You are here > Home > Primate Studies > Discussion
 
Primate Studies — Where BTF's Members Investigate the Grand Old Game Friday, March 21, 2003Ultimate Zone Rating (UZR), Part 2Adjusting for everything under the sun, or dome, or whatever the case may be. Note: A Primer reader, and researcher in his own right (Shorty), diligently pointed out to me a possible error in the methodology described in Part I of this series. Rather than the twopart (and confusing) process I used to determine the number of balls, and hence, runs, cost or saved by a fielder in each zone, he suggested that a onepart and much simpler process can and should be used. His suggested methodology is as follows:
To determine a fielder?s "balls cost or saved" in each zone, first the fielder?s out percentage is determined by dividing his "balls caught" (his outs) by the total balls in play in that zone (hits plus outs) when that player is on the field, and then subtracting the league outs in that zone (made by a fielder at that position only) divided by the league balls in play in that zone, and multiplying the difference by the total balls in play in that zone when that player is on the field. I know it still sounds a little complicated, but it is actually simple, straightforward, and obvious. More importantly, it may be more accurate than the old methodology. Basically it is a fielder?s simple zone rating in each zone minus an average fielder?s (at that position) simple zone rating in that zone, multiplied by the fielder?s total chances in that zone. As I stated in Part I, a UZR is essentially the weighted average of a player?s simple ZR in every zone on the field.
Since Shorty?s comments were posted on Primer, several other equally credible readers and researchers have suggested that the original methodology may in fact be better  for various reasons that I won?t go into. To be honest, I have gone back and forth between the two methodologies (and others) for several years now, and I?m not even sure anymore why I settled on the current one. Well, after some more rumination and sleepless nights, and invaluable help from Primer friends and colleagues, I have decided that Shorty is right  the simpler methodology is the more correct one.
At the end of this article, you will find updated unadjusted UZR runs (using the simpler methodology) for the 2002 NL and AL shortstops, as well as the corresponding adjusted results (also using the new methodology), which is the subject of this article.
Anyway, let?s get on with the UZR adjustments?
Since my original UZR came out several years ago, it has been suggested that there are a number of factors which might influence a fielder?s ability to turn a batted ball into an out other than what zone that ball is hit into. These are noted in Part I and are reiterated below:
Park Factors Actually, park factors have always been included in my UZR ratings. This year, I made some minor changes. I?ll explain what changes were made, how UZR park factors are calculated, and how they are applied to the UZR player ratings.
Originally, I used separate infield park factors for each of the infield positions, reasoning that what effect a park?s peculiarities had on one infield position (e.g., the condition of the turf, lighting, glare from the sun, etc.) might not be the same for another infield position.
At some point, however, I decided to use only one infield park factor for all infield positions. The reason for the change was twofold: One, this considerably increased my sample size ? thus the resultant infield park factors were more reliable. Two, I figured that the primary influence on an infield park factor was the condition (and type) of the turf. Given that, I figured that the infield park factor should be more or less the same for all infield positions. Right or wrong, that is how it is presently done.
For the outfield park factors, I have always separated the OF into three segments and assigned a separate park factor to each segment ? LF, CF, and RF. The LF segment consists of all 7L, 7, and 78 zones (see the retrosheet zones). The CF segment consists of all 8L and 8R zones, and the RF segment consists of all 89, 9, and 9L zones.
You could certainly make an argument for wanting more granularity in the OF park factors (and probably for the IF park factors as well), but as with all complex methodologies, you often reach a point of diminishing returns. As well, in determining the level of granularity in any methodology, there is always a balance that must be attained between rigor and sample size. In fact, I grappled with this issue (rigor versus sample size) many times throughout the process of adjusting the UZR ratings.
The final change that was made this year, in terms of the park adjustments, was that I used a larger sample size for each park (up to ten years), and I was more careful in accounting for situations which might have affected the park factors (such as changes in OF dimensions, fence heights, turf type, etc.). As you will see in the 2002 park factor chart below, I used data since the 1993 season, and treated a park as separate as long as no material changes were made. If a material change was made to a park (such as replacing artificial turf with natural grass, or changing an OF dimension), I treated the "renovated" park as a completely different park. If a change was made to the OF but not to the IF, I treated the renovated park as new for the OF park factors but not for the IF park factors (and vice versa). Thus, some parks (like Wrigley Field and Yankee Stadium) have 10 years of data that go into their IF and OF park factors, other parks (Turner Field, Coors Field, et al.) have less than 10 years for their IF and OF factors, while still others (like the new Comisky Park) have x years for their IF factors and y years for their OF factors (the OF dimensions in Comisky were changed in 2001).
UZR park factors are calculated in the same way that regular park factors are calculated. For the IF, the home (home and road teams combined) groundball out percentage is divided by 1/14th (or 1/16th, depending upon the number of parks in the league) of the home GB out percentage plus 13/14th (or 15/16th) of the road (again, home and road teams combined) GB out percentage. (Actually, the computer is programmed to use 1/15th and 14/15th for all leagues and years.) Road game data is of course for that team?s road games only.
For the OF, the same calculations are done, using flyball and line drive out percentages (FB?s and LD?s are treated as if they were the same) rather than GB out percentage. The same OF park factor is applied to fly balls and line drives. As I said earlier, separate calculations are done for the LF, CF, and RF zones in the OF. Errors in the IF and OF are treated as outs. A ground ball error park factor is also calculated, using the number of GB errors divided by the number of GB outs plus errors.
When all is said and done, for each "park", we get an IF park factor, a LF PF, a CF PF, a RF PF, and a GB error PF. I put the word "park" in quotes because, as I explained above, two different "parks" may actually be the same park with different dimensions and/or different turf.
The chart below contains the 2002 regressed park factors for all 30 NL and AL parks. These factors are used to park adjust the 2002 UZR player ratings. Park factors are regressed according to the size of the sample data.
The UZR park factors are applied in the same way that some of the other UZR adjustments are applied, and in the same way that most offensive park factors are applied.
(Note: Technically this is not the correct way to apply park factors  however it is close enough.)
When an out is recorded by a particular fielder, rather than crediting that fielder with exactly one out, I credit him with "one divided by the park factor" number of outs. For example, the infield park factor at Coors Field is .97, so for every out recorded by an infielder in Coors Field, he gets credited with one divided by .97, or 1.03 outs. Every out for every fielder is park adjusted in this way, depending upon what park the out was recorded in and what the corresponding park factor is for that park at that position. In other words, outs in a fielder?s home park are not the only outs that are park adjusted ? all outs are park adjusted. Batted Ball Speed This one is obvious, yet this is the first year that I was able to obtain the speed of every batted ball (since 1999), as judged by the same people ("stringers" I think they call them) who record batted ball type and location. By the way, all of my playbyplay data is courtesy of two independent sources. One is Gary Gillette and Pete Palmer and the other is STATS Inc. I believe that STATS, at least, uses three "stringers" for each game, and somehow combines their judgments, in order to reduce human error and bias.
Anyway, each ball in play is designated (by the "stringers") as hard, medium, or soft. For ground balls, the meaning of these designations is obvious. For bunts, fly balls, and especially pop files, it is not (things like height, distance and trajectory are considered for fly balls and popups). The important thing is that all of the "stringers" are reasonably consistent. From working with the data, I am fairly confident they are.
Here is an example of how the GB out percentages change in the various IF zones, depending upon the speed of the ground ball: GB Out Percentages by Zone and Batted Ball Speed
How the OF fly ball and line drive out percentages vary with the speed of the ball depends upon the OF zone. For example, softly hit fly balls are harder to catch in the short outfield zones. The opposite is true in the medium and deep OF zones. FB Out Percentages by Fly Ball Depth and Batted Ball Speed
How is the batted ball speed applied to UZR? Rather than using a park factor type adjustment, I opted to "split" each zone into six separate "subzones", and keep track of player outs and chances and league outs and chances separately in each "subzone". Why six and not three (soft, medium, and hard)? Well, I also "tacked on" the handedness of the batter, which is another important adjustment, as you will see later on. In other words, a fielder?s runs saved or cost is calculated six separate times for each zone on the field. I warned you that there were going to be lots of "rigor versus sample size" issues in the UZR adjustments! Batter Handedness Well, I already mentioned above that the handedness of the batter significantly affects the out rate in the various zones for both the IF and the OF. The reason for this is threefold: One, the positioning of the fielders change, so that for example, the SS catches more balls in zone 56 (the SS hole) with a RHB at the plate than with a LHB (he is presumably shaded towards the hole). Two, when a batter pulls a ground ball, it is a weaker hit on the average, and when a batter pulls a fly ball or line drive, it is generally hit harder and further (the opposite of the ground ball). Three, RHB?s and LHB?s, as a group, hit the ball differently, even after accounting for which side of the field they tend to hit to.
Here are some examples as to how GB outs in the various zones are affected by the handedness of the batter: GB Out Percentage and Batter Handedness
FB Out Percentage and Batter Handedness
LD Out Percentages and Batter Handedness
As I explained above, the way I adjust UZR for batter handedness is the same as the way I adjust for batted ball speed. I keep track of LHB?s and RHB?s separately. Pitcher G/F Ratio The ground/fly ratio of the pitcher also affects the GB and FB (not so much for LD?s) out rate. Basically, a ground ball pitcher allows ground balls that are easier to field and fly balls that are more difficult to field. The opposite is true for fly ball pitchers. The more extreme a pitcher?s G/F ratio, the more pronounced the effect.
Originally, I assumed that if I controlled for ground ball and fly ball speed, the differences would disappear (IOW, that ground ball pitchers allowed a greater percentage of soft ground balls, etc.). I figured then, that since I was already accounting for batted ball speed, I wouldn?t need to account for pitcher G/F ratio. I was wrong. Interestingly, and somewhat inexplicably, when I controlled for batted ball speed, the differences between ground ball and fly ball pitchers were still there and almost as pronounced as before.
(Also, when I controlled for batted ball speed, the differences between LHB?s and RHB?s did not change much either.)
In the following chart, FB pitchers had an average G/F ratio of around .8 and GB pitchers around 2.0. FB pitchers were around 25% of all pitchers with at least 100 PA in a season. Same for GB pitchers.
As it turns out, pitcher G/F ratios do not have much of an affect on a player?s UZR rating for one simple reason (actually two reasons). GB and FB out percentages are not that sensitive to a pitcher?s G/F ratio, most pitchers? G/F ratios are near average (around 1.4), and almost all pitching staffs have nearaverage G/F ratios (well, maybe that was three reasons). In any case, the way I adjust a fielder?s UZR for his pitching staff?s G/F ratio is to keep track of the average G/F ratio for all pitchers while the fielder is on the field, and then to apply this to his UZR rating  at the end. In fact, I simply adjust a player?s UZR rate (and then, eventually, his UZR runs) by .001 per .1 above or below an average pitcher G/F ratio. For example, if an infielder?s pitching staff had an average G/F ratio of 1.8, since this is .4 more than the average pitcher G/F ratio of 1.4, the infielder?s UZR rate would be reduced by .004 (since those pitchers presumably allow easier ground balls). This is admittedly a very course way to do an adjustment, however considering how relatively unimportant pitcher G/F ratio it is, I think it works just fine. Baserunners/Outs Finally, we get to the last, but certainly not the least, UZR adjustment. Each infielder?s (but not the outfielders) GB out percentage is significantly influenced by the baserunners and the number of outs (as with the other adjustments, this is not to say that fielders, as a general rule, have markedly different distributions of baserunners and outs ? in fact, they don?t, as you will see from a comparison of the unadjusted and adjusted UZR ratings).
This is mostly due to the positioning of the infielders (e.g., with a runner on first, the first baseman has limited range, with a runner on third and less than two outs, the infield may be playing up, etc.), and to a much lesser extent to the approach of the pitchers and batters (e.g., with two outs, GB out percentages tend to be higher across the board).
Here are the GB out percentages for each "set of zones" and for each of the 24 bases/outs situations: The "3" zones (all zones beginning with the number "3")
Overall: .513
The "4" zones (all zones beginning with the number "4")
Overall: .748
The "5" zones (all zones beginning with the number "5")
Overall: .566
The "6" zones (all zones beginning with the number "6")
Overall: .728
How are the baserunner/outs adjustments handled? Dare I break each subzone down further into 24 (the number of bases/outs combinations) more subsubzones? Not a chance! First I go through my tenyear database (9302) to determine the "adjustment factors" for each infield position and for each of the 24 bases/outs combinations. For example, as you can see above, a simple ZR for an average first baseman for 19932002 was .513. With a runner on first only, and 0 outs, however, it was .402. Therefore, the bases/outs adjustment factor for a first baseman and this particular bases/outs combination, is .402/.513, or .784. I use this adjustment factor for all outs recorded by a first baseman, regardless of the zone (yes, I know that each zone should have its own bases/outs adjustment factor, but I can only deal with so much granularity in one lifetime), and I apply the adjustment in the same way that I apply the park factor adjustments ? by dividing each out recorded by the adjustment factor. In the case of a first baseman who records an out with a runner on first base only and no outs, he gets credit for 1/.784, or 1.28 outs (remember  this is technically not the correct way to apply an adjustment factor ? but it is good enough, IMO).
Well, those are about all the nontrivial adjustments that I could think of. If anyone comes up with any more, please keep them to yourself! I still have to crank out the rest of the 2002 revised (new and improved) Superlwts!
To get an idea as to how all of these adjustments affect a player?s UZR rating, here are the same SS charts I printed in Part I with the adjusted UZR runs added. Note again that I redid the original unadjusted ratings using the new methodology, as described at the beginning of this article, so that the following charts contain adjusted and unadjusted UZR runs using the new methodology only. 2002 NL SS UZR Data
2002 AL SS UZR Data

BookmarksYou must be logged in to view your Bookmarks. Hot TopicsLoser Scores 2015
(12  2:28pm, Nov 17) Last: jingoist Loser Scores 2014 (8  2:36pm, Nov 15) Last: willcarrolldoesnotsuk Winning Pitcher: Bumgarner....er, Affeldt (43  8:29am, Nov 05) Last: ERRORJolly Old St. Nick What do you do with Deacon White? (17  12:12pm, Dec 23) Last: Alex King Loser Scores (15  12:05am, Oct 18) Last: mkt42 Nine (Year) Men Out: Free El Duque! (67  10:46am, May 09) Last: DanG Who is Shyam Das? (4  7:52pm, Feb 23) Last: RoyalsRetro (AG#1F) Greg Spira, RIP (45  9:22pm, Jan 09) Last: Jonathan Spira Northern California Symposium on Statistics and Operations Research in Sports, October 16, 2010 (5  12:50am, Sep 18) Last: balamar Mike Morgan, the Nexus of the Baseball Universe? (37  12:33pm, Jun 23) Last: The Keith Law Blog Blah Blah (battlekow) Sabermetrics, Scouting, and the Science of Baseball – May 21 and 22, 2011 (2  8:03pm, May 16) Last: Diamond Research Retrosheet SemiAnnual Site Update! (4  3:07pm, Nov 18) Last: Sweatpants What Might Work in the World Series, 2010 Edition (5  2:27pm, Nov 12) Last: fra paolo Predicting the 2010 Playoffs (11  5:21pm, Oct 20) Last: TomH SABR 40: Impressions of a FirstTime Attendee (5  11:12pm, Aug 19) Last: Joe Bivens, Floundering Pumpkin 

Page rendered in 0.7935 seconds 
Reader Comments and Retorts
Go to end of page
Statements posted here are those of our readers and do not represent the BaseballThinkFactory. Names are provided by the poster and are not verified. We ask that posters follow our submission policy. Please report any inappropriate comments.
1. tangotiger Posted: March 21, 2003 at 01:48 AM (#609817)I can't say how grateful I am that the finetoothed combing of fielding has finally reached a new level.
I see that really only the Colorado shortstop is heavily affected, while the others are slightly affected (3 or less runs).
One note regarding PF: note that a 1.0 in 1993 might not be the same as 1.0 in 2002, since the parks themselves have changed, or even created. A park in 1993 might be a 1.01, but become a .98 in 2002, because a PF is relative to other parks in the league. To solve this, if you wanted to, is one of two ways: 1) the delta approach, as we've discussed many times for other things like age adjustments, or 2) look at only the parks that have not changed from 93 to 02, and make that the baseline 1.00, and make all parks relative to that baseline. But, that's probably just quibbling.
Very very enjoyable.
Since MGL said the relationship is about .001 outs made per .1 change in ratio (not the best way to do it, since your ratio will be .50 or 2.00 depending on what your denominator is. I would choose "rate" as in FB/(FB+GB) ), then we should see something like an extra .005 to .010 outs made per BIP. At 500 BIP, that's 2.5 to 5 outs, or about 2 to 4 runs.
This information will be much much more valuable for a pitcher than for an individual fielder.
Whether that difference is due to mistake pitch or because of positioning or other factors makes it clear though that we are talking about a "little thing" effect.
Essentially, DER = UZR + PZR on a team level.
Now, all you have to do is apply the same process, but switch fielders for pitchers. Solving for this will lead you into proof for DIPS.
If it is  then just save it.
Bordick: I assume that those numbers are /162 GP, and therefore, Bordick's performance needs be pared down.
"All these numbers  isn't this the same system that said Mo Vaughn was better then JT Snow last year? If it is  then just save it."
Yes it is! Jim, what would the point of an objective "system" be if it only reinforced what it is you think you already know? I think it was Tango who said that a good system (offense, defense, whatever) should coincide with what you think you know 80% of the time and you should be surprised 20% of the time. I don't know if I am doing justice to his statement and obviously no one knows what the numbers are (80/20, 85/15, etc.), but I agree with the general concept. On the flip side, just because a system follows that patter, doesn't make it a good one of course. That is just a quick and dirty "check" on the sytem right off the bat. Implicit in that 20% (or whatever percentage), is that some smaller percentage will be REALLY surprising (like Snow, T. Hunter, maybe N. Perez). Whether that means that the system is "wrong" with regards to those players, I have no idea. I doubt it. I think it either means that these players' true defensive abilitites are somewhere in between (the objective rating and the subjective consensus), or that these players are the ones, for whatever reasons, LOOK good but really aren't. I suspect it is a little of both, but I lean towards the latter theory (since it's my objective rating!). Seriously, I lean towards the latter, since after all, that is the whole point (or one of them) of these objective ratings  to identify those players whose defenewive abilitites we CAN'T, again, for whatever reason, nail down by observation.
For the record, when the smoke cleared, Snow's 02 adjusted UZR runs was 9 (11 per 162 "games"). Mo Vaughn's was 15, or 25 per 162 "games". So for you Snow fans, while Snow's performance was bad (hey, I don't make up these numbers  I just report them  that's how many ground balls Snow did or didn't catch in the various zones, etc.) in 02, Vaughn was over twice as bad! Vaughn is indeed a statue at first and was the worst first baseman in the NL last year (McGriff, who used to be good, was second at 22 per 162), and should, of course, be a DH (like F. Thomas, who has 27 UZR runs per 162 at first in 59 games since 1999).
As far as Snow, someone suggested that he was hurt last year which could easily have affetced his range, although he has no great UZR years going back to 1999 (he was a +3 in 01, 123 in 00, and a 1 in 99. The subjective consensus also seems to be that he is a wizard at catching bad throws. The low number of errors by the SF infield supports this view. UZR does not measure that skill at all.
BTW, the numbers in the article are absolute runs and not runs per 162, so they have to be taken in the context of the number of games or the number of chances. Also, the "games played" column in the article is the actual number of games played which I took off of the ESPN defensive stats website (the same place I got the range factor (RF) and the STATS ZR from in Part I). Normally, I put down in the "games" column the number of a player's chances divided by the league average chances per game at that position. So for example, Izturis' 9 UZR runs in 290 chances is a much better performance (ratewise) then Hernandez' 10 runs in 469 chances.
As far as Bordick being "better" than ARod, apparently last year he was (again, I don't make up the numbers  just report them), at least in terms of fielding ground balls in the various zones and not making errors, which is what UZR measures. Keep in mind that we have fairly large sample error especially in those oneyear samples. For example, if I were to use a (fulltime) oneyear sample to estimate a player's "ability" I would probably regress on the order of 50% (at least). So Bordick's one year UZR runs of 19 might correspond to a "true" UZR runs (ability) of 9 or 10 (in 116 "games), which is around 1214 per 162. Of course, if you want to know more about who is "better" in terms of ability or projection for this year, you would look at multiple years to increase sample size. In 01, Bordick was +5 per 162 and ARod was +6. In 00, Bordick was +8 and ARod was +14. Finally, in 99, Bordick was +21 and ARod was +1. So it looks like Bordick is indeed a great defensive shortstop, which is amazing for someone at that age (although good hands will remain more stable with age than good range, I assume). ARod looks (from his UZR runs) like a great defensive SS as well, or at least a very good one. As far as who would be better this year (I think Bordick retired, did he not?), it's probably a tossup. Given Bordick's and ARod's age, I'd probably give the edge to ARod.
That brings up Tango's question about error range. I have no idea (am I supposed to?). I guess we can take the UZR rate and assume that we have a binomila distribution and go from there. For example, Bordick had 347 chances in 02 with a UZR rate of .815. Using the binomial formula for standard deviation (SD), we get a SD of 21 points on the UZR rate, which is around 7 balls (.021 * 347), which is around 5.6 runs. Since UZR is essentially the sum of the individual ZR's, in the various zones, and becuase of all the adjustments, we probably have a SD which is higher than that, maybe 7 or 8 runs (in those 347 chances). So at 2 SD's (95% confidence interval), that's an "error range" of around 15 runs! That's another reason for not "worrying" about unusual looking numbers like Snow's. It is entirely plausible that Snow's 02 numbers or even his 9902 numbers make him look bad entirely by chance. Becuase of this, it is completely acceptable to "narrow" that error range by using other independent measures, like observation and scouting.
While I have always insisted that objective measures for offense and defense are MUCH more accurate than scouting and observation, your objective measures are always "handcuffed" by error range (random variation). Observation and scoutng can help to narrow that range. In fact, the larger your sample (for your objective measure), the less important scouting and observation are (and, the more dangerous they can be  I'm actually with Mike Gimble on this  for a veteran player, on whom I have lots of data  I don't even want to look at him  case in point is Tex  they have to LOOK at him in Spring Training to see if they want him to be their everyday player (according to GM Hart)?  God help the Rangers if he happens to get lucky or "look good" during the Spring). Most of us know this intuitively, but it is a very important concept nonethesless  in fact, it is one of MGL's rules!
BTW, I changed my UZR program to use 4 years worth of data, both leagues, for the baseline (league) numbers in the various zones to see how that would change each player's ratings. The idea was to increase the sample size for each zone, since remember I am splitting the data in each zone into 6 pieces (and no one complained about that?)!
IOW, the way the program works right now, Bordick's 02 "outs/BIP" in the "56" zone is compared to the league (for the AL in 02) average "outs/BIP" for all SS's in zone "56". The other method is to compare Bordick's "outs/BIP" to 4 years' and both league's worth of SS "outs/BIP" in zone "56". At the end, I do an across the board adjustment to make sure that all SS's UZR runs add up to 0. USing the old method this happens automatically. Using the new method it does not. For some reason I am afraid to "rock the boat" and use the new method even though it should be much more accurate (basically it should reduce that "error range" for each player's UZR runs, by reducing the range of sample league ZR's in each zone, by using more (8 times more) league data).
BTW, I appreciate that Tango and the other "ratio adjustment guys" didn't scream at me for the way I applied the adjustments. Doing it the "right" way woulkd have been a programming nightmare. ALso, yes I really fudged the pitcher G/F adjustments. At the very least, as Tango suggested, GB/(GB+FB) is better than using G/F ratios...
Mike Bordick is Ozzie Smith?
Yeah they are numbers and it took a million crazy calculations to figure them out  but they look pretty worthless.
I know you guys love to pat each other on the back but come on  trying to apply numbers like these to the 'real world' and speak about them as truths is why so many mainstream baseball types are so skeptical of statheads.
On the other hand, if you do have a high yeartoyear correlation, do you think that means anything?
There's a reason that Bordick converted all those plays into outs. MGL has considered where the ball was hit (close to him or not), whether the batter was lefty/righty, whether the pitcher was gb or fb, the base/out situations, and the speed of the ball. Is there something else that we should look at? If the results are that far out of whack from our perceptions, then either our perceptions are wrong, or there's alot of noise in the data. Then again, if there is alot of noise, we'd expect low yeartoyear correlation. Let's wait for the results of that.
***
Shorty: I suspect that the low outconversion rates for those short soft flyballs is because they are low flyballs. What we really want, more than anything, is hang time. Not sure why only football keeps track of this.
***
MGL: the error range is more than basing it on the binomial distribution. Every time you apply an adjustment factor, that factor itself, based on the sample size, is subject to error as well. As you noted, it's a balancing act. I suspect that the more factors you introduce, the greater the range of error, even if the overall error is reduced. Not sure if I'm saying it right, nor how to figure it out.
***
The 80/20 rule belongs to Bill James, but I subscribe to it.
In any case, I think that the binomial model gives us a pretty good idea as to the standard error (the measurement error). The fact of the adjustments increases the standard error somewhat, but I don't think by much. I could be wrong though. For example, the difference in the standard errors of BA and OPS are great. It is more than twice as large for OPS than for BA.
However, even the measurement error does not tell us the "error range" in terms of estimating defensive ability from the UZR results. Those are two different (although related) things (accuracy versus reliability?). For example, we could come up with a totally worthless system (even more worthless than Jim thinks this system is), which would have a finite standard error, even very small, for a large enough sample, yet the "error range" in terms of how well it estimates a fielder's actual ability would be totally different. If that makes sense...
Anyway, I am going to try and address some of Shorty's, er MAH's, comments, and then I'm going back to work (on all my other baseball stuff). I sure wish that his comments were like his (previous) moniker! BTW, can we call you "The poster formerly known as Shorty"? I do like MAH better though. It gives you a little more credibility. That's why I chose MGL, rather than the "the baseball guy" or something like that. Even if my stuff is totally worthless, as Jim thinks, at least my name gives me SOME credibility.
As far as regression, I do them by hand (I'm like a fine craftsman when it comes to regression)! Seriously, I do, when they are not that important. There are many ways to come up with regression formuals. One way is to do a regression analysis and use the resultant regression formula. You want to use, say one year sample data (say, PF's) as one set of variables and as many other years as possible combined as your other set of variables. If you wanted to know how much to regress, say 10 years of data, you would use those 10year, say PF's, as one set of variables, and another 10 (or some other time frame) years as your second set of variables, and again use the resultant regression formula. One problem (there are many) with this method (at least when it comes to park factors) is that you are assuming that all parks get regressed equally  not the case! For example, if Yankee stadium had a 1year PF of 1.08 and so did Coors Field, which one would you regress the most? Actually it is a trick question because it is not that you would regress each one differently (you woudn't), it is that you would regress each one towards a different number. Using regression formulas from regression analyses doesn't allow you to do this (unless you plugged other things into your regression and did a mutliple regression analysis). Another way is to take all the 1.03's (for example) for 1 year and look at what they are the next year (or next 5 years) as a group. If they are 1.02 the next 5 years, then that is your regression for all 1 year 1.05's. Again, this doesn't distinguish between parks. Anyway, a good rule of thumb is that you should better regress too much than too little ("It is better to see too little than to see something that isn't there!") If you look at my chart, you will see that all of the PF's are regressed aggresively toward 1.0, unless I have reason to think otherwise (like Coors, Fenway, etc.). Another good rule of thumb is that as long as you think that there is some "true" differences among your samples, you are ALWAYS better off using them with an aggressive regression, then not using them at all. That was way too much about regression!
As far as the differences between GB and FB pitchers, your theory of defensive platooning is interesting. Perhaps that is the only explanation for the differences in out percentages. I hope not! If it is, then I shouldn't be adjusting at all! Perhaps, as someone suggested, infielders are more "heads up" when playing behind GB pitchers, and the same for the OF'ers and FB pitchers (is the number of GB or FB chances per game correlated with GB or FB out %?).
As far as why "soft" fly balls are harder to catch in the short zones, it is simply that the out percentages in the various zones determine how soft, medium, and hard is defined apparently. Apparently a "soft" fly ball or line drive is one with a low trajectory. As I said, the definitions of soft, medium, and hard are not so obvious for fly balls and line drives amd popups. As I also said, it doesn't really matter how they derine any category, as long as all the "stringers" are consistent.
As far as real sow balls down the line being harder to turn into an out, I imagine that there is a subset of "soft" ground balls that become more difficult to field, but this subset must be small, and without having another category (like supersoft) there's nothing we can do about it. Also keep in mind that since I use league averages for every zone and for all 3 categories of batted ball speed, a slow ball in a short infield zone might in fact have a low out percentage.
As far as using GO/FO ratios rather than G/F ratios, I don't think there is going to be much difference (although I always wondered why the "official stat" was GO/FO ratio and not G/F ratio), even for extreme ratios. Let's say a pitcher has, say 200 GB and 100 FB, for a G/F ratio of 2.0. Because he is an extreme GB pitcher, he will have a .748 GB out% and a .695 FB out% (see the chart in the article). So his GB outs will be 149.6 and his FB outs will be 69.5. If a nonPBP databse uses these FO and GO numbers, they will think that the pitcher had 149.6/.735, or 203.5 GB's and 69.5/.702, or 99 FB's (based on an average GB and FB out% of .735 and .702), not too far off of the real 200 and 100. I'm not sure if that's what you meant.
That's all I can think of right now...
Let me first state that I am open to new ideas, I was blown away by DIPS and I am realtively openminded?
The first problem is that the information you are using is subjective. You might not think that there is a bias in this information but there inherently is. To draw a parallel from my own experience. I officate high school football. We have rule interpretation meetings where we watch NCAA sanctioned videos on everything from pass interference to unsportsmanlike conduct. They show us 15 plays and tell us what the call should be by NCAA ruling. We all watch the videos disagree with each other and then go out and call the games the way that we feel the rules should be interpreted. These types of biases will go into the PBP data, no matter what Stats tells the stringers to enter. I am assuming (I used to know someone that did it) that they still handle things the same way. The stringers are oftentimes at home watching on TV and many times do the same teams games over and over. Not only do you end up with just a difference of opinion on an event you get the bias of a person who has prejudices towards certain players. Oh, Mike Bordick is the best SS in the AL if he coudln't get to that ball. . . I'll enter my data accordingly.
The second great problem is that defense is not an individual aspect of the game. Unlike offense, on defense the players are completely intertwined  it seems impossible to take them out of a team context into an individual context. Is JT Snow really so bad at first, or does Dusty Baker just like to guard the lines? Is Jeter really that bad at short or did Brosius and Ventura 'steal' outs from him? What about injuries. Nagging injuries that won't keep a player out of the lineup may make a big difference as far as things like range, throwing ability and overall 'hustle'.
Just judging by the overall results  I don't know if they pass the 'Smell Test'. Is the best shortstop in the league really only worth 1 run a week or 1 run every 10 days over the worst shortstop in a huge offensive era? What do the results look like overall at the other positions?
Will there be correlation in future seasons? That might depend on how you define correlation I guess.
I do appreciate the effort and look forward to seeing the rest of the results, but at first look it doesn't look to me as though UZR is the holy grail that you are searching for.
Then again, I am biased. As I said on another thread, one of the biggest obstacles to seeing and utilizing the truth is human ego. So you may be right...
It strikes me that if this is not so it could be a devastating problem. We have, for example data that show significant deviations in umpires on balls and strikes, etc. However, they are rotated pretty fairly if not perfectly. But I personally doubt this is so for stringers, but it would seem to me that this is very much worth serious investigation and testing by the proponents of ultimate defensive metric systems.
Who knows, maybe the info is done by 3 people actually in the stadium, I don't know. I do know that people used to do it at home, and therefore would be watching on TV which in itself is a huge problem.
Do I appreciate the work you've done and think that there are some great ideas there. Yes. However when you mention that there might a 15 run error range then it's hard to put a lot of stock into what I'm looking at.
If defense matters as little as these numbers show  that doesn't really jive with what DIPS says  that makes defense look awfully important, UZR tells me that there really is no difference in major league shortstops outside of the extreme outliers.
Do you have the rest of the positions somewhere on the web or have you not published them yet?
When I've looked at fielding in the past, I can pretty much tell you that the range should be +/ 20 runs for each position. And while one player doesn't have that much impact, you could have a team, like the 2002 Angels, that can be worth +100 runs over average.
One thing I want to do with CAD is do a reconcile with UZR. Until we can improve upon things, we could assume UZR is "right" and then find out where there are differences, and why there are differences. Maybe teams that allow many walks screw with the fielding stats. Maybe teams that strike out many batters have fewer hardhit balls.
To some degree, fixing the numbers for how hard the batter hit the ball lessens this impact.
2. Your infielder plays on a team with poor pitching which has more runners on base more frequently. When holding runners on it shortens the infielders range. How does your rating system account for this?
The base/out table fixes this.
3. You have the wrong shift on because: a. You have a poor manager who doesn't read his reports; b. You have a rookie pitcher that likes to 2nd guess the pitch call or can't hit his spots. Instead of a fastball inside its offspeed over the outside of the plate. Your range factor is now off.
This could affect the results. I doubt it is an issue in the major leagues, or in the high minors, not nowadays. Surely not #b. If #b were true, pitchers would have a significant skill of not allowing balls in play to turn into hits. And thanks to Voros McCracken, we know that, if there is such a skill, it is very small.
4. You play behind one of those lanky kids that falls off to one side or the other of the mound and always screens you from the ball so you have less time to react.
See above.
5. You have a weak fielder (i.e. weak 3b to SS) & they play you deeper and over to help keep balls in the infield.
This is a big issue. At some point when evaulating a fielder, we need to look at the infield as a unit, and the outfield as a unit, or at least look at the fielder with the men next to him.
6. Your team has a knuckeballer.
Clear issue. You know, I wonder on what plays a knuckleballer has an advantage. Does he cause more hardhit balls? MGL, could you look at Wakefield, Candiotti and Sparks against their teams? Is there more or less of a certain type of play, or are some plays easier?
7. You have a poor fielding or stretching 1B. Your SS, 2B, 3B change the time they take with the throw to get it more accurate.
Clear issue. There is a difference, not a big one but enough of one to alter the numbers.
8. Your team is in a division with more bunters and you are doing your field rating on a 3B.
As I said above, I think MGL should count bunts alone, not with the other plays.
9. You have a poor pitching staff that loads the bases frequently forcing you to draw the infield in.
The base/out adjustment should fix most of this. Of more concern is the manager's tendency to bring in the infield.
For OPS, the data pairs were 1998 and 1999 and 2000 and 2001.
OPS, min 50 games for each year, r=.659, n=748. OPS, min 100 games per year, r=.748, n=324.
For UZR runs, the data points were UZR runs per 162 games, and also for min of 50 and 100 games. Also, if a player had no data pairs for consecutive years (because they didn't have the min number of games in one of those years), I used nonconsecutive years. IOW, some of the data pairs in the UZR data are for nonconsecutive years, but I never overlapped pairs (every data point is unique).
UZR runs, min 50 games for each year, r=.479, n=331. UZR runs, min 100 games per year, r=.528, n=190.
Here is the breakdown by position:
first base, UZR runs, min 50 games for each year, r=.278, n=56. UZR runs, min 100 games per year, r=.411, n=32.
second base, UZR runs, min 50 games for each year, r=.543, n=48. UZR runs, min 100 games per year, r=.484, n=31.
third base, UZR runs, min 50 games for each year, r=.606, n=48. UZR runs, min 100 games per year, r=.592, n=26.
SS, UZR runs, min 50 games for each year, r=.497, n=50. UZR runs, min 100 games per year, r=.526, n=34.
LF, UZR runs, min 50 games for each year, r=.672, n=39. UZR runs, min 100 games per year, r=.807, n=18.
CF, UZR runs, min 50 games for each year, r=.468, n=44. UZR runs, min 100 games per year, r=.396, n=25.
RF, UZR runs, min 50 games for each year, r=.20, n=46. UZR runs, min 100 games per year, r=.560, n=24.
I don't know what the various confidence intervals (for r) are relative to the sample sizes. Obviously for the indiviual positions, it is large...
1. I suppose MGL needs to add a batter/speed component.
2. MGL has baserunner/outs adjustments.
3. Yes, positioning is part of fielding. The player, not the manager, gets the blame in this system.
4. Screening from ball? Unless you play right behind him, what does this matter?
5. Yes, "stealing plays" and adjacent fielders will have an impact. A very valid point.
6. Knuckles: yes, that's correct. If we had more data, we would correct for that as well. For now, how much impact can 1015% of the innings have on some of the teams?
7. 1B: yes, valid point. We should classify our 1B as good or poor catchers,and make the adjustmens accordingly.
8. Bunts: how many could there be? I'm not sure if MGL counts or ignores this. I suppose then you need a batter/bunterLevel adjustment too.
9. Bases loaded: covered in #2 above.
Important point: the most impactful situations are covered by MGL's 5 points  park, baserunners/out, pitching staff, batter handedness, speed of ball. And all of that combined causes most players a swing of around 2 or 3 runs (with Colorado an outlier). How much more swing do you thnk you'll get with these lesser variables?
One thing to keep in mind. How much impact an adjustment has is dependent not only on the magnitude of the effect that that particular feature has, but on the liklihood that a player will have an anomolous "profle" with regard to that feature. For example, the pitcher's G/F ratio has a minimal overall effect because a pitcher's G/F ratio doesn't impact GB and FB out percentages all that much and it is unlikely that a player will have a pitching staff with a very unusual G/F ratio, even though he has only 4 or 5 pitchers he mainly fields behind (because pitchers in general do not vary all that much in G/F ratio, so 4 or 5 is enough to smooth things out). OTOH, while park effects do not impact GB and FB out percentages tremendously, a player is likely to have a nonaverage overall PF since he plays half his games in his home park. Anyway, the point I am trying to make is for something like speed of baserunners, while this may impact GB out %'s significantly, the likelihood of an individual player having an unusual group (speedwise) of batters while is on the field is small. The only reason why the likelihood of a fielder having an unusual group of batters, L/Rwise, is greater is because the handedness of the batters is related to the handedness of the fielder's pitching staff, which is only around 4 or 5 pitchers (starters, at least), so for example, you would expect Oak fielders to see lots more RHB's, since their starting rotation has lots of lefties.
So basically in deciding what adjustmenets to use, you want to factor in not only the impact of that asjustment on a single batter or runner or pitcher basis, but the likelihood that individual fielders will have unusual combinations with regards to those adjutments...
MGL, great break downs.
Mike Emeigh and I have discussed and disagreed about positioning. MGL's breakdown of LHB adn RHB seem to make MWE right and me wrong. I'd prefer a different set of zones and more specific breakdowns, but I don't think defense should be adjusted for that.
STATS scorers: Jim is right. STATS scorers typically score ofr one team. While the suggestion of bias is fair, I don't think it's correct. I was a scorer just as Jim describes, but he overstates the difficulty *and* the motives. Besides, you get "graded" on your scoring. They would actually pblish a "newsletter' with scorers' "Percent Right". Please note  "Right". With multiple scorers, biases get smoothed over. I can't doctor a player unless all three (+) scorers are doctoring the same player.
Softness of fly balls: well, they are softly hit. Mostly those "loopers" the 2B can't quite get to and the RF is sliding in trying not to get a knee in the face from the retreating 2B. Even GBs, you know what is hard medium and soft. Okay, that isn't always perfect obviuosly, but three scorers will smooth the biaes.
3B plays: Your breakdown of 3B plays is interesting. MAH notes that they are the opposite of what one might expect. I disagree. When a LHB hits one down the 3B line, it is either *hard* or *soft*. much lower percentage of "mediums" (MGL can actually crosscheck this). *IN addition* one *very* important aspect to this is MWE's ob re positioning (and a few others around USENET  I think DMN hates this part of it).
I think adjusting UZR based on factors is important. I think "type of play" is one  but I think type of play (zone/speed) mitigates the batterhandedness for the most part. And if not completely, I think unless there is an inordinate number of difficult plays, the adjustments should be very small.
I missed something  how did the COL SS get such a boost on a 3% PF?
Did he just get more "hard 56" balls?
I really like the PFs for COL and BOS LF. THat's some great work. I'm going to apply the PFs to my methodology and see what happens.
Thanks, MGL.
Oh  in my DPI system, I have Bordick at +20 runs prorated to 1430 innings. I need to finish the 02 rankings before 03 starts.. heh.
Some of the scores looked borderlineOK but I didn't see any really strong numbers. As a whole, pretty underwhelming evidence for UZR as a predictive tool for evaluating individual players. Hate to say this, but the scores reminded me of Voros' r scores for BABIP for indivudual pitchers from year to year (OK, maybe a little better than that, but not a whole lot)  and everyone seemed to regard that as prima facie evidence that BABIP was no way to evaluate a pitcher.
I wonder whether all the attempts to try to isolate a fielder's performance independent of his teammates' performance isn't somehow messing up the data. Even if the adjustment method *seems* logical, if it is too simplistic and fails to take account of the more subtle interactions between how fielders play together as a team, then you might just end up butchering your data beyond recognition, rather than *adjusting* it. Sort of like the cumulative error of every adjustment that Tango talked about.
Here's a reasonableness test. Is there a way to do UZR for the whole team, or the infield or the outfield as a group  basically a teamvsteam measure? If you did this, then you ditch all the adjustments for *sharing* balls between zones (would this be very different than DER?). Then, if a team or infield or outfield showed as being among the best, then you would expect their players to show as at least average to good relative to other fielders. If you didn't see that  in other words, you had a top fielding infield comprised of a bunch of supposedly weak to middling infielders (or viceversa)  then that would be suggestive, maybe, of some sort of a problem with the sharing adjustments that are happening with UZR. Maybe the White Sox are a team to look at in this regard  it would seem that they're regarded popularly as a team of weak fielders but, if I remember some comments from Part 1, your data suggested otherwise.
Thinking about the team UZR (or DER, if that's what it amounts to), I have a hunch that the *best* defensive teams may just be those with consistent average to aboveaverage fielders, BUT without any real liabilities (and maybe without many real stars  think Minnesota in recent years). In other words, the "greater than the sum of the parts" phenomenon. One liability on defense might significantly counteract the benefit of 3 stars, what with all the various, unnatural adjustments that a team might employ to try to minimize their exposure to their one big liability.
As for the sum of the parts comment, if MGL did the process as I think I read it, it should match exactly. That is, if the Angels individually come out to +100 runs, then as a team, without worrying about sharing, they would also come in at exactly +100.
One of the major reasons that the r is .50 rather than something higher, is that there are alot of interactions going on (pitcher, hitter, fielder). That adds up to alot of noise. And some fielders get alot less plays than others, meaning that their error range will be greater. It may very well be that this is the best that you can get, given the data. That is, given the level of noise, and the abundance of variables to consider, that an r of .50 is as good as it gets. That may be good enough for some, and not good enough for others. And if the quantitative approach isn't good enough, you'll have to rely on "good" scouting to make further adjustments, which is entirely possible, though highly problematic.
http://www.baseballprimer.com/articles/tangotigre_20030320_0.shtml
BABIP shows substantially less consistency for hitters than other stats. Here are yeartoyear correlation for hitters' stats 19902001... essentially, this describes how good a predictor a hitter's year is of his following year in each area:
BABIP: .476
AVG: .496
BB/(BB+AB): .808
SO/AB: .862
HR/AB: .809
For pitchers, it was:
HR/9: .470
BB/9: .692
SO/9: .776
BABIP: .218
**********
This is Tom. Seems to me that UZR is as reliable an indicator of fielding balls in play, as is batting average to a hitter as an indicator of making contact for a base hit.
The individual r's by fielding position are "worthless" (or at least misleading) in the sense that the samples are too small. However, the overall r of .500 and change (for min 100 games or so) seems to suggest that there is considerable predictive value. In fact, if UZR r is roughly comparable to BA r, that would also seem to suggest that there isn't a whole lot of "noise" in UZR, since I don't think we consider there to be a lot of "noise" in BA (in fact, I'm surprised that the BA r is not considerably higher). In any case, you can "like" .500 or "dislike" it, but to "dismiss" it does not seem right...
Yes, you are probably right that adjustments could be made for type of pitcher (power/finnesse, etc.). Maybe some time in the future I will add them. It could be that certain players like Womack get "punished" for having some unusual situation that UZR does not address. Who knows?
As far as Counsell, he may be a SS naturally which suggests that he would have better than average numbers at third, even if he were an average SS. I don't think he hits well enough to be a fulltime 3B'man (of course neither does M. Williams). It may well be that he could (and should) be a fulltime SS on many teams. I would find it hard to beleive that he couldn't do a better overall job than Womack, Guzman, Mears, or Halter.
Keep in mind (and I will repeat this until my dying breath) that these are SAMPLE results. They do not necessarily mean that they are indicative of a player's true fielding ability (sample error, sample error, sample error  the statistician's mantra), or if they are "indicative" we certainly don't know to any large degree of certainty, how indicative. In fact, it is almost a certainty that if you look at a whole list of sample UZR results, that you will see several players whose sample results are NOT particularly indicative of their true abilities (the ones that are 2 or 2.5 standard deviations off). Maybe Counsell is one, Snow is one, and T. Hunter is one. Their reputations suggest that they are...
If Uribe is already getting credit for these "hard" grounders, he may be getting double compensation for one factor.
If Uribe is already getting credit for these "hard" grounders, he may be getting double compensation for one factor.
Why didn't anyone else think of that?!
The only big change was in Colorado, as Ryan suspected. The sample IF PF for Colorado after controlling for ball speed was 1.00, rather than .96 (I had regressed the .96 to .97 for the PF chart in the article). Interestingly, the OF PF's changed dramatically. Whereas previously the sample OF PF's in Colorado were .89, .91, and .89 for CF, LF, and RF respectively, after the batted ball speed and LD/FB adjustments, the sample OF PF's for Coors were .99, 1.02, and 1.01, respectively. I guess that the low unadjusted out percentage in the OF was due more to the batted ball speed and more line drives than to the expansive OF. I guess also that the low IF gound ball out percentage was also due to more hard hit ground balls (there are more DP's in Coors as well), whereas in the other parks, when the IF PF's are due to the playing surface it doesn't "show up" in the batted ball speed. IOW, the stringers must rate the batted ball speeds relative to the playing surface or the playing surface affects the batted ball speed enough to affect the out percentage but not enough to affect how the stringers rate the speed. Or something like that.
Anyway, I am going to change the IF and OF park factors in Coors, which should primarily affect the Rockie players, of course. I'll have Tango update the files on his web site. Thanks again to Ryan for pointing out my mistake!
ALso, when I make the adjustments (LD/FB and ball speed) in the OF, the Fenway sample PF in LF is .97 rather than .81. I'm not sure why. I need to do some more research on this.
One thing I'm not too happy about is that since I only have batted ball speed data from 9902, my sample for the new PF's (with the adjustments) is much smaller than for the old PF's (4 years versus 10 years), especially for the old parks like Fenway. You'd be surprised how much variability there is in the defensive PF's, especially in the OF, when the sample size gets fairly small (less than 5 years or so).
Also, when I control for batted ball speed in the IF, the ARI IF PF is real low (.90), as compared to a previous factor of .95. Does anyone know whether the IF at the BOB is particularly short (fast)? Maybe that is one reason why Womack's UZR rating is so bad.
I am also a little concerned that when I control for batted ball speed at each park, things could get screwed up if we have "stringer bias", since I guess the same stringers tend to score the same teams...
"The percentage of ground ball basehits that stay in the infield (excluding bunts) could serve as an indicator of the speed of
the infield, thereby perhaps allowing you to group parks together for the purpose of calculating IF PF's."
That's a good idea!
BTW, there are 2 links for the UZR data on Tom's site. One is the "min 120 games" link and the other is a downloadable .csv file which has every player who had at least one chance in the field in 9902...
There really isn't much to check since we already know that all of the Ari players in the IF will have considerable fewer (10% less) GB out's at home as compared to on the road...
As I said, the complete file should include every player who had at least one chance (or maybe 1 out  I forgot) at any position during 19992002, regardless of whether they were still playing in 2002.
As far as some players not showing up, I'll check the ones you mentioned and report back. I'm also running "infield hits" park factors right now, which will be based on nonbunt infield hits divided by total ground balls...
I'm trying to run those "IF hits" PF's, but it is a little tough to define an infield hit the way the hit locations are coded. The hit location for a GB hit to the outfield is the IF section where it went through the IF and a GB hit to the infield is where the ball is fielded or where it stops rolling, so the only distinction is who fields the ball (an outfielder or an infielder). The criteria I used for an infield hit is a nonbunt hit that was fielded (picked up, knocked down, thrown to first too late, etc.) by an infielder (not an outfielder) that did not land or was not fielded in one of the deep infield zones.
I'm not sure this is going to yield anything fruitful..
Based on MGL's IF PFs, I would say IF PF is mostly a wash, and helpful, but not needed (but nice to have).
I have DPI numbers for all starters from 19982002, adn once the tweaking is done, I'll make a comparison to our two sets.
I have always used a "pitching staff correction" for OF. Perhaps MGL can tell us the difficulty average for teams behind good pitchers. (perhaps number Hard 3, medium 2, soft 1 to create a s/ number) I made mine based on SLG of nonHR hits. MGL, can you generate a "ptching staff" value for each team? Liek the avrage FB hit off Braves pitchers is 2.15, while the average FB hit off TB pitchers is 2.35 (10% higher). That adjusts by pitchers. Maybe you are saying you do this, but I missed that.
If you look at pitcher or pitching staff average hardness rates for OF or IF balls, I think that "Stringer" bias is going to show up, or at least be a serious problem, assuming that the same stringer or stringers tend to score for one team. I suppose you could look at road games only for all pitchers, but then you are cutting your samples in half. Kind of like only looking at a batter's road stats only to eliminate home aprk adjustment issues (which is not a horrible idea, BTW, for a player with a very long career)...
Even though a fielder playing on a turf field at home makes substantially fewer errors than a fielder on a grass field (a typical infielder makes 6.8 errors per 200 chances at home on a turf field and 7.2 on a grass field), that is completely balanced out by the fact that a fielder who plays at home on a turf field makes more errors on the road on a grass field than a fielder who plays on grass at home. IOW, if a fielder is used to playing on turf (at home), he has particular trouble with grass on the road, as opposed to a fielder who is used to playing on grass at home (he DOESNT have trouble with grass on the road). SO basically, all fielders, at least as far as turf and grass are concerned, have the same error rate on the home and raod combined, regardless of whether their home field is truf or grass.
I thought that was interesting!
In fact, I may scrap my error park factors, since most of it is based on whether a park is grass or turf, and because of the above "syndrome" I applied them incorrectly anyway (there would have to be 2 different error PF's for grass parks  one for the home players and one for the road players).
Here are the "OF hits per nonbunt GB" sample park factors for all parks since 1993. I am also including, for your reading pleasure, the "bunt hits per gb" and the "IF hits per nonbunt GB" park factors.
I guess this should give you some idea as to the "speed" of the infield. One of the interesting things is that even though past research has shown that turf and grass parks have around the same GB out percentage (actually turf parks are slightly higher in hit rate), it looks like considerable more GB's make it through the turf parks but that more infield hits and bunt hits occur in the grass parks.
Does this mean that slow GB hitters are favored in turf parks and fast GB hitters are favored in grass parks? IOW, if you remove the bunt hits and infield hits (which for slow runners and/or power hitters are presumably few and far between), then what is important as far as whether a ground ball is a hit or not is the speed of the truf, which seems to vary signficiantly from park to park, even though that is not evident from only looking at overall outs (or hits) per GB.
(I don't know how to do the formatting on these comments, so this is probably going to look horrible!)
The format is park, OF hit PF, IF hit PF, Bunt PF.
ANA, .96, .97, .92.
ANA2, .96, .90. .96.
ARI, 1.39, .69, .73.
ARI2, 1.33, 1.05, 1.03.
ATL, 1.07, .94, .91.
ATL2, .98, .92, .81.
BAL, .94, .91, 1.28.
BOS, 1.08, .93, .85.
CHA, .95, 1.08, 1.14.
CHN, .98, .99, 1.46.
CIN, .93, 1.00, .97.
CIN2, 1.04, 1.14, 1.49.
CLE, .87, 1.08, 1.26.
CLE2, .93, .99, 1.22.
COL, 1.17, .86, .64.
COL2, 1.21, .98, .87.
DET, .98, 1.14, 1.30.
DET2, 1.10, 1.01, .98.
FLO, 1.02, .82, 1.56.
FLO2, .97, .97, 1.05.
HOU, 1.07, .96, .92.
HOU2, 1.09, 1.11, .58.
KCA, 1.15, 1.00, .74.
KCA2, 1.03, .91, 1.11.
LAN, .88, .96, .94.
MIL, 1.04, 1.16, 1.05.
MIL2, .96, .99, 1.71.
MIN, .98, 1.07, .64.
MON, 1.08, .98, .8.
NYA, 1.05, .96, 1.06.
NYN, .90, 1.06, 1.22.
OAK, 1.03, 1.09, 1.09.
OAK2, 1.05, .93, .83.
PHI, 1.00, 1.16, .91.
PHI2, .91, .83, .85.
PIT, 1.03, .99, .90.
PIT2, .91, 1.15, 1.31.
SDN, .93, 1.08, .96.
SEA, 1.04, 1.00, .76.
SEA2, .87, 1.25, .92.
SFN, .89, .94, 1.25.
SFN2, .97, 1.06, .8.
SLN, .98, 1.03, .64.
SLN2, 1.01, .94, 1.07.
TBA, 1.02, .66, .93.
TBA2, 1.10, 1.07, 1.01
TEX, .94, .83, 1.05.
TEX2, .92, 1.00, 1.22.
TOR, 1.00, 1.09, .67.
Lots of fascinating stuff here! I hope some of you take the time to look at it and comment. There seems to be large variation in speed of the IF, as indicated by the OF hits per nonbunt GB and percentage of IF hits. The bunt hit percentage is curious. It seems as if batters do not like to bunt on turf whether or not the turn is seemingly slow like grass or not (cin, phi, SL and tor all have OF hit PF's of 1.00 or less, indicating slow turf, but the bunt PF's are all less than 1.00). I don't know whether it is still tough to bunt even on slow turf or the batters just think that they shouldn't bunt on turf.
The same thing is true on fast grass. Batters like to bunt on grass even if the grass is fast like turf (ARI2, CIN2, FLO, KCA2, MIL, NYA, OAK, SLN2). Not in all cases though. There are some fast grass parks where batters apparently don't like to bunt (ATL, ARI, COL, BOS, DET2, OAK2). I need to have another category for bnt hits per bunt attempt to see if it is indeed much harder to bunt on turf fields than on grass fields, notwithstanding the speed of the turf or grass. Of course, there will probably be large selective sampling there as only the best bunters may bunt on turf versus grass.
I'm fascinated anyway, especially since it's 5:00 AM and I have to be in school at 7:30 and I haven't been to sleep yet!
Here is some more interesting data (note: none of the following data includes Coors, as either a turf or grass field):
There are 50% more bunt attempts (for a base hit) on grass than turf, which is not surprising, but the bunt success (bunt hits per bunt attempt) is actually higher on turf than on grass (.484 to .461). There is of course a different subset of players who bunt on grass and turf (I assume) and since turf bunts are less frequent, they probably occur when they are easier on the average. Nevertheless, since the bunt success in general is so great both on turf and on grass, the data suggests that many more bunts could perhaps be attempted on turf. Do you think?
Also, the bunt success rate ON THE ROAD is higher for turf teams and their opponents, which suggests that, unless for some reason turf teams have better bunters, which I doubt, that fielders who regularly play on turf have trouble fielding bunts on grass. In order to test this hypothesis, I need to break down the "turf team road data" into the home and road team, rather than lumping them together like you do when you conpute PF's.
As I mentioned before, higher error rate on grass (around 5%), but it also looks like when turf fielders play on grass, they make even more errors, as compared to fielders used to playing on grass (just like the bunt thing above). Again, I need to split up the road data.
It looks like turf fields do indeed allow around 3.5% more hits thru the IF. The road data is the same so it doesn't look like turf fielders have more trouble on grass as compared to grass fielders, as far as hits are concerned (a hit is not an error  an error is an out).
Perhaps most interestingly, turf fields actually allow 7.5% more IF hits! Is this because of the "high chop" hits? Is it because infielders are forced to play back further on turf? Both? I would have thought it would be the opposite as the grass fields tend to slow down ground balls. Also, for the IF hits, it looks like the same problem for the turf fielders. They appear to allow more IF hits on grass fields than the grass fielders. Is this becuase they play a little too deep on grass fields? Is it because they just have more trouble coming in on slow hit balls  the same trouble they appear to have on bunt attempts?
Because of the problems that turf fielders seem to have on grass, the PF for IF hits is around 1.00 for both grass and turf parks, even though the IF hit rate is higher on turf than grass.
So really, the reason why grass and turf parks have around the same hit rate is because you get more hits on ground balls on turf but fewer bunt hits (because there are fewer bunt attempts). On top of that you have many more errors on grass. On top of that, how you do on grass depends on whether you are the home team and the road team is a turf team or not.
So now I need to figure out the lwts value of a ground ball in a turf park as compared to a grass park, including errors, and with and without bunts included (since some players bunt a lot and others never bunt)...
As far as the bunts, I try and exlcude all sac bunt attempts. The only thing I consider a bunt attempt (success or failure) is when there are 2 outs or the bases are empty. Everything else I "assume" is a sac bunt attempt (or course that is not always the case). I think this is a good idea though, although some "bunt attempts for a hit" are eliminated, all of the sac atempts are eliminated and most of the pitcher bunt attempts in the NL are eliminated so that NL and AL can be fairly compared as far as bunts are concerned. Also, I don't think there is much, if any, bias left in the bunt attempts I do keep track of (it's nice when you can remove a problem area and there isn't much bias or "selective sampling" in what's left)...
Yes, it is surprising to me that even if the BOB infield is very hard, that the OF hit PF would we that high. Remember that these are sample PF's, subject to random variation, and that ARI2 is based on only 4 years of data. Your suspicion that visiting players do not field well at the BOB could be correct. I suspect they may play too shallow since it is a grass field but plays like a turf field (even much faster). I'll check.
As far as the low OF PF's for such turf fields as Philly and Min, yes, that is surprising. Again, the "real factor could be 1.01 or 1.02. Given enough parks (there are 52 I think), some of them will have sample PF's far from their true PF's. Expect 2 or 3 (5%) to have sample PF's more than 2 SD's away from their real PF's, right? That's why I regress them all before I use them.
As far as Sea and Ichiro, you are sugesting that his infield hits contributes to the SEA2 IF hit PF, right? He should have just as many IF hits on the road as at home, so that should not affect the SEA2 PF...
You must be Registered and Logged In to post comments.
<< Back to main