Yes, I know the formatting is screwed up yet again, but I don't have an hour to fix it up. Sorry.

As I told John, "75%" doesn't really mean anything here.

Here are the results expressed in a slightly different, but entirely equivalent way. Instead of stating each candidate's point total, I'll give the average placement on everyone's ballots. Then I'll also give the standard deviation of that placement. The first number in the chart below is the average placement; the second number is the standard deviation. The 1.00 and 2.00 with zero standard deviation for Williams and Musial are signs of unanimity.

Consensus scores: (Mean = 82)

Rusty Priske: 87
andrew siegel: 86
whoisalhedges: 86
DL from MN: 86
Chris Cobb: 86
Devin McCullen: 85
Joe Dimino: 85
Howie Menckel: 85
OCF: 84
Dan R: 84
ronw: 84
== median ==
Tiboreau: 84
bjhanke: 83
Sean Gilman: 82
TomH: 82
Mark Donelson: 80
John Murphy: 78
mulder & scully: 78
Esteban Rivera: 76
EricC: 76
Rick A: 70
sunnyday2: 70

I'm confused. There's 22 first and second place votes, 16 thirds, 20 fourths, 22 fifths, and so on.

What gives?

The formatting of John's table is what gives. I think all the correct information is in there somewhere, but not in the correct columns.

For instance:

3rd place votes: Delahanty 16, Yaz 6.
4th place votes: Delahanty 3, Yaz 8, Raines 8, Burkett 2, Simmons 1.

And so on. They do all add up to 22.

I'm confused. There's 22 first and second place votes, 16 thirds, 20 fourths, 22 fifths, and so on.

What gives?

Yaz's totals are shifted, methinks. His name is too damn long.

It's probably mostly the Yastrzemski row - that's the problem with spelling his name out rather than abbreviating it.

OK, I see that now.

It's probably mostly the Yastrzemski row - that's the problem with spelling his name out rather than abbreviating it.

With the old tabulator, Yastrzemski's name wouldn't be a problem. However, the makeshift ballot counter that I created doesn't format properly here for some reason, despite it looking fine on my Excel spreadsheet.

Boy. I'm getting into a lot of debates here. If it starts to get old, please let me know. Anyway, this one is my defense of what I did to Burkett and Magee's hot seasons in 1901, 1914, and 1915. Those of you who read my comment on "SD/% Dissonance" back in the third base thread have a head start here, because the reason I treated those three seasons the way I did is a corollary of that.

As briefly as possible, here's the concept. Almost all - actually, all that I know of - of the measures we use to rank players (Win Shares, Total Player Ranking, etc.) express their results in a form that is not related to Standard Deviation. Now, when a league season is a "high standard deviation" season, that means that the "size" of the standard deviation for that league is greater than the size of the normal league's SD. Well, "size" means Win Shares (or whatever). A high-SD league has more Win Shares per standard deviation than a normal league. And that means that the height of the SD does not apply evenly to all players. The more SDs the player has, the more he gains from the high-SD league. Burkett and Magee are, of course, players whose normal seasons are more than one standard deviation above the league mean. And so, they gain more from a high-SD league like 1901, 1914, or 1915 than a normal player does. Their large numbers of Win Shares in those leagues are, at least in large part, not a result of their having a hot season. They are the result of having a large boost from the high-SD league.

OK, that was as short as I can write. What follows is an example, using Magee and the hypothetical player "Joe Ordinary". Please don't obsess over the exact numbers or the fact that I'm using Win Shares. I'm making the numbers up in order to make the example easy to read and understand. I'm using Win Shares because they provide nice, easy numbers. That's all.

Let's say, just for argument's sake, that a league with a normal standard deviation has ten Win Shares in one SD for a player who plays the entire year. Again, that's just a convenient number I made up. Let's postulate a player called Joe Ordinary. Joe's normal season is one SD above the league mean. That does have value, of course - ten WS a year. Joe is a starter, but no more than an ordinary one. The concept, though, is important. What I'm doing is measuring a player's performance by his standard deviations, rather than WS or TPR or anything else. Joe Ordinary is a one-SD player, NOT a ten-WS player. That's important. And I think it's correct for purposes of Hall of Merit voting. I have reasons for that, but they triple the length of the post, so they'll have to wait for the center field discussion thread that is coming.

Now consider Sherry Magee. He is hardly Joe Ordinary. His normal season comes in at what? Three SD above the mean? That sounds close to correct. There should be, in a population the size of baseball's, about 5 SD between the mean and the absolute best player. I'm giving Magee three SD. Again, I'm making that up, but at least there is some logic to it. Magee might be a 4-SD player. Or a 2.5. I don't know. Just bear with me.

Now let's consider 1914 and 1915, when the Federal League drained some talent away from the NL and AL. That increases the size of the SD in the two established leagues. Let's say the gain in Magee's NL is 2 Win Shares per SD. Again, I'm making this number up for convenience.

What is the effect of this on Joe Ordinary? Well, his typical season goes from 10 WS to 12, because he has one SD. And no one notices or cares. It just looks like a normal variance among Joe's seasons. No problem.

But how about Magee? Well, he's a 3-SD player. So his gain is not 2 Win Shares, but 2x3=6. His normal season (again, I know this is high for Sherry Magee, but bear with me) runs about 30 WS if he plays every single day. But in 1914 and 1915, he turns in 36 WS. That looks huge. It looks like career years. In any analysis I know of, it those two seasons define his peak ranking. But is it that huge? Well, not really. Sherry was still the same old 3-SD player he always was. His Win Shares are higher only because there are more of them in each of Sherry's 3 SDs.

That's my argument for making deductions for Magee, Burkett, and 1961's Cash, Gentile, Mantle, and Maris. These guys may have turned in their best years ever, in terms of WS or TPR, but some, maybe all, of what we see is simply the effect of a high SD on a multiple-SD player.

The argument turns on the idea of SD/% Dissonance. Win Shares (or TPR or whatever) are not statistically related to Standard Deviations. So, if a league has a high SD, that means that each SD, NOT each player (sorry, Paul, but this is where we disagree), gains a WS or two or whatever. Multiple-SD players benefit more from that than lesser players do.

I rely on this concept a LOT. I make a LOT of ad hoc deductions based on it. And right now, I'm begging for help. It appears, from some of the comments made in these here HoM threads, that at least some of you actually have worked out the SDs for every league in the history of the majors. Can I get a copy of this data? It would help me a LOT. I am willing to pay for it, as I imagine the effort took a great deal of time and trouble. But I really, really want this tool. If you have it, and are willing to share, PLEASE post up here and let me know.

OK, enough begging. Now you know why I made the deductions. 1901, 1914 and 1915 are high-SD seasons. Jesse Burkett and Sherry Magee are multiple-SD players. The benefit per SD gets multiplied by the players' SDs.

I am fully aware that the counter argument goes "but those extra WS contribute actual runs and actual wins to the player's actual team." And that is correct. In a high-SD league, the best players contribute even more to their teams' efforts than they normally do. That is, basically, why Bill James timelines. If he doesn't, too many of the best rankings end up coming from the dead ball era (and would come from the 19th c. if Bill used FSEs), because early SDs are higher, in general, than later ones. But his timeline is just linear. It's not related to SD at all. This concept is. As far as I know, using SDs to rank players automatically timelines, because it automatically adjusts for high and low SDs. War years. Early baseball. Expansion years. It catches them all.

At this point, I'm stopping. If I go any further, it means discussing the difference between "WS contribute actual runs" and "SDs rank players correctly within their contexts." That is what would triple the length of this post. I promise to get into that one, but not right now. Balloting for left field is closed. I'll post more in center, unless people are just sick and tired of reading my stuff.

Yours, and Thanks! - Brock

What?! You mean I did NOT have the worst agreement with the consensus? Recount the votes! Fire the Russian judge!
No, really, I guess it means all those weird adjustments I made weren't altogether ridiculous, after all. Whew!
- Brock

Grandma Murphy says, "With the old tabulator, Yastrzemski's name wouldn't be a problem.
However, the makeshift ballot counter that I created doesn't format properly here for some reason,
despite it looking fine on my Excel spreadsheet."

I think the problem is that whatever translator went from Excel to the web here placed the tabs at half an inch.
My display is very very wide - just as wide as the ballot displays.
It's true that Yaz' name is too long and runs over the first tab,
but I don't know if that's what threw the whole tab setting off.

BTW, the reason this post doesn't have the width problem is that I forced carriage returns,
not because I somehow solved the problem.

- Brock

I have the highest consensus?

Really?


I'm usually near the bottom.

Finally you are all coming around to my way of thinking!

:)

The columns are misaligned because of the 'z' in Yastrzemski which no one pronounces.
The display is very wide because of the TAB-alignment and the extra characters (TABs) at the far right. Highlight the region and see that clearly.

--
Brock #11
The argument turns on the idea of SD/% Dissonance. Win Shares (or TPR or whatever) are not statistically related to Standard Deviations.
So, if a league has a high SD, that means that each SD, NOT each player (sorry, Paul, but this is where we disagree), gains a WS or two or whatever.
Multiple-SD players benefit more from that than lesser players do.

I don't disagree with that but I disagree that that should make much difference. Regarding Sherry Magee 1913-1916, when you knock
Magee 19-29-26-16 down to 19-26-24-16 (losing five win shares)
you must knock
Sam Crawford 27-31-28-13 down to 27-28-25-13 (losing six) and
Joe Jackson 36-20-18-34 down to 36-18-16-34 (losing four).
(Crawford and Jackson show up in right field next month.)

And when you look at 1969-70, making the 1970 effect a bit smaller in this illustration,
you must knock
Yaz 26-36 down to 23-34 (losing five)
Williams 24-29 down to 22-27 (losing four)
Stargell 27-17 down to 25-16 (losing three)

How will such season effects --contrast knocking decades or half-centuries up and down-- show up as notable differences in a ranking such as this election?
Only for someone who puts a big emphasis on "peak" defined by best non-consecutive seasons, such as the Bill James 3yr peak.

--
For Jesse Burkett, when you knock down his league-first 38 win shares in 1901 you should also boost his league-sixth 25 in 1900, perhaps 25-38 ==> 27-35 (losing one).

How will such season effects --contrast knocking decades or half-centuries up and down-- show up as notable differences in a ranking such as this election?
Only for someone who puts a big emphasis on "peak" defined by best non-consecutive seasons, such as the Bill James 3yr peak.

I'm not certain that the difference should be remarkable even for any such 3yr peakist.
Someone who counts "MVP type seasons" where 30=yes, 29=no might fortunately discount two such seasons, with remarkable consequences.

bjhanke, as the group's self-anointed standard deviation guru, I very much appreciate your interest in the concept. I think your understanding of a few points could be deepened a bit.

1. The counterargument you propose is a straw man. First, let's not use Win Shares even in this theoretical analysis, because they are poorly thought out and lead to all sorts of problems in this type of discussion (I can explain why if you're interested). Let's use something that measures actual value, some indicator of wins above replacement. Doesn't matter whether you use mine, Baseball Prospectus's, or a home-grown version, it's just the concept. Let's also just assume that player performance is normally distributed about the mean (it isn't, but it's close enough for our purposes). OK, take a league where the standard deviation of player performance is 2 wins per season. If we call the bottom 2-3% of major leaguers replacement players, that means they will be four wins below average per season, while the All-Stars will be four wins above average per season. This makes league average a four-WARP player, and an All-Stars an eight-WARP player. Let's also say that the top two teams in the league win 95 and 90 games.
OK, now let's say that something, some real external factor, actually causes this stdev to double (as opposed to simply the addition of a bunch of superstars or super-scrubs to the league). (In practice, this would most likely be an increase in run scoring or an expansion). Now, replacement is eight wins below average, while All-Stars are eight wins above average, meaning that league average players have become eight-WARP players, and All-Stars have become 16-WARP players, overnight, with no change to their underlying ability. What happens?
Well, assuming the distribution of talent between teams doesn't change, you'd see a corresponding increase in the standard deviation of wins between teams. So the 90-win team (nine wins above average) will become a 99-win team (18 wins above average), while the 95-win team will become a 109-win team.
Why is winning games important? Because it leads to pennants. When you increase standard deviation, ceteris paribus, you change not only the stats-wins relationship, but also the wins-pennants relationship by the same amount. 97 wins is enough to eke out a pennant in the low-stdev league, but is only good for third place in the high-stdev league. So, if what we are interested in is "pennants added," then we most definitely DO need to correct for standard deviation when assessing players' value.

2. The correct standard deviation adjustment is multiplicative, not additive. You don't "subtract 2" to discount the effects of a high-stdev league, you "multiply by .9" or whatever. As you say, this will reduce the value attributed to the stars more than to the scrubs.

3. That said, we need to distinguish between a "true" increase in standard deviation--one that actually makes a league "easier to dominate"--and the inevitable year-to-year fluctuation that takes place due to random noise and to the actual distribution of talent in a league. The clearest example of this is, the highest observed major league standard deviations since 1893 are clustered in the 1920's AL. Was this because the league was easy to dominate? No, it's because the league had a one-man star glut by the name of George Herman Ruth, who singlehandledly was increasing the overall league stdev by massive proportions.
The way to do this is with a regression analysis, which determines the relationships between league factors like run scoring, expansion, and population per team, and observed stdevs over the course of baseball history. By applying the resulting equation to each league-season, we can then determine how easy it was to dominate based on these factors, without making any reference to the actual performance of the players in the league, thus avoiding the temptation to give extra credit to George Burns for playing in low standard deviation leagues just because all of the stars were in the AL. The result of this is the standard deviation adjustment I use in my WARP.

4. You say, "Now let's consider 1914 and 1915, when the Federal League drained some talent away from the NL and AL. That increases the size of the SD in the two established leagues." It does? This is true neither conceptually (Why would it? If all the superstars jumped to the FL, the major league stdev would go *down*) nor empirically (the stdev for 1914-15 was 2.69 wins per player-season, compared to 2.83 wins per player-season over the 1910-13 and 1916-19 period). There is no reason to deduct for 1914-15. There *is* a reason to deduct for the 1901 expansion, but not more than there is to deduct for the mid 1890's NL being such a high scoring league and producing astronomical stdevs.

5. Someone wants to pay for my data? Are you f'ing kidding me? Did I just die and go to heaven???? It's available for free in the StDevs and Rep Levels.xls file in the Rosenheck WARP.zip archive posted to the Hall of Merit Yahoo group. I can send you an offense-only version as well if you'd like, which gets a higher r-squared on the regression and, therefore, bigger standard deviation corrections than the ones I am currently using.

6. Using SDs to rank players does not "automatically timeline." It simply allows us to compare players' contributions in terms of pennants added on a level playing field, across league-seasons where the wins-pennants relationship can differ substantially. Timelining, as it is generally understood here, means discounting pennants from earlier eras, on the grounds that the quality of competition is not as high. My stdev adjustment only goes far enough to equalize pennants from the 1890s and the 1980's.

7. If you are interested in learning more about this subject, there's a 550-post thread on my WARP data that you might find informative. I have been deluging the group with analysis and arguments about standard deviations ever since I rejoined the project, to the great consternation of a large number of participants and lurkers (see around post 170 on the 2005 results thread for criticism of my approach).

Shouldn't Keller be ahead of Wheat? Tied on points but Keller got the highest up-ballot support.

The error bar on Stargell is huge. This group ranked him from 6th to 19th. Also Sheckard - 9th to 21st.

We have a jarring lack of consensus on defensive value.

I'm not a big fan of ending elections on weekends. I usually don't check this page on the weekends (especially gorgeous weekends in August).

Dan - do you have an updated spreadsheet with 2006 and 2007 data? I'd love to see that.

Yes, clearly Stargell vs. Sheckard is all about defense. I would expect that voters who had Stargell high had Sheckard low, and vice versa.

Le Samourai, I don't at the moment, although if there are any particular players you are interested in I can calculate it for them. I plan on producing a 2.1 build of my WARP (no methodological changes, just incorporating the new baserunning and defensive statistics that have become available since I first developed the metric), and would definitely update it for '06-'07 (and '08, presumably) then. Version 3.0 would be a fully integrated model including pitchers, but until I can figure out how to approach the pitching/fielding split over time (so I don't have 1930's SS getting 5 FWAA), that's going to remain on the back burner.

The error bar on Stargell is huge.

Do you see the last column of my post #2? That's the "error bar." And the runaway most-disagreed-about candidate is Jones. That - the standard deviation of ballot placement - should be higher in the middle of the ranking and lower at both ends. As such, the 3.18 for Stargell isn't really out of line, and we disagreed more about Williams, Sheckard, and Kiner.

I understand disagreements about Charley Jones - turn of the century player with an incomplete record and controversial argument for credit.

Kiner is just a peak/career disagreement.

I don't get Billy Williams at 17th or 20th. He just seems to have an unusually large "tail" and I would expect more voters would reduce his
standard deviation. I don't expect the picture would get clearer on Stargell or Sheckard with more samples.

Le Samourai, I don't at the moment, although if there are any particular players you are interested in I can calculate it for them. I plan on producing a 2.1 build of my WARP (no methodological changes, just incorporating the new baserunning and defensive statistics that have become available since I first developed the metric), and would definitely update it for '06-'07 (and '08, presumably) then. Version 3.0 would be a fully integrated model including pitchers, but until I can figure out how to approach the pitching/fielding split over time (so I don't have 1930's SS getting 5 FWAA), that's going to remain on the back burner.

Ah, well I'm more than willing to wait. I'm very excited to see what your WARP has to say about pitchers when you eventually figure it out.

I have a preliminary version for starting pitchers which I can send you, Le Samourai, but it has a ton of caveats.

I'd love to see it if you don't mind. My email is criminal5a@gmail.com

I think B. Williams is just a peak/career differential, too. Ditto Sheckard, for that matter. Even Stargell, it's just a question whether you measure his peak by seasonal totals or rate.

As to my consensus rating, I am pleased. I just don't think of "merit" as being necessarily tied so closely to career value. I sorta like guys who were "the best of their time" among some cohort or other--i.e. best player, best hitter, best OF, best LF, etc. etc. etc. Also I've never been much of a "rate" voter, but somehow over our long layoff I developed somewhat of a like for a high rate. I suppose I should revisit John McGraw when we resume the regular HoM balloting.

Yes, yes, yes you should! :) He was a *dominant* player, even after docking him for his missed games. Don't forget holdout credit for 1900! :)

Sunnyday2's largest deviations from consensus: a + sign means he put that player that many positions above the average, and a - sign means he put that player that many positions below the average:

Jones: +7.05
Stargell: +5.00
Minoso: +3.00

B. Williams: -7.14
Sheckard: -6.77
Kelley: -5.64
Goslin: -4.32
Wheat: -3.73
Raines: -3.59

Anyone else can do the same with their own votes - just look at #2. For instance, I had these deviations:

Stargell: +4.00
Medwick: +3.59

Sheckard: -4.77
Jones: -3.95

ha ha, you hit the Charley Jones floor, OCF, but that is not even 1 s.d. below average

There is some slack around #9-10 in the election report, Williams and Stargell, but that is reasonably good separation, Billy Williams a clear number nine, ranked in the top ten by a big majority. The top eight are almost unanimously ranked in the first nine and the bottom eleven are almost unanimously ranked in the last twelve.

Formatting fixed. Many of the posts on the thread are more readable now too.

The tiebreaker for Keller/Wheat should be head to head ballots. Up or down the ballot support doesn't matter - if it did, up the ballot spots would get more points. Obviously both were names on all 22 ballots, so that's meaningless here too.

Can one of the ballot counters easily tell who won the 'head-to-head' battle? If it was 11-11 then the tie would just stand.

Formatting fixed. Many of the posts on the thread are more readable now too.

Much obliged, Joe. Did you do something easy that I should know about or did you spend some time fixing it manually?

Can one of the ballot counters easily tell who won the 'head-to-head' battle? If it was 11-11 then the tie would just stand.

I can't do it easily, Joe, since my makeshift tabulator didn't take that into account.

Head to head, Keller versus Wheat, is an 11-11 tie.

Thanks OCF.

Much obliged, Joe. Did you do something easy that I should know about or did you spend some time fixing it manually?

It's mostly manual - I copy the results you post into a spreadsheet (which is probably where you copy them from).

Then I copy and paste into a text file. I do a find and replace where I copy the 'tab' type character that separate each number and replace that with two space characters.

This gets everything 'close' from there I manually add and remove space characters until it all lines up. Probably takes about 15-30 minutes depending on the particular ballot.

I don't mind doing it at all (others do much more work than that). I was out of town until Monday afternoon and then slept most of the rest of the day and finally had a chance to work on it this morning.

Monte Irvin added to the left fielders group.