from the sample instances in which the Jeter and non-Jeter samples were too small. I forget right now the formula I used, but it involved comparing two binomial means. Also, the WOWY results were consistent with the basic new DRA based described in The 2012 Hardball Times Annual. We are talking a decade and a half of numbingly consistent seasons.
   733. Joey Numbaz (Scruff) Posted: January 12, 2012 at 12:00 PM (#4035054)
Just wanted to bump this . . . right now I'm just doing this to estimate post 2005 . . .

Run an individualized regression for each player. Compare his career DanR WAR through 2005, season by season, with BPro and B-R WAR. I suppose I could add fangraphs WAR as well.

Use the resulting individualized regression equation to estimate DanR WAR from 2006-present.

Obviously this has some pretty major flaws and will become less useful over time, as players have fewer pre-2005 years.

It would be better to do this for each component (batting, fielding, base-running), but haven't really seen the need yet. Plus I'd need to figure out the SFrac (fraction of a season played) and replacement level by each player. And not all systems break out the components like Dan does, some including fielding and replacement level together, etc..

   734. David Concepcion de la Desviacion Estandar (Dan R) Posted: January 13, 2012 at 09:16 PM (#4036691)
Joe, I can email you the master spreadsheet and you should be able to figure it out and input all the data yourself if you'd like.
   735. Joey Numbaz (Scruff) Posted: January 14, 2012 at 01:50 AM (#4036912)
That would be great Dan, thanks!
   736. Joey Numbaz (Scruff) Posted: January 14, 2012 at 01:50 AM (#4036913)
Will you also be available for consulting services, should I need them (rather likely). :-)
   737. David Concepcion de la Desviacion Estandar (Dan R) Posted: January 14, 2012 at 10:13 AM (#4036968)
Yes, of course. Remind me your email?
   738. Joey Numbaz (Scruff) Posted: January 20, 2012 at 06:54 PM (#4041580)
Hey Dan, sorry I missed your request . . . it's in your g mail chat window . . .
   739. Bleed the Freak Posted: November 29, 2012 at 02:45 PM (#4312555)
On the Buzz Arlett thread, James Newburg discussed his reliance on DRA as his main defensive metric.

The basis for my position player rankings is ~75% Dan R(25% Old Dan - 75% weight to salary, 25% to PA) and 50% Dan R modified for DRA defensive metrics, and 25 % WAR (12.5% Rally and 12.5% Baseball-reference).

Below is a list of players with the largest fluctations in my valuation system between:
Player value using Dan's defense and using DRA ratings:

Huge positive swing with use of DRA:
Cupid Chlds
Joe Gordon
Keith Hernandez
Richie Ashburn
Fred Clarke
Tommy Leach - straddles PHOM, though Rally/BR aren't at all fans
Art Fletcher - moves to consideration set
Ed Delehanty
Frankie Frisch
Ivan Rodriguez
Bobby Veach - moves to consideration set
Jimmy Sheckard
Mike Griffin
Bill Dahlen
Harry Hooper
Luke Appling
Joe Tinker
Dave Bancroft - moves to PHOM
Andruw Jones - potential PHOM
Roberto Clemente
Sam Rice
Roy White - moves to consideration set
Buddy Bell - everyone should review his case - solid to excellent by the metrics available
Rickey Henderson
Todd Helton - potential PHOM
Joe Cronin
Bobby Grich
George Sisler - cements PHOM status
Bill Terry - straddles PHOM line
Bobby Wallace
Tony Phillips
Jose Cruz - moves to consideration set

Huge negative swing with DRA:
Chipper Jones
Derek Jeter
Duke Snider
Willie Stargell
Wade Boggs
Dave Winfield
Craig Biggio
Roy Campanella
Bill Freehan
Stan Hack - straddles PHOM
Vada Pinson
Ozzie Smith
Harmon Killebrew - straddles PHOM
Scott Rolen
Harry Heilmann
Sam Crawford
Joe Kelley
Roger Bresnahan
Amos Otis
Earl Averill - straddles consideration set
Chuck Klein
Mickey Cochrane
Pete Rose
Bill Dickey
Jason Giambi
Nellie Fox - makes HOM selection look even worse
Sal Bando
Gary Sheffield
Joe Medwick - moves to thick of consideration set
Edd Roush - makes HOM selection look even worse
Kirby Puckett

The reliance on DRA to compute Dan R WAR has resulted in the following large variances when compared against baseball-reference WAR:

Dan R/DRA huge positive ranking difference:
Barry Larkin
Gabby Hartnett
Alan Trammell
Gary Sheffield
Joe Cronin
Jimmy Sheckard
Tommy Leach
Arky Vaughan
David Concepcion
Yogi Berra
Lou Boudreau
Pie Traynor
Paul Waner
Heinie Groh
Fred Clarke
Hughie Jennings
Jim Edmonds
Elmer Flick
Bert Campaneris
Tim Raines
Tim Salmon
Darrell Evans
Eric Davis
Luke Appling
Mike Piazza
Robin Yount
Rabbit Maranville
Brian Giles
Dick Bartell
Bill Dahlen
Bill Dickey
Mark McGwire
Max Carey

Baseball Reference WAR ranking significantly higher:
Sal Bando
Brooks Robinson
Kenny Lofton
Carl Yastrzemski
Andre Dawson
Pete Rose
Edgar Martinez
Duke Snider
Roberto Clemente
Larry Walker
Ron Santo
Vada Pinson
Ken Griffey Jr.
Paul Molitor
Wade Boggs
Mike Tiernan
Andruw Jones
Ken Boyer
Kirby Puckett
Willie Davis
Craig Biggio
Harry Heilmann
Earl Averill
Buddy Bell
Jake Beckley
George Davis
Bobby Abreu
Billy Hamilton
Tony Perez
Nellie Fox
Cesar Cedeno
   740. Bleed the Freak Posted: June 25, 2013 at 11:32 PM (#4478028)
Hey Dan,

If you get a chance, can you dive into how Heinie Groh is significantly more valuable in your system than the other WAR proponents...the others see him well short, while he easily makes it in your analysis...he is solid by DRA, but maybe not as high defensively as your metrics.

Thanks,
Bleed
   741. Bleed the Freak Posted: June 26, 2013 at 01:02 AM (#4478057)
Would love to also see why Joe Sewell comes out well and what your stance is on Andruw Jones.
   742. Sunday silence Posted: August 30, 2013 at 01:32 AM (#4531036)
The rewards that Yankee fielders got for making plays, on balance, didn't counter that effect primarily because they had fewer balls in play than did most teams (they had a lot of strikeout pitchers).


People are argue about this effect all the time at least with me, and I really dont see it as being very significant. I know there are GB or FB heavy pitchers, but not the whole pitching staff. So I looked at the Yankee pitching staff for the 5 years in question. Most years they had more strike outs than league average, below I express that difference as % of total plate appearances for that season. (E.g. most seasons each pitching staff will face about 6300 PA, so if your staff had 63 or so more KOs than average that would be 1%). So here are the 5 years in question:


1998 1%
1999 2%
2000 0%
2001 4%
2002 2%

These were done on the fly to be honest, but pretty sure that's correct for +/- 0.5%. So that's a grand total of 1.8% more strike outs per year than league average.

So how much could this strike out heavy NY staff effect fielding ranges? If say a SS range factor per game is 5.0, then this raw correction would be a + 0.1.
   743. Sunday silence Posted: August 30, 2013 at 02:35 AM (#4531069)
EDITED THE ABOVE

The rewards that Yankee fielders got for making plays, on balance, didn't counter that effect primarily because they had fewer balls in play than did most teams (they had a lot of strikeout pitchers).


People are argue about this effect all the time at least with me, and I really dont see it as being very significant. I know there are GB or FB heavy pitchers, but not the whole pitching staff. So I looked at the Yankee pitching staff for the 5 years in question. Most years they had more strike outs than league average, below I express that difference as % of total plate appearances for that season. (E.g. most seasons each pitching staff will face about 6300 PA, so if your staff had 63 or so more KOs than average that would be 1%). So here are the 5 years in question:


1998 1%
1999 2%
2000 0%
2001 4%
2002 2%

These were done on the fly to be honest, but pretty sure that's correct for +/- 0.5%. So that's a grand total of 1.8% more strike outs per year than league average.

So how much could this strike out heavy NY staff effect fielding ranges? If say a SS range factor per game is 5.0, then this raw correction would be a + 0.1.

OK so let's add this to Jeter's range factor to the years in question and we get:

1998 4.35 per 9 inn
1999 4.1
2000 4.2
2001 3.9
2002 3.9

Now if we compare him to the SS with the best range factors in this league during these years, these guys are topping out at about 5.1 (Valentin 2000) or 5.2 (Bordick 1999) with a bunch of guys posting 4.9 or 4.95 just about every year. (I omitted F Martinez's 5.67 in 2000 as perhaps an outlier)

So the best SS in the game are getting to 160+ more balls per year than Jeter if we take Jeter's down years, or if we are being generous and take his 4.35 are more accurate, they are still getting to over a 100 balls year more than Jeter. With corresponds to what in terms of ba? 160 pts of ba? assuming OBP and slug were identical.

My pt. is really not about Jeter, nor is it to be critical of Mike Emeigh who puts a lot of effort into his posts and I respect his insights. My pts are really these:

1) Stop talking about Fly ball pitching staffs or Strike out pitching staffs as if such things exist. Ok, they do exist a little, but use some numbers so we can quantify this effect.

2) Before you go all out and start arguing about TZ or UZR or whatever proprietary system is out there, just go back to the basic range factors and make appropriate corrections and start the conversation from there. These numbers seem quite stable over the long haul even over a full season they seem quite reliable.

3) Yes these basic range factors can be corrected. How would I correct them:

a) consider infielders who iniate Double plays . Mainly because starting a DP will count as one chance, but in fact that infielder may have been the reason for two outs. One has to make a judgment here, but with enuf eyeballs here it could be done. Mazeroski for example participated in a record setting no of DPs one year (I think 162), it might be reasonable to assume he initiated 1/3 of them and that w/o his presence maybe 50 less DPs would have been turned. you have to make some guesses here, but one can presume that Gene Alley or Bob Bailey or whomever was probably not the reason they turned so many DPs.

b) correct for FB/GB staffs or parks and for KO staffs all you want. but use real numbers. Sure there is an effect here. it's not nearly as much as a lot of you think it is. And it's not jiggering the range factor numbers as much as some people claim.

c) the other thing is that it seems that despite good intentions, a lot of people still seem to take the attitude that if something cant be measured well it probably doesnt matter. Or as Einstein said: "not everything that matters can be counted, not everything that can be counted matters."

I mean I saw this attitude a lot in Bill James's work, he didnt seem to acknowledge defense as much as he should have and the only reason I could figure was that the work on quanitifying it hadnt been done. But I still think reasonable attempts can be made by using basic range factor plus whatever reasonable judgments people had about these players. He never seemed to get that Tris Speaker was probably more effective than Cobb. He seemed enamored with Arky Vaughan who seems a terrible SS to me. He pays no attention to Richie Ashburn when talking about Duke Snider. Dom DiMaggio figures in none his listings. etc etc.

And I see the thing on BTF, the same thinking just carries over: people routinely rank CFer's and invriably: CObb....Speaker. this is ridiculous, just do the raw math. Or they list DUke Snider, no mention of Ashburn. Do you know how many frakin balls a year Ashburn is getting to over Duke Snider? I dont know, but it was a lot...

Those are my only points.
   744. fra paolo Posted: August 30, 2013 at 09:44 AM (#4531183)
correct for FB/GB staffs or parks and for KO staffs all you want. but use real numbers. Sure there is an effect here. it's not nearly as much as a lot of you think it is.

I kind of agree with this, and have great concerns that the park factor I currently apply to building an MMP ballot may be weighted too heavily.
   745. Sunday silence Posted: August 30, 2013 at 08:40 PM (#4531679)
I think for Outfielders (CFs mainly) once we correct for discretionary chances will be a long way to getting handle on that. Assists plus that XBase factor they have in baseballreference.com should then comprise the arm component.

THere are a lot of discretionary fly balls. It is probably some function of fly balls, e.g. 25% of flyballs or something.
   746. Mike Emeigh Posted: August 30, 2013 at 09:16 PM (#4531701)
Sure there is an effect here. it's not nearly as much as a lot of you think it is. And it's not jiggering the range factor numbers as much as some people claim.


Charlie Saeger and I spent a lot of time looking at real numbers in the late 90s and early 00s. The range of GBIP from the best to the worst team was about 2 GB per game (about 300 for the season) and it tended to repeat from year to year. The Yankees were in the bottom third of the league every year from 1998-2001 on GBIP, and while they allowed a few more they were still under league average in 2002. The Yankees also had a pronounced right-side skew - and they positioned their infielders accordingly. I must have watched 50 video games on ML that year; Brosius played off the line for everyone - which let him make at least 10-15 plays in the shortstop hole - and Jeter leaned toward the middle. Jeter had something like 50-75 fewer balls in his zone, every year, than every other shortstop (without regard to whether he'd fielded them or not, just the total number of in-zone balls while he was at SS). I wish I'd saved that data, I was surprised at how big the gap was.

FB/GB and K rate aren't the only things that affect range factors. Runners on base affect them - you can pick up assists and putouts (in the infield) from plays made by other fielders. Batter handedness affects them; ground balls tend to be pulled (about 60/40), fly balls tend to be hit the other way (about 45/55). And positioning affects them, especially on discretionary plays such as popups and short fly balls. Andruw Jones caught a good number of fly balls that infielders would catch on other teams because he played a very shallow center field, which he why he often had very good range factors but very poor zone stats.

Jeter was not a good defensive shortstop, but the metrics (all of them) foster a lot of misconceptions about how bad his defense actually was. Jeter's biggest problem was actually his throwing mechanics - when he was moving away from first base he couldn't get any juice on his throws at all (that's where the jump throw came from). When I looked at the breakdown on plays from 2000-2002, what surprised me most was that the balls on which plays were being made at a below-average rate were always to Jeter's right, toward the hole - and not to his left, which was where he was supposedly weak but where he actually made plays at a more-or-less average rate. That's when I started looking at video - and when I figured out what was actually happening on the field.

-- MWE
   747. Sunday silence Posted: August 31, 2013 at 02:43 PM (#4531944)
FB/GB and K rate aren't the only things that affect range factors. Runners on base affect them - you can pick up assists and putouts (in the infield) from plays made by other fielders.


i.e. you make the play at 2b on a runner coming from first. OK but what is the maximum amount it could skew from team to team? Not 300 plays a year, I wouldnt think?

Batter handedness affects them; ground balls tend to be pulled (about 60/40), fly balls tend to be hit the other way (about 45/55).


again, how much does it skew from team to team?
   748. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 13, 2013 at 11:29 AM (#4617398)
Copying from 2014 ballot discussion thread:

As a brief refresher, there are two reasons why my system is so much higher on SS than baseball-reference/CHONE's:

1. I use as a baseline the Freely Available Talent levels derived by Nate Silver in 2006, which represent the aggregate performance of major leaguers over age 27 making less than twice the league minimum. That study found that replacement SS were below-average fielders as well as poor hitters--a result not found at any other position, but intuitive since it's the hardest position on the diamond excluding catcher. BB-ref uses the Tango/Sean Smith position-switcher method, which I strongly object to because of its inherent selection bias. (Only below-average SS are used as utility infielders rather than playing SS full-time).

2. I track changes in the defensive spectrum by measuring the aggregate performance of the worst 3/8 of major league regulars in moving nine-year intervals. CHONE asserts axiomatically, with no evidence, that the average defensive value of IF and OF are equal, and then uses the position-switcher method at hard decade intervals (e.g. 1940s or 1980s) to allocate value between the positions in each group. My method shows that IF had more defensive value than OF from 1965-85, and that the vast majority of said value accrued to SS, while CHONE's both artificially reduces that IF value and redistributes it to 2B and 3B.

===========================

As a general rule, I do not trust the FWAA scores in version 1 of my WARP, which were just a re-scaled average of BP FRAA and Fielding Win Shares back when those were all we had (which was useful at the time, since there was valuable information hidden in Fielding WS that I was able to extract). Now that we have DRA, TotalZone, and SFR (which people really should look at in addition to the other two), there is absolutely no reason to pay any attention to my old FWAA.

But the post-1987 fielding numbers are still very good. In fact, I would not-so-humbly suggest that they remain the best out there for this period. They were derived by studying the relationship, separately at each position, between the play-by-play statistics available in each individual season from 1987 on (including Chris Dial's Zone Rating-based RSpt) and the average of the most granular play-by-play stats (UZR, DRS, and David Pinto's old PMR)--back in the years when UZR was based on the STATS rather than BIS database, when we actually knew significantly more about fielding quality than we do now. (MGL found the correlation between STATS-based UZR and BIS-based UZR was only 0.5 I believe). E.g., if in a given season (say, I dunno, 1992) the only stats available were RSpt, DRA, SFR, TZ, BP FRAA, and Fielding WS, then I would do a separate multiple regression at each position to get a best-fit equation between those 6 stats (dropping ones that weren't statistically significant) and the UZR/DRS/PMR average during the 2003-05 period when we had all of them. It turned out that the quality of the different stats varied strongly by position: I believe SFR was much better than TZ at first base, but not in the outfield, or something like that.

==========================================

BB-ref is using 600 total position player WAR per season; I use 2.1 wins per 162 games per position player, which works out to 536. So BB-ref position player totals will be systematically higher than mine by about 0.2 wins per year/4 wins per 20-year career.
   749. AROM Posted: December 13, 2013 at 11:51 AM (#4617423)
CHONE asserts axiomatically, with no evidence, that the average defensive value of IF and OF are equal


Not their defensive value, but their overall value. So if outfielders are 10 runs better as hitters, then infielders are assumed to be 10 runs better as fielders. Is this not approximately how you determine the relative value of a shortstop vs. a second baseman?

Correct me if I'm wrong, but aren't you using the weaker offensive numbers of shortstops to credit them with defensive value? In your own words then, asserting "axiomatically, with no evidence" that the overall value of 2B and SS are equal?

   750. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 13, 2013 at 01:52 PM (#4617530)
Ah yes, I misremembered/spoke/interpreted. Total value indeed. My mistake.

No, I don't make that assertion at all. Hypothetically, if over a nine-year period, every single infielder performed exactly at replacement level (i.e., SS hit 38 runs below league average, 2B 23 below, 3B 21 below, 1B 9 below), while catchers, outfielders, and DH's as a group averaged 20 runs above league average offense, then my system would value all infielders at exactly 0 WAR, and attribute all available WAR to the C, OF, and DH. Similarly, if over a nine-year period, all SS hit exactly 38 runs below league average and 2B as a group averaged 36 runs above league average offense, then the SS would be credited with 0 WAR and the 2B would get a little under 6 WAR each.
   751. AROM Posted: December 13, 2013 at 02:27 PM (#4617560)
In these hypotheticals you have exact clones at some positions, and a distribution at others. I assume that is how a 74 run gap between 2B and SS turns into just a little under 6 wins. So your replacement level 2B (worst 3 right?) is probably something like -20, with the average at +36 and a Rogers Morgan or Joe Hornsby at the top.

What happens if each shortstop is exactly 38 runs below average, and each 2B is exactly 36 runs above average? In this hypothetical all of these players are Rip-kin, playing every inning of every game for 9 years, so only 60 middle infielders are needed for the league.
   752. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 13, 2013 at 03:16 PM (#4617600)
Correct, the replacement 2B is -23, with the worst 3/8 probably being around -25, -23, and -21 or thereabouts.

I can't answer that question until I know the timeframe (and I was oversimplifying in my prior example). My system takes Nate Silver's 1985-2005 averages as a baseline, and then adjusts it for *changes* in the 9-year moving average of the worst 3/8 of regulars at the position. If you are keeping the 1985-2005 data the same, and suggesting that the worst 3/8 of 2B improved from 23 runs below average in 1985-2005 to 36 runs above average in a different 9-year period, then yes, my system would go haywire, and value all 2B at zero.

Fortunately, in real MLB there always is a distribution from best to worst at a position. By contrast, I would venture that it is not always true that the total value per OF slot is equal to the total value per IF slot.

And remind me how you place catchers, who don't position-switch, on your spectrum over time?
   753. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 13, 2013 at 03:26 PM (#4617606)
That said, I shouldn't claim that my method is foolproof over the course of MLB history, because it's not. It can't handle RF before the live ball era, who were often both weak hitters and had little defensive value--what I call the "fat kid plays right" in softball syndrome, in which good athletes are probably good at both hitting and fielding, and bad athletes good at neither, and so you stick your worst player in RF where the ball will never be hit to him since no one's left-handed, and bat him 9th for good measure. The worst-regulars approach incorrectly interprets this as meaning RF was more difficult to play than CF in the 19th century. I use an awkward work-around for this, keeping the gaps between CF and the corners steady before 1920, but a position-switcher method would certainly be preferable for outfielders in this era.
   754. AROM Posted: December 13, 2013 at 03:58 PM (#4617629)
For catchers, position switchers don't get you anywhere. I think it's mostly based on inverse of their batting stats, with some consideration of the demands of the position - catchers in the 50's didn't have to throw much, those in the 70's did.

Fortunately, in real MLB there always is a distribution from best to worst at a position.


Yes, but I wasn't the first to hypothesize about a position with no distribution - you did that in post #750. So let's stick with apples to apples. Let's assume a normal distribution for SS and 2B, and forget about the other positions - they are typical. The shortstop group averages -38, with the worst 3 being -95 or something, and the 2B average +36, with the worst 3 being -23. The average Kozma is going to have about the same WAR as the average Cano.

By contrast, I would venture that it is not always true that the total value per OF slot is equal to the total value per IF slot.


I agree. It's just a useful approximation since the position switcher sample is too selective. I don't trust positions switchers for IF-OF, or C vs. anybody, but I trust it more for LF-CF or 2B-SS than the alternatives. For me it comes down to what skills are similar. Infielders need to field a ground ball and throw accurately to first. Outfielders need to run and catch a ball.
   755. Blackadder Posted: December 13, 2013 at 04:18 PM (#4617653)
a propos of the Kenny Lofton comparison, two players I would like to see compared via dWARP vs rWAR are Tim Raines and Larry Walker. This is not a purely academic question, as they are fighting for a spot on my (hypothetical) Hall of Fame ballot this year. What a terrible mess the BBWAA has created.

Anyway, if you go back to post 160 of this thread, Raines has a sizable lead in Dan's "salary estimator", with a WARP2 advantage of ~80 to ~63; meanwhile, rWAR (or at least the current BBREF incarnation thereof) gives the career WAR advantage to Walker 72.6 to 69.1; approximating Dan's "salary estimator" by a simple sum of squares of seasonal WAR (valuing peak) gives Walker an advantage comparable to the one Raines enjoys in Dan's post.

So there is obviously a large discrepancy; I think I can close a lot of it, but can't quite get all the way:

dWARP2, and the numbers in post 160 they are based on, is STDEV adjusted, while rWAR is not. Walker obviously played in a higher standard deviation era overall than Raines. The rest of my comments refer to Dan's unadjusted numbers.

Defense is the biggest gap. The original version of dWARP gave Raines and Walker comparable defensive value, while TotalZone has a ~100 run advantage for Walker. Going to Dan's updated WARP spreadsheet, Walker goes from ~4.5 wins on defense to ~10.5, while takes him up to 71 dWARP1. Dan apparently has a slightly higher replacement level than AROM, so the fact that he is a few wins lower is essentially a perfect match.

Raines, however, still seems to just do better in dWARP than rWAR, as far as I can tell. Dan's updated spreadsheet only covers the later half of Raines' career, so I'll start by looking at the original numbers. Baserunning numbers seem almost identical, which isn't surprising. The big difference is career 4.8 fielding wins for dWARP, while TotalZone has -7 career runs. Interestingly, the later number exactly with the version of DRA in Mike's book. Dan's orginal defensive numbers aren't so great, obviously, but even if you knock off 5.5 wins Raines' is still at 74.5 dWARP1 vs ~69 rWAR, and the difference in replacement levels pushes the difference even further.

Comparing Dan's updated numbers from 1987-2005 to AROM's from 1987 on, Raines has 40.7 dWARP1 and 37.5 rWAR. Defense explains about half of that difference, but then the lower replacement level gives it back.

So there appears to be a ~5 win gap in opinion on Raines that is not explained by differences in defensive evaluation. I would be curious to see what the source of the difference is.
   756. David Concepcion de la Desviacion Estandar (Dan R) Posted: December 21, 2013 at 02:42 PM (#4622198)
I've posted the archive via Dropbox at https://dl.dropboxusercontent.com/u/67402381/Rosenheck WARP.zip
   757. Joey Numbaz (Scruff) Posted: December 23, 2013 at 01:00 PM (#4622925)
Dan, is it possible that for RF pre-liveball era, managers put a premium on throwing arm or something that isn't all that measurable/significant to the modern game - which would not show up in the range numbers? The theory that bad hitters are in RF *and* it requires the least defensive value doesn't hold much for me once you are in a position to chose the players that play with the best funneled to the top. There has to be another answer other than 'runt' theory (for lack of a better word). By 1876 or so there were plenty of players to choose from and the best players were mostly in the top leagues. There weren't any guys that couldn't play or anything like there are in little league or even HS today. If RF were worse hitters and fielders, they would have shuffled players around to a better equilibrium.

On the good side of the spectrum (I can never remember which is left or right), I can see how SS/C can have fielding replacement levels further below the typical "fielding replacement level is average" because those positions are much more determined by fielding quality than the others, which are mostly determined by hitting as long as minimum fielding skill for the position is met. But I don't see that happening on the bad side of the spectrum, that side would find equilibrium quicker.
   758. Lance Reddick! Lance him! Posted: December 23, 2013 at 02:11 PM (#4622962)
You're overlooking one important thing: selection for ability as a secondary pitcher. You don't get the choice of Belanger's glove or Luzinski's bat when you're selecting from the Kieschnick-Owings population. That's how you get a regular that both bats eighth and plays the least demanding defensive position.
   759. DL from MN Posted: December 23, 2013 at 07:28 PM (#4623184)
Is roster size an issue with pre-liveball RF? They may have run out of players and an out of position player would get RF.
   760. Mike Emeigh Posted: December 23, 2013 at 08:07 PM (#4623198)
If RF were worse hitters and fielders, they would have shuffled players around to a better equilibrium.


It's probable (IMO) that RF pre-liveball weren't truly worse fielders.

Ballparks were built to fit the dimensions of the lot and the surrounding streets, and they tended to be more asymmetric pre-liveball than they are today. Most of the time, the shorter dimension was in right field; parks like Baker Bowl, League Park, Forbes Field, and Ebbets Field (and even the Polo Grounds) were shorter in RF than in LF, sometimes by quite a bit. Asymmetric ballparks tend to skew outfield fielding stats, and the players fielding the area where the dimension is shorter tend to make fewer plays and to rank poorly in fielding stats. Zone-based PBP metrics don't adequately account for this problem, but it's only important in a few places today (notably Fenway and Houston LF and Baltimore RF). Non-PBP metrics are fitted to the PBP metrics, so they're going to reflect the same issue. When you go back pre-liveball, where most batted balls stayed in play and more ballparks were asymmetric (and the asymmetries were more severe), this issue becomes more of a problem.

-- MWE
   761. AROM Posted: December 30, 2013 at 02:16 PM (#4626484)
Asymmetric parks will depress range factors for some players at the position, but it won't make any difference as to the average rating for all fielders at a postion, which will be zero runs (give or take rounding errors).

I'm not an expert on the deadball era, but I would think that outfield defense was less important, relative to infielders, than it is now simply because more balls leave the infield. Just picking 2 years, 2013 and 1913, OF putouts have increased from 5.92 per game to 6.37 per game. This is despite strikeouts doubling in that time, from 3.9 per game to 7.6.

So all other outs, whether groundouts, air outs to the infield, double plays, caught stealing, runners out on the bases, would have dropped from 17.6 to 13.0 (to get to 27).
   762. Blackadder Posted: January 12, 2014 at 12:07 PM (#4636857)
OK, easy question: remind me what the right "rule of thumb" is for converting BWAA in the spreadsheet into batting runs above average excluding pitchers, so one can compare directly to most other reported run estimators. Obviously it will change as you go back through history and (relative) pitcher hitting improves; I think I recall subtracting something like .6*SFRAC for recent seasons, but I'm not sure.
   763. Joey Numbaz (Scruff) Posted: January 12, 2014 at 08:17 PM (#4637327)
AROM, might want to factor in strikeouts there too. If OF putouts are up .45, despite strikeouts probably doubling (just a guess), maybe more, I'd say OF relative to IF by that measurement has increased even more than you are showing.
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