Maybe this is a simplistic question, but why, in the determination of batting runs, is a double worth more than a hit and a stolen base? On the other hand, why is a hit and a CS worth any more than an out?

"Maybe this is a simplistic question, but why, in the determination of batting runs, is a double worth more than a hit and a stolen base? On the other hand, why is a hit and a CS worth any more than an out?"

1. The double advances any other baserunners farther.
2. The hit, even though the baserunner is later erased, has advancement potential that the out lacks.

Cheers,
Alan Shank

Mitchell, have you considered doing the fielding stats by comparing a fielder at home vs. visiting players, rather than applying park adjustments? This is Mike Gimbel's approach, and it has always appealed to me.
Cheers,
Alan Shank

The links to data files are now correct. Sorry about the error.

I'd like to say a big, hearty "Thank you" to Mitchell for posting this data. I have "sucked" it all into spreadsheets and will be doing a "Tidbits" sometime soon on some of the ramifications. What immediately jumps out at me is the large number of runs attributed to defensive play in the outfield, which agrees with Mike Gimbel's data. Summing up the UZR data from '98-'00, I find the greatest defensive contribution goes to Jermaine Dye with 89 runs, almost 30/season, while Ben Grieve cost Oakland 73 runs at the same position. That's a difference of 162 runs, or 54 runs/season.

Cheers,
Alan Shank

"Although UZR carries a pretty good year-to-year correlation coefficient (i.e. it is pretty reliable), there are still some problems associated with it"

Mike Cameron cost the Mariners 9 runs last year in center field but Darin Erstad saved 52? I'm not sure I believe that.

The problem with any zone rating - it's less of a problem with UZR than with straight ZR, but it's still a problem - is that zones don't move, while fielders do. A ground ball hit in a particular location might be fielded 50% of the time by the average SS, for example, but when you look at a team like the Yankees, who have a non-standard distribution of balls in play (with a right-side skew), that same ball might only be fielded 40% of the time by an average SS, who would normally be shading more toward the middle of the diamond. The fact that UZRs correlate year-to-year means only that they're internally consistent, and not necessarily that they're an accurate measure of fielding skill.

A bigger problem than that, though, is the idea that defensive failures can be allocated to "particular" fielders based on where the ball happens to fall. The vast majority of defense is positioning of the fielders to reduce the risk resulting from a defensive failure, and defensive positioning interacts in ways that aren't necessarily obvious. If a team has a weakness in RF, for example, the CF and 2B, at a minimum, have to adjust to cover that weakness to some extent - and because they have to adjust, the other fielders do, too. If a ball falls in LCF, who's to say that the CF might not have been able to make the catch if it hadn't been for that sluggard in RF? Should the RF get off entirely without responsibility, even though everyone else on the team was covering for him?

I'm of the opinion (heresy though it may be) that responsibility for defensive failures is rarely due to "just" the players in the vicinity of the ball. Defensice failures are almost always "team" failures, and most should be credited more or less equally to the entire team.

-- MWE

Mike, good point, and this is the problem with the "total" evaluations. The best thing to do is to publish as much data as possible so that we can look into the effects of all this.

Someone else asked how did those LWTS values work out, etc. Mickey explained he used real-life data. However, let me just explain it in a mathematical sense. The chance of scoring from 1b is 27% (more or less). This means that if you can get to 1B, you will have added .27 runs to your team. At 2b, it is .43, and 3b is .60. The "baserunner-advancement" value works out to .20/.30/.40 runs for the 1b/2b/3b-hr. So, if you simply add all this, you end up with LWTS values of .47/.73/1.00/1.40, which is very close to what happens in real-life. The reason that they don't add up exactly is because the distribution of these events does not occur evenly. If you want to get into the technical details, you can also see another explanation here: http://baseball.fanhome.com/forums/showthread.php?threadid=76367

This is the problem with the "positional adjustment". Basically, EVERY centerfielder is an above average defender on the field. But Mickey is comparing the defense of the CF to the defense of other CF. There's not much wiggling room between a great fielder and a good fielder. This is why you need a "defensive positional adjustment", something that I've been harping on for a while now. Otherwise, if you move Andruw Jones to RF, he will end up with a far BETTER value. This of course makes no sense.

Perhaps this isn't the best place to discuss this, but is there any correlation between team UZR and team outs per balls in play?

Correlation between team UZR and outs per BIP? UZR is outs per BIP with a little "slop" thrown in, so (if there aren't too many bugs in my calculations) the correlation should be near 1.

As far as Jermaine Dye and Darrin Ersdat and Bernie Williams and Ken Griffey Junior, etc., I have contended for a long time that it is difficult if not impossible to evaluate a fielder by observing him, even day in and day out. Of course, you can probably get some idea as to whether he is poor, average, or above average, but I guarantee that if G-d came down from the sky and told you exactly how good each fielder was, you would be very surprised at some of His evaluations and you would have made some egregious errors. It would be somewhat like trying to figure out a batter's exact offensive production (OPS, RC, lwts, etc.) simply by watching him and not knowing any of his actual stats. You might think that Piazza or Sosa, for example, were not as good as they are, etc. Because of the flawed nature of human nature, observational defensive evaluation is often based on things like number of great plays you observe, the perceived speed of a fielder, the "smoothness" of his catches (e.g. Andruw Jones, Bernie Williams, Jim Edmonds, Devon White), and his reputation. Some or all of these things can belie his true defensive ability. I much prefer objective measures, although I suppose that if you knew that these objective methods were imprefect (and they are), you could add some subjectivity to your evaluation. I would be ever so careful in doing so, however.

As far as the player who suggests using team defense (team defensive average, I assume, which is trivial to calculate) pro-rated among each player, while your arguments (one player influencing another) have some merit, that is like throwing out the entire family with the bathwater! Last year, the Cub infield had a team UZR lwts of -4 runs. Would you want to Mark Grace to share in that number (-1 runs). His own UZR lwts was +9. The Rockies had an infield UZR lwts of -8 runs in 2000, yet Neifi Perez' lwts was +12. Would you want Neifi to get his share of the -8 rather than the +12?

I have a few corrections for the article (so far). My thanks to several readers for pointing them out. First an innocuous typo: "That is where the 1.06 number comes from. (1.06 is the average of the double value for the NL and AL combined in 1998 through 2000.)" Of course, this should read .77 is the average of the double value...

More importantly, my description of the "moving runners over" super-lwts component was totally wrong. Without going into the exact methodology here, a batter gets around .1 run credit for every runner on second he moves to third on an out, above league average, and gets docked around .05 runs for every runner he "strands" at second, again, above league average. For some strange reason (Gremlins in my computer), I said .25 runs credited or docked...

Updated player ratings files (98,99,00, and 98-00) should be up on Tuesday. These are the same as before, but they include 2 more colums for each player - one for total lwts adjusted for position (I simply subtract the league average total lwts for each defensive position, including DH), and one for pro-rated - per 162 games - total lwts, adjusted for position. I also sort the players by total adjusted lwts in descending order.

I am also in the process of tweaking the UZR calculations. So keep an eye out for updated UZR's.

Thanks for all the comments!

I've posted Mitchel's updated data files. Hopefully, I'll be able to free up some time to play around with the data a little the next few days. Like most of my fellow readers, I'm very interested in this data and appreciate Mitchel's effort and generosity.

MShultz: you will be very happy to know that we've discussed EXACTLY this at Defense and Positional Adjustments, with Mr Lichtman providing the final piece to the puzzle, along with David, Warren, Patriot et al contributing greatly.

Jim,

The format for the updated files is a little screwed up. Would you check that please?


Here is some data that might be useful:

These are the normalized (1.00 is league average at that position) park-adjusted Ultimate Zone Ratings for every pair of positions for 1998 to 2000 (3 years). I looked at every player who played more than 1 position in any given year. The UZR's are the weighted (by number of chances) averages at each position for players who played (at least) those 2 pairs in any given year. The numbers in parentheses are the total number of chances at each position.

The first UZR and # of chances (the number in parentheses) refer to the first fielding position in the position pairs and the second UZR and # of chances refer to the second position.

Position pair UZR1 (chances1) UZR2 (chances2)

3/4 1.03 (628) .99 (3270)
3/5 .99 (620) .94 (4562)
3/7 1.01 (2453).97 (3490)
3/8 1.01 (37) 1.01 (554)
3/9 1.03 (810) .99 (1124)

4/5 1.01 (7159) 1.00 (5488)
4/6 1.02 (4262) 1.00 (4816)
4/7 .99 (1557) .99 (518)
4/8 .94 (107) .86 (60)
4/9 .97 (727) .93 (57)

5/6 1.00 (4654) .99 (5530)
5/7 .95 (1604) .98 (845)
5/8 1.23 (52) 1.06 (17)
5/9 1.03 (474) 1.07 (226)

6/7 .99 (1723) .98 (524)
6/8 1.00 (411) .88 (99)
6/9 .78 (101) 1.12 (14)
7/8 1.01 (12016) .99 (16697)
7/9 .98 (8603) .98 (7224)

8/9 .99 (15592) 1.01 (12054)

A reply to David's comments:

"I'm a bit confused by the positional adjustment being based on total lwts (Off+Def) at each position. Isn't the purpose of the adjustment to deal with the fact that the Def. super-lwts are (already) relative to position, while the Off. are not? Therefore, shouldn't the adjustment be in regards to the ave. Off. lwts at the position (only). Then both components would be relative to positional averages. Unless I'm mistaken (always a distinct possibility), that's what Palmer does."

David, you are not mistaken. The positonal adjustments should be based on the average OFFENSIVE lwts (including baserunning, etc.) at each position. What made you think that that was not the case with super-lwts and the charts that are included in the article? Even you included defensive lwts, they would average to zero for each position anyway.

"I'm also a bit confused by Mickey's philosophy with respect to baserunner movement during batting outs. Baserunners advance on outs in several different ways, yet he chooses to include only two of these instances--GDPs and "moving a runner from 2nd to 3rd with no outs". His reason for not including SFs (in their correct weight) is that a study showed that batters fly out in SF situations at their typical rate. But another study (by the same group) showed that batters also ground out in GDP situations at their typical rate. So, I ask, why the seeming inconsistency of approach? And what about all of the other baserunner advances on outs, such as a runner scoring on a groundout? By including only the "moving a runner from 2nd to 3rd..." category, Mickey seems to be implying that this event uniquely reflects an actual ability (to ground out to the right side), while the others don't. But where is the evidence that this is true? If batters make their normal kind of outs in SF and (especially) GDP situations, why would they not also do so in this, relatively less impactful, situation? Overall, in terms on non-value-added ability, would it not make more sense to use several different out values (for GO to the left side, GO to the right side, the same for FO, and then strikeouts), and simply forget about baserunner movement?"

Long comment, long answer.

This is important, so everyone sit down and get comfortable! There are two kinds of evaluations. One is what some people call "performance" or simply "value." You can call it whatever you want, I suppose. It is the actual run (and win) contribution that a player made (past-tense) to his team. We can even break down this type of evaluation into two different sub-evaluations - one would be their actual run contribution, and one would be their actual "win" contribution. (They are not the same; some runs are "meaningless," like all the runs scored after the go-ahead run is scored in a game. I put the word "meaningless" in quotations becuase I don't want o hear "The last runs has the same value as the first," etc. You all know what I mean by "meaningless.") The latter ("win" contribution is not so easy to evaluate and interpret as the former ("run" contribution), so let's forget about it for now. I only bring it up to illustrate there are many ways to conceptualize an "evaluation" measure or metric. Anyway, back to the 2 basic ways to "evaluate" a player.

What is the reason for doing a "performance" type (the first method) evaluation? I can think of two (ther may be more). One is to decide an MVP type award, and; two, is to simply apportion a team's runs among their individual players, for whatever that is worth.

So what are some of the ways to do a "performace" type evaluation. One is with regular lwts (or super-lwts). This is not very good for some of the reasons that David brings up in his "criticism" of super-lwts (don't worry; I can take it). Mostly it is because "all events are not created equal in an actual team contest." If a player only hits solo home runs, then clearly they were (again, notice the past-tense) worth only 1.0 runs to his team, and not 1.40. Ditto for sac fly outs and some ground outs, singles, doubles, walks, etc. Using league-average values for all events, and ignoring sac flies and IBB's, etc., does not give us a real good idea of exactlt how much a player contributed to his team's runs scored. OK, how about RBI and Runs Scored. While much maligned stats, these are not too bad in measuring a player's actual run contribution to his team. In fact, the only things that are missing are events which "help" a run to score, like a single that advances a baserunner who actually scores. And of course, for a HR, RBI and Runs Scored create a double counting problem (why DOES a player get an RBI for "driving himself in" on a HR? I guess someone has to!). Beleive it or not, if I wanted to evaluate a player's actual performance, in terms of actual (not theoretical) run contribution, I would prefer RBI plus Runs Scored over lwts!

Another method for evaluating "peformance" is the once-ballyhoed (sp?) "value-added" lwts. Frankly, I hate this metric. It is apparently an attempt to combine performnce and ability, and in doing so, it combines the "worst of both worlds." If you are not familiar with "value-added" lwts, for every individual event a player produces, rather than using a league average lwts value (like .77 for every double), you use the actual change in Run Expectancy (RE) before and after that particular event. Obviously, yoiu need PBP data to compute value-added lwts. For example, if a player hits a HR with no one on, he gets credited with 1.0 runs. With 2 players on, 3.0 runs. For HR's, that makes a lot of sense, and is basically the same as RBI plus Runs! But what about a single? If a player hits a single with no one on and 2 outs, he gets credit for around .13 runs, while if he hits a single with a runner on first and 1 out, and the runners advances to third, he gets credit for around .68 runs. (I am using the RE tables to figure these numbers.) Well, this is silly! If, in the first example (single, no one one and 2 outs), if the run never scores why should the batter be given any credit at all? The value added proponents will want you to beleive it is because that was (notice the tense again) the theoretical value of the single. Yes it was, but... if the run didn't score, then who cares? We certainly don't if we want to measure actual performance (run contribution to a real-live team). On the other hand, if we want to measure "hypothetical" value, or "context-neutral" performance, then we would want to use the average run value of a single, which is, low and behold, lwts! Value added lwts is a very bad "hybrid" metric in my not-so humble opinion!

What is the "correct" way, then, to apportion a team's runs scored. I'll give you the general idea, and leave the rest to you. First of all, you give no credit for anything that doesn't in some way cause a run to score (that's why a "performance" measure don't measure ability; if you hit a leadoff triple and don't score - tough luck!) Second (and last) of all, if a run or runs score, you figure out a nice, neat way to divey up the responsibility! That's it! If a player hits a home run with 2 runners on, he gets most of the credit for the 3 runs. The players on base get some of the credit. How much? I have no idea!

This leads us to what I think is the most intersting and useful evaluation method. That is "ability" or "context-neutral" performance, or the "Bob Dylan" metric, or whatever you want to call it. It is the theoretical run contribution of each of a player's offensive events to a random, hypothetical team (in a random batting slot, a random inning, against a random team, with a random number of outs, and a random number of baserunners, etc., etc.). This kind of measure closely resembles what we think of as "ability" (perhaps it defines ability) AND it is generally the best way to project performance (again, on a random team, BO slot, inning, etc.). That is why I find it much more intersting and useful than a "peformance" or "run contribution" measure. In fact, if I hear that Brett Boone (or whoever) contributed so-and-so runs to the Mariners in 2001, and it is a huge number, I am like, "So what? Is he realy that good? How much was due to circumstnace? Did he have lots of players on base to pump up his RBI?" When I get over saying "Wow! I didn't think he was that good, going into the season," all I really want to know is "How 'good' is he (compared to other players), and what will he likely do next year on the Mariners or some other team?"

In any case, given the definiton (MY definiton) of ability above, it just so happens that lwts, and specifically super-lwts is perfectly designed to give you the answer that you want (per the definition). If you want to evaluate something else, use a different metric!

So, for example, the above definiton PRECLUDES treating sac flies as anything more or less than a regular fly out (and yes, a fly out has a different lwt value than a K or a ground ball; actually the value of the GB and FB are almost exactly the same). This is because we can only assume that on a random team, every player will have the same nubmer of runners on third with less than 2 outs, and we believe that no player can hit more fly balls (or ALL players can hit more fly balls) in a sac fly situation. If some players can hit significantly more fly balls in a sac fly situation, then they SHOULD be given some credit for this ability, as it will "repeat" itself on a hypo, random team in the future, and it is part of those player's hypothetical run contribution to a hypo, random team. Ditto for grounf balls with a runner on third and less than 2 outs.

What about GDP's? If the number of GDP's a player hit into was dictated solely by the number of GDP opportunities, as has been suggested by Palmer and others, then I would ignore the GDP as I do the sac fly. But we KNOW this is not true. First of all, the more GB's per PA a batter hits, the more DP's he will hit into (per DP opp, of course). So why not just use a separate lwt value for GB's, FB's, and K's, as David suggests? We could, and that would certainly account for this aspect of GDP propensity. But there is another aspect! That is the batter's speed to first (foot-speed plus whether he is a RHB or LHB), and the speed of his ground balls! To be true to our defintion of "ability" we must account for this. The only practical way, of course, is to use a player's GDP per GDP opp, as part of our measure of his "ability." That is exactly what super-lwts does!

What about "moving runners over," which is the crux of David's criticism. First of all, "moving a runner over from first" is already counted in our GDP component. (I suppose I could include how often a batter is out at first and the runner advances to second, although if there is no "ability" involved their [i.e. if it is just plain old "luck"], then it violates our defintion of a metric that measures ability.) Second of all, moving a runner over (in) from third is assumed to be random (luck; no "control" by the batter, other than the fact that he isn't striking out). So what is left? Moving a runner over from second! David, that is why it is the only "runner movement" I include in the super-lwts components (BTW, I DO track it with 0 or 1 out, not just 0). Can't we say that moving a runner over from second, like from first or from third, is random (again, other than the fact that it is a non-K), therefore it should not be given any attention in an "ability" formula? No - for 2 reasons! One is that batters who naturally hit the ball to the right side (mostly lefties) tend to move more runners over - so even though it is not "intentional," they need to get credit for it, in order to be true to out defintion of ability! Two, is that some batters can and do "try" and hit the ball to the right side with a runner on second and 1 out. Since they reduce their overall production in doing so (and their traditional offensive lwts suffer), they need to get credit for "giving themsleves up," as certainly they will continue to do so with a hypo, random team in the future, and it is part of their theoretical value to any team at any time! Again, as David suggests, we could simply assign different lwt values to "K's," "ground balls hit to the right side," "fly balls to the left," etc. (I thought about it), however one problem would be that we would be "shortchanging" those players who went out of their way to make contact and hit the ball to the right side in those "runner on second/no out" situaions.

Probably the only thing I shoul'd have done was to separte outs into K's and non-Ks and then do the GDP adjustments. I didn't because I didn't want to make the super-lwts formuals any more complicated than they already were. In any case, know that, after you separately adjust for GDP's, the K's are worth about -.016 more than a non-K out, whereas, after you adjust for moving runners over, a FB and GB out are worth about the same (so there is no need to distinguish the two).

That's all folks!

BTW, "27 bases/outs combinations" should read "24 bases/outs conbinations, unless they added another baserunner situation when I wasn't looking (bases loaded, no one on?) Sorry if I drove anyone to drink!

BTW, in my last (long) post, it should read "runner on second and 0 out, not 1 out" (for a batter to "try" and move a runner over), among other typos...

David, as far as not realizing that the def. lwts total to 0 for each position, don't feel bad. I forgot the same thing when I read your post. At first, I thought, "Oops, I made a mistake (I thought I DID add in the defensive lwts to each positon's average total lwts amd that this was a problem). Then I looked at the average total lwts charts and noticed that I didn't do this and when I was writing the response, I went "Duh, it wouldn't matter anyway!"

As far as my explanation of what "matters" in supe-lwts, it is not that "it is my metric and I get to do anything I want." It is that I am only doing what is "commensurate" with MY DEFINITION of my metric, although there is certainly a LITTLE wiggle room in deciding WHAT is commensurate. Howver, MY definiton is not unique or unusual. It is the same definiton that is attributed to regular lwts - i.e. "ability" or "context-neutral" performace. The trick, in deciding what to include, and what to ignore, is REPEATABILIY. Whatever events or situations that were not average for a particular player, if they would be expected to "show up" in the future ON A RANDOM TEAM, then they MUST be included. If they would be expected to regress to a lwague average in the future (other than due to a small sample size in the past, then thet MUST be ignored. This basically boils down to any tangible quality or skill that the batter (whether "intentional" or not; it could be something innate, like whether he bats RH or LH) possesses.

That being said, if I did the formulas again, I think I might have separate values for GB and FB outs (and perhaps to the right and left sides), and K's, as you suggest, although this would present the problem I mentioned concerning "moving runners over." Of course, if I used separate values for GB and FB outs, I would only use the "speed" element of GDP's to make that adjustment...

Mitchell writes: "If, in the first example (single, no one one and 2 outs), if the run never scores why should the batter be given any credit at all? The value added proponents will want you to beleive it is because that was (notice the tense again) the theoretical value of the single. Yes it was, but... if the run didn't score, then who cares? "

Let's forget about runs. Let's look at wins, and the Win Expectancy Charts. Every event in baseball changes the EXPECTED win% of the game. At the start of the game, the Braves might have a 46.2% chance of beating the Yankees in NY. The leadoff hitter for the Braves hits a triple, and brings the EXPECTED chance of his team winning to let's say 51.0%. The next 3 batters get out, and each out brings the chance of the Braves losing down somewhat to let's say 45.1%. All of this is EXPECTED chances and percentages. But it is based on ACTUAL PERFORMANCE. In terms of value/performance, this method is totally perfect.

On the other hand, if it went 1-2-3, the win% would have gone directly from 46.2% to 45.1%. These 3 outs are LESS costly to the PLAYERS, because of their specific CONTEXT. By the end of the inning however, the sum of the 3 or 4 player will add up to -1.1 win%.

In terms of ability, this method does not work. Because ability implies "all things being equal, how would this player do". Therefore, you assume that the frequency of the "context states" is the same for all batters, and then you apply their "success rates". In the previous example, the "expected win%" is based on an "average succcess rate", but given the player's "frequency of states".

Mitchell is right though in that we should clearly separate the 2. And they both have their purposes in life. And as Mitchell pointed out, one is to look backward to what happened (value), and the other looks forward to what may happen (ability).

You're right, we CANNOT assume what I said. Nor can we assume what Mitchell is saying is right either. The whole point of course is to try to figure it out properly. I think we are on the right path though.

Mitchell...

Is it not possible that certain batters have a tendency to hit more fly balls to CF than others ?

If this is true, then it should be much easier for a runner to score on a sac fly to CF than to the corners because the throw is likely to be longer from CF.

Maybe the data sample is not large enough for this ability to show up, but that doesn't mean that no ability exists. Even if this ability is relatively insignificant, shouldn't you want to account for it to make your measurement of ability as accurate as possible ?

My other concern is lineup position - perhaps a hitter who bats leadoff will try to develop his on-base skills rather than his slugging skills - since the relative value of walk versus homerun is highest when leading off an inning, this should be accounted for. After all whether to bat a player 3rd or 1st in the batting order is the manager's decision and does not reflect a player's ability (though of course the manager looks at the player's abilities when making this decision).

Robert, great question!

Generally speaking, how does a player's hitting style change with the particular base/out/score situation? And is this affected if he is the #1 or #7 hitter? That is, does the leadoff hitter and the #7 hitter approach the same situation (say bases empty, no outs) the same way?

Mitchell has already shown in the past how hitters do change their approach when at Coors.

We know that certain events happen more with man on 1B or with 1B being empty, but you have a man on 2B. It doesn't change the LWTS component values much.

The other one though, about moving a batter from #1 to #3 is much harder to figure out. Was he moved because of a "perceived" problem, thereby affecting the "random sample selection" process? Or was he moved simply because it made sense. Tim Raines batted #3 for a while not because he blew it as a leadoff hitter, but simply because they needed a bat (not a good reason, but whatever). This would be one of the tougher studies to research.

To add further support to David on the "do the best defensive players have to play at the toughest defensive position" argument, the answer is "no" when looking at 2b and 3b. The current average 3b is a BETTER fielder than the current average 2b, based on the study cited earlier. However, given that the 2B faces far more plays than the 3B, this is one example where you have "lesser" players playing a "tougher" position. You can argue of course that the "physical makeup" of a 3b is different from a 2b, and that the 2b has different responsibilities than a 3b. But David's point is accurate.

Just like we don't have to expect that the managers will have an offensive spectrum that is a complete opposition of the defensive spectrum, nor should we assume that the best fielders are playing at the toughest positions.

Brad, excellent comments and questions. I'll address them one at a time...

1. Your "disagreement with my assessment of sac flies" is a bit unfounded. If you read some of my comments concerning the article and my methodologies, you would see that we are on the same page here. Clearly a player's GB/FB ratio and his SO rate will affect his overall out value. Of course, a player who has more FB's relative to K's will have greater value, due to the greater frequency of sac flies. Worrying about a player's sac flies is not the way to handle this though. The best way is to simply assign an out value for K's and one for non-K's. Obviously a K has a unique out value that is less (more negative) than that of a batted ball out. There is no need, however, to distinguish between FB's and GB's. This is due the coincidental fact that GB's and FB's have almost exactly the same out value. How can this be, you ask, since GB's include some double plays, and FB's generally do not, and FB outs generally score more runners from third than ground ball outs? Well, the primary reason that GB outs are worth exaclty the same as FB outs, even including the GDP, is that errors occur much more often on GB's than FB's; and of course the difference between an error and an out is huge. So to repeat myself, we do not need to record how many actual sac flies a player has, since anything above or below average is simply a matter of "luck" (usually because of fewer or greater opportunties than average) anyway. You could say that certain players will have more sac flies on the average becuase of the average distance of their fly balls or whether they tend to hit fly balls to right or left (left field arms are generally weaker). BTW, I don't know why a previous poster said that fly balls to center are generally further from home plate. Of course they are not. Batters hit the furthest fly balls to the pull field and the shortest to the opposite field, with center field being somewhere in between. Anyway, a batter's fly ball "tendencies" (location and distance) has a negligble effect on their sac fly frequencies and I don't think that super-lwts (or any other evaluative measure) needs to take every niggardly thing into consideration. There has to be a point of diminsishing returns. So, in retrosoect, and with the help of some of the comments and criticisms herein and on the BaseballBoards, in retrospect (and I still may do this, when I update my ratings), I wish I had used one out value for all FB's and GB's and another one for K's, and then adjusted for the foot-speed (and handedness) aspect of GDP's. But either way, it is definitely not necessary or correct to use, in any way, a player's actual number of sac flies or even his sac flies per opportunity, unless you think that individual players can "control" their fly ball frequency to a significant degree in a sac fly situation.

2. Adjusting for the quality of pitchers faced: I use a pitcher's component stats (s,d,t,hr,bb,so allowed) for the given season, expressed as a funsction of the league average in each category. For example, an average K pitcher will be 1.0 for K's, etc. On a PA by PA basis, I simply keep track of the average rating for all the stat categories of a batter's "pitchers faced." For example, if a bater happened to face a pool of pitchers during the season who had a K rating of 1.05 (high K pitchers), then his total K's would be divided by 1.05. Tango would kill me for treating each offensive category separately rather than as ratios, but I've been killed before! I don't know for sure, but my guess is that for full-time players, pitcher adjusting makes little difference. Of course, most of whatever difference it does make is due to batter not having to face his own pitchers (Palmer incorporates this into his park adjustment formulas; he has spearate park adjustments - one for batters and one for pitchers (BPF's and PPF's); I do not, so my pitcher adjustments take care of this - and then some).

3. No, I do not adjust the stolen base and caught stealings for the opposing catchers (and pitchers). I could and probably should, to be consistent with my "pitcher adjustments." How much work do you want me to do? I work AND go to law school, and do this crap for fun!

4. I do not take into consideration the handedness of players. It doesn't really matter, except for part-time or platooned players who face more opposite sided pitchers. For them, although their total lwts (total theoretical) contribution would still be accurate and relevant to describe their ability (and value) and to project their performace, to use their pro-rated, per 162 games, lwts would probably overestimate their value or ability. Now that I think of it, lefties are probably overvalued in the pro-rated lwts category since they are often rested versus the tough lefty pitchers. Players may also have faced an inordinately high percentage of RHP's or LHP's by chance alone, so I probably should do some kind of "adjustment" for the average handedness of pitchers faced, like I do for the overall quality of pitchers faced. Thanks for the suggestion. I hadn't thought of that. Now I'll have to quit school and maybe work part-time!

5. I can't remember, but I think that each pitcher's overall normalized stats are park adjusted, then I park adjust and pitcher adjust each batter at the same time. My park adjustments are also on a PA by PA and park by park basis. I don't just park adjust a player's home stats for his home park, or half of his total stats for his home park. I park adjust according to the average (component) park adjustments of all the stadiums he has played in, according to how many PA's he has had in each park. It's probably not worth all that work, although the computer of course does most of it.

6. Like I said, errors are automatically included in the value of the out, and they should mostly be included in the value of a GB out (a little in a FB out), and not at all in the K. As far as players inducing errors becuase of their speed and/or agressiveness(I assume these are the only reasons), if I see evidence or come up with evidence that this is true to a significant degree (it would not be hard to find out), then yes, it should somehow be accounted for.

7. I like that idea. It is very possible, as you suggest, that a SB may be "worth" considerably more than .17 runs, if there were a strong correlation between a high number of SB's or a high SB success rate (or a high SB-CS, as you state), and other baserunning. If there is, I supsect that the relationship would not be linear and the y intercept of the regression curve would not be 0. For example, a 5/2 SB/CS guy might be have negative value for other baserunning, while a 30/20 or 15/5 guy, might have much positive value in other baserunning. So we would have to be careful how we would adjust for this. SB-CS might be just the way to go!

Any more questions or comments, save them for Part III!

:)

Batters hit the furthest fly balls to the pull field and the shortest to the opposite field, with center field being somewhere in between.

This is true of ALL batted balls. However, the reader was probably talking about the balls that STAY IN THE STADIUM. Of that subset, it's equally obvious that the farthest hit balls is in CF. If they moved the LF and CF and RF walls to 500 feet, then MGL's statement would be accurate.

You are right, I would have killed you for not using ratios, which I've already shown to be accurate mathematical way to go. However, I would guess that it balances out mostly, and wouldn't expect to see much if any difference.

I second the request for the "unadjusted" stats. The only way to really move super-LWTS ahead is to have the data somewhat verifiable, and that would be the first step. The second step would be to produce them along team-by-team lines.