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4 October 2007 to 8 August 2007
My favorite moment in the New England Revolution's 3-2 defeat of FC Dallas for the 2007 U.S. Open Cup is not unheralded rookie Wells Thompson beating semi-heralded international Adrian Serioux to the ball and slipping it past almost-semi-heralded international Dario Sala for what ends up being the game-winning goal. It is not once-unheralded-rookie Pat Noonan's no-look back-flick into Thompson's trailing run. It is not once-unheralded-rookie Taylor Twellman's pinpoint cross to Noonan's feet. It is before that, as Twellman and Noonan work forward, and Noonan's pass back towards Twellman goes a little wide. As he veers to chase it down, Twellman gives one of his little chugging acceleration moves, a tiny but unmistakable physical manifestation of his personality. I've watched him do this for years. I can recognize him out of the corner of my eye on a tiny TV across a crowded room, just from how he runs. Just from how he steps as he runs.  

This is the Revolution's first trophy, after losing three MLS Cups and one prior Open Cup, all in overtime or worse. They are my team because I live here, that's how sports fandom mainly works. But I care about them, not just support them, because Steve Nicol runs the team with deliberate atavistic moral clarity. The Revs develop players. Of the 12 players who appeared in this victory, 8 were Revolution draft picks, 1 was a Revolution discovery player, 2 of the remaining 3 were acquired in trades before Nicol took over, and the last one (Matt Reis) was acquired in an off-season trade before Nicol's first season even started. Of the 5 other field-players on the bench last night, even, 3 are Nicol draft-picks and other 2 are Revs discoveries. As is Shalrie Joseph, suspended for a red-card picked up in the semi-final during an altercation with, ironically enough, yet another Revolution draft-pick now playing in the USL. Even the Revs' misfortunes are products of their own dedication.  

Arguably other teams, using more opportunistic methods, have acquired better players. Several of them have acquired more trophies. But none of them, I think, are more coherently themselves. None of them can hold up a trophy and know that they earned it, as a self-contained organization, this completely.  

Sometimes, as a sports fan, you get to be happy. Much more rarely, you get to be proud.


It is a small, powerful thing to rescue small truths hidden in seas of numbers. It is an even smaller and unfathomably deeper joy to hover, enraptured, in the countless endless instants between when some tiny thing happens to her for the first time, and when she shrieks with the joy of a universe expanding.
The Deciblog just published Justin Foley's reply to my implication that he botched his analysis of first letters of heavy-metal band names. [Read those if you want the rest of this to make any sense, not that I'm saying you need to want that...]  

Foley cc'ed a bunch of other people in the actual email, and in an ensuing thread that got well-underway before I noticed it in my spam filter (which wouldn't have happened if I'd had the good sense to put all Southern Lord label personnel in my Address Book proactively), someone beat me to taking statistical issue with Foley's idea that my 50,000+ EM-derived sample-size was "too large", but agreed that in the abstract some sort of weighting scheme could account for the idea that Metallica earns M more points than some unkown band called The Austerity Program earns for A (or, in Foley's original analysis, T).  

To all of which I said:  



Weighting is easy. Let's say that a band only counts if somebody has actually bothered to write a review of one of their releases, and we'll weight them by the number of releases that have reviews. This method counts 6778 of EM's artists, who have 14057 releases between them.  

Here are the percentages from the whole sample, the smaller sample unweighted, and the smaller sample weighted:

? All SU SW
# 0.3 0.4 0.3
A 9.1 9.8 9.8
B 5.9 6.2 6.2
C 6.3 6.4 6.0
D 8.9 8.1 8.3
E 4.9 4.6 4.3
F 3.6 3.7 3.1
G 3.0 3.5 3.4
H 3.9 3.9 3.7
I 3.7 3.5 3.7
J 0.6 0.6 0.8
K 2.2 2.3 2.6
L 3.1 3.0 2.8
M 7.4 6.8 8.2
N 4.2 4.0 4.0
O 2.3 2.4 2.6
P 4.0 3.7 3.6
Q 0.2 0.2 0.3
R 3.3 2.9 3.2
S 10.8 10.6 10.5
T 4.5 4.6 4.4
U 1.3 1.3 1.2
V 2.7 2.8 2.8
W 2.7 3.3 2.8
X 0.3 0.4 0.4
Y 0.2 0.3 0.3
Z 0.7 0.7 0.5


As you see, both restricting the sample and weighting do make small differences in the percentages, but S still wins, and D is still only in third.  

It's also easy to rigorously calculate the most metal of all names, in essentially exactly the way [FH] suggests. Using only the smaller sample, we can build up the name by at each position taking the most common letter (again weighting each band name by the number of reviewed releases) among the names which match what we have so far, working towards a goal length obtained in the same weighted-average fashion. This produces this incremental search result:  

goal length: 10
searching: [] 6778 partial matches
searching: [s] 718 partial matches
searching: [sa] 122 partial matches
searching: [sac] 23 partial matches
searching: [sacr] 23 partial matches
searching: [sacri] 9 partial matches
searching: [sacrif] 5 partial matches
searching: [sacrifi] 5 partial matches
searching: [sacrific] 5 partial matches
searching: [sacrifici] 4 partial matches
searching: [sacrificia] 3 partial matches  

I submit that when Daree Eeee and the mighty Sacrificia tour together, Daree Eeee will be going on first, and carrying their own mangy amps off the stage when they're done with their 3 crappy songs...  

glenn  
 

PS: I most definitely did not type in any numbers by hand.
PPS: Excel is a fine tool for lots of things. Not *these* things, though.



The aforementioned FH then clarified the less-rigorous most-metal algorithm he had in mind, which was also easy to produce:  



It's more or less just as easy to do it that way, considering only the weighted likelihood of a given letter at a given position with a given preceeding character.  

searching: [] 6778 candidates
searching: [s] 718 candidates
searching: [sa] 1109 candidates
searching: [sar] 870 candidates
searching: [sara] 533 candidates
searching: [saran] 450 candidates
searching: [saran ] 521 candidates
searching: [saran o] 419 candidates
searching: [saran or] 271 candidates
searching: [saran ore] 270 candidates
searching: [saran orer] 182 candidates  

I think Saran Orer get a guitar tech and some sandwiches, and go on after Daree Eeee, but they're still playing for people who are there to hail Sacrificia.



I hope everything is clear now, as I'm way overdue to get back to posting pictures of my daughter...  
 

[Discussion, if you can bear the thought, here on vF.]
And for completeness, here are the top bands by average rating across all releases, counting only the bands that have reviews from at least 10 different reviewers.  

# Artist Reviewers Average Spread
1Repulsion 11 96.43 3.064
2Esoteric (UK) 13 96.2 3.544
3Gorguts 15 95.43 4.03
4Lykathea Aflame 12 94.6 4.363
5Atheist 12 94.56 4.272
6Solitude Aeturnus 10 93.9 5.485
7Sacramentum 11 93.7 6.067
8Disembowelment 10 93.43 3.959
9Deeds of Flesh 12 93.33 5.375
10The Axis of Perdition 10 93.33 4.607
11Martyr (Can) 11 93.29 4.399
12Cult of Luna 10 92.56 6.735
13Persuader 11 92.4 5.886
14Katharsis (Ger) 10 92.0 5.715
15Novembre 12 91.86 5.436
16Vintersorg 21 91.67 6.968
17Demilich 15 91.58 7.522
18Saint Vitus 13 91.36 6.526
19Belphegor (Aut) 15 90.94 6.571
20Manticora 11 90.64 7.889
21Windir 21 90.64 6.986
22Agent Steel 15 90.2 6.002
23Negurã Bunget 10 90.17 10.123
24Pentagram (US) 10 90.13 6.827
25Maudlin of the Well 11 90.08 8.558
26Deströyer 666 15 90.07 7.676
 

Vintersorg and Windir are the only bands to get an average above 90 with 20 or more reviewers. So clearly those are the greatest bands in all of heavy metal.  

The worst metal band in the world is Apocalypse, who got an average review of 7.0 from 14 reviewers. Dishonorable mention to Six Feet Under, the only band with at least 4 releases and 10 reviewers who averaged below 50 (49.91 from 47 reviewers).
And here are the 25 least consistent:  

# Artist Spread Average
1Sepultura 25.022 67.55
2In Flames 21.621 60.74
3Megadeth 19.333 71.54
4Krieg 18.455 71.19
5Deicide 18.007 74.15
6Deathspell Omega 17.925 83.93
7Metallica 17.863 69.16
8Virgin Steele 17.747 74.5
9Six Feet Under (US) 17.601 55.15
10Dissection (Swe) 17.275 70.67
11Sentenced 16.626 66.79
12Moonspell 16.312 78.28
13Nuclear Assault 16.234 72.04
14Mayhem (Nor) 15.754 67.69
15Machine Head (US) 15.453 55.06
16Within Temptation 15.379 61.25
17Slayer (US) 14.537 73.97
18Children of Bodom 13.943 77.63
19Black Label Society 13.937 75.05
20Pantera 13.921 69.97
21Celtic Frost 13.717 73.4
22Cannibal Corpse 13.476 74.89
23Motörhead 13.319 78.23
24Danzig 13.037 80.0
25Pain of Salvation 12.934 87.77
 

Most of these follow the "great once, crap now" pattern (I think we can now officially call this "Sepulturding"), which makes one wonder whether developing a fan-base is really worth the bother in the end. Deathspell Omega deserve a special note: if they'd had the sense to release Infernal Battles under a different name, their other 4 albums would give them a standard deviation of 1.66 on an average of 92.86, and we could have a very obscure statistical argument over whether that means they are in fact even greater than Fates Warning.
My analytical tools make various otherwise-elusive questions easy to answer, so while I'm playing with heavy-metal data, here's another thing I wondered about: which bands have the narrowest and widest ranges of ratings? To answer this meaningfully I counted only releases that have 4 or more reviews, and only bands that have 4 or more of these releases and at least 10 different reviewers. For these I then averaged the ratings for each such release, and ran standard deviations on the sets of averages. So a low standard deviation means there's some consensus that the quality of the band's output is consistent. High means consensus that the quality varies widely.  

Here are 25 most consistent. "Spread" is the standard deviation, "Average" is the average rating of the releases used in the calculation.  

# Artist Spread Average
1Coroner 0.908 88.21
2Helstar 1.455 90.54
3Moonsorrow 1.676 89.98
4Dark Angel (US) 1.767 82.15
5Candlemass 1.842 89.78
6Lamb of God 1.845 68.5
7Obituary 2.004 85.32
8Type O Negative 2.035 89.16
9Accept 2.193 88.06
10Agent Steel 2.479 90.49
11Fates Warning 2.531 93.36
12Alice in Chains 2.538 88.83
13Iron Savior 3.025 88.25
14Falconer 3.083 84.42
15Therion (Swe) 3.159 90.38
16Sodom 3.294 83.4
17Kamelot 3.463 90.52
18Gorgoroth 3.496 84.71
19Judas Iscariot 3.602 89.03
20Bolt Thrower 3.652 88.31
21Suffocation (US) 3.701 86.48
22Angra 3.758 88.63
23Enslaved (Nor) 3.926 88.85
24Vader 4.162 85.78
25Bal-Sagoth 4.249 89.9
 

I sense a hastily-assembled cash-in Coroner boxset in our future. I think this also means that Fates Warning is the most consistently great band in all of heavy metal. So now we know. And Lamb of God gets some sort of weird prize for being the most consistently mediocre.
If you're going to waste your time doing obsessive analysis of data on which nobody's life or ecology depends, you ought to at least do it diligently and efficiently.  

About a month ago The Deciblog published Justin Foley's attempt to answer the timeless question "How likely is a metal band to start their name with a particular letter of the alphabet?". For his sample set, Foley took the combined rosters of several metal labels (he doesn't reveal either list), which gave him 814 names, for which he then calculated the first-letter distributions, reaching the startling conclusion that the most likely letter is S.  

Foley put his results in a bar-chart, which I assume means he used a spreadsheet, so hopefully he didn't spend a whole lot of time hand-counting. But he should have spent even less. The Encyclopaedia Metallum is not only just sitting there with a collaboratively-amassed and collectively-moderated database of 50,000+ metal bands, but they've even already split it up by first-letter and there are band-counts right at the top of each letter-page. Add, divide, and you're done.  

Here, then, is the much better-informed version of this still-pointless breakdown:

? % *
# 0.3%
A 9.1% *********
B 5.9% *****
C 6.3% ******
D 8.9% ********
E 4.9% ****
F 3.6% ***
G 3.0% ***
H 3.9% ***
I 3.7% ***
J 0.6%
K 2.2% **
L 3.1% ***
M 7.4% *******
N 4.2% ****
O 2.3% **
P 4.0% ***
Q 0.2%
R 3.3% ***
S 10.8% **********
T 4.5% ****
U 1.3% *
V 2.7% **
W 2.7% **
X 0.3%
Y 0.2%
Z 0.7%


Most of Foley's numbers aren't that far off. His small sample-size leads him to overestimate J and Y, and underestimate Q and V, but the absolute numbers for these letters are small anyway. He also seems to underestimate A, P and R, for reasons which are not apparent in his opaque reporting, but might have to do with language tendences, as EM's list is probably more global than his.  

But the biggest discrepancy in Foley's numbers, by far, is T, which he credits with 9.3% of the band-names, where EM data indicates less than half that. Here I have a wearyingly mundane but highly plausible theory: Foley has accidentally counted all the bands whose names begin with "The " as T, despite specifically saying that he didn't. This is obviously both philosophically and methodologically repugnant, and although I regret the maelstrom of blogospheric outrage that will undoubtably accompany my public exposure of this error, I think we owe ourselves (the) Truth.  



Of course, the thousands of metal fans around the world who have put time and effort into building the Encyclopaedia Metallum did it because they care about the music, not the alphabet. The site is the definitive central reference source for most metal-related matters, and certainly the final arbiter of obscurity for the vast unknown majority of the bands it lists.  

It also has, in addition to its factual content, tens of thousands of percentage-scored, user-attributed, peer-moderated reviews of metal recordings. What it does not have is any sort of similarity analysis to make use of the huge data-graph represented by the connections between bands, users and ratings. There is a wearyingly mundane reason for this, too: trying to do similarity analysis with SQL queries will make you want to eat your own neck.  

So here is an actual contribution to the world's knowledge on this admittedly peripheral subject: empath, the missing similarity analysis of EM user/review/band data, accurate as of yesterday. Pick a band, see the other bands that people who like the first band also like. Data wants to form shapes. In a better world, this would be just as easy for EM to do themselves, updating live, as it is for them to serve their raw data into web pages.  

Easier, actually. I bet it took me less time to do this analysis than it took Foley to make a bar-chart of first letters. But I have better tools. I have better tools because at the moment I'm paid to design better tools. If I do my job well enough, eventually you'll have my better tools, too. I'm not designing them to tabulate heavy metal, I'm designing them to answer questions. Not all answers turn out to be shaped like Truths, of course. But if you can't answer them, you can't be sure which are which.
1. Good light.
2. Elevate child to proper height.
3. Remove awkward outer casing from instrument.  

It should be possible to refer explicitly to something particular on a web page. To anything, whether or not the page's author thought to assign it a (secret) labeled ahead of time.  

The most obvious way to mostly provide this, it seems to me, is for the HTTP URL syntax to include a way to specify a search string at the end, which the browser simply plugs into its own Find function after loading the page. I will randomly suggest that since we already have "#" for fragments at the end of a URL, and valid fragment IDs must begin with an alphanumeric or an underscore, "#=" could be used for passing search text. So where  

http://www.furia.com/page.cgi?type=log&id=273  

refers to this blog-entry as a whole page, you could also do  

http://www.furia.com/page.cgi?type=log&id=273#=this+phrase  

to refer directly to this phrase.  

For extra credit we could also support "@" and a number for getting to the Nth use of that text. So  

http://www.furia.com/page.cgi?type=log&id=273#=this+phrase@2  

would refer directly to this phrase, not the one above.  

Useful, semantically reasonable, eminently implementable.  
 

[See the discussion on vF.]
Two of the very bad features of the SQL-based conception of relational data-modeling, I think, are these:  

- one-to-many relationships are significantly harder to handle than one-to-one relationships (and, inversely, many-to-one relationships easiest to handle by defactoring)  

- absence is significantly harder to handle than presence  

It's easy to add an Artist column to an Album table, for example, so we get:  

AlbumArtist
Black OneSunn O)))

But if you want to model the fact that collaboration albums can have multiple artists, you need an Albums table, an Artists table, a join table, and a bunch of IDs:  

Albums
AlbumID
Black One1
Altar2

Artists
ArtistID
Sunn O)))1
Boris2

Authorship
AlbumIDArtistID
11
21
22

This is already a pain in the ass, and thus you get a world filled with cop-outs like this:  

AlbumArtist
Black OneSunn O)))
AltarSunn O))) / Boris

and this:  

AlbumArtist 1Artist 2Artist 3
Black OneSunn O)))FALSEFALSE
AltarSunn O)))BorisFALSE

both of which are tolerable enough to just look at, but awful when you go to try to get the computer to answer even perfectly sensible questions like what albums Boris has done.  

And then, when you want to add a little more detail, you really ought to do this:  

Albums
AlbumID
Black One1
Altar2

Artists
ArtistID
Sunn O)))1
Boris2

Formats
FormatID
CD1
LP2
Download3
Authorship
AlbumIDArtistID
11
21
22
Formatship
AlbumIDFormatID
11
12
13
21
22

but instead you'll probably try to get away with:  

AlbumArtistFormatCode
Black OneSunn O)))CD/LP/D
AltarSunn O))) & BorisCD/LP

or  

AlbumArtist 1Artist 2Artist 3CDLPD
Black OneSunn O)))FALSEFALSETRUETRUETRUE
AltarSunn O)))BorisFALSETRUETRUEFALSE

because if you have the latter, you can do:  

SELECT Album, Artist

FROM Albums
WHERE D=FALSE

which is easy to understand, although it won't work because you forgot you don't have a field called plain "Artist" anymore. Whereas with the five-table form you have to do something like:  

SELECT Albums.Album, Artists.Artist

FROM Albums, Artists, Authorship
WHERE Albums.ID=Authorship.AlbumID
AND Artists.ID=Authorship.ArtistID
AND NOT EXISTS (
SELECT *
FROM Formats, Formatship
WHERE Albums.ID=Formatship.AlbumID
AND Formats.ID=Formatship.FormatID
AND Formats.Format='Download'
)

and that's probably still wrong.  
 

I submit that this is all a colossal mess, and it's a wonder the database-driven software we get driven out of it isn't even more woeful than it is. A better paradigm would embrace the opposites of these two flaws:  

- the modeling of a relationship should be the same whether the relationship is one-to-one, one-to-many, many-to-one or many-to-many, and should be the same no matter how many "many"s you have  

- absence and presence should be equally easy to assess  

And I think a useful rough metric for such a new system is that it allows you to model your data without ever needing to say FALSE.  

And I think this, I think, because I think this is how we think and talk about relationships between things when nobody is getting in our way:  

Black One was recorded by Sunn O))), and was released in CD, LP and Download formats. Altar was recorded by Sunn O))) and Boris, and was released in CD and LP formats. All of these albums were released on CD and LP. Altar is the only one that wasn't released in Download format.  

Take these out of sentence form and make the data-types explicit and everything is still perfectly straightforward:  

Album: [Black One]

Artists: [Sunn O)))]
Formats: [CD] [LP] [Download]  

Album: [Altar]
Artists: [Sunn O)))] [Boris]
Formats: [CD] [LP]

or, looking at the same data from a different perspective:  

Format: [CD]

Albums: [Black One] [Altar]  

Format: [LP]
Albums: [Black One] [Altar]  

Format: [Download]
Albums: [Black One]

and then the old questions are also easier:  

FIND Formats WHOSE Albums INCLUDE 'Black One' and 'Altar'

FIND Albums WHOSE Formats DON'T INCLUDE 'Download'

Subliminating the one/many distinction allows us to talk about albums with 3 or 8 or 0 artists just as readily as we talk about albums with 1 or 2. Fixing the query language to handle absence lets us cope with the introduction of a new edition into our dataset, say, without having to add a new field for it and/or go through asserting that everything we already knew about doesn't have that format. I suspect pretty much every database field headed X and filled with TRUEs and FALSEs could at least be more usefully modeled in my new world as a relationship called "Characteristics" that either does or doesn't include X. And usually, as with formats, there's actually a meaningful label that contains information itself. The point is that things have relationships to their characteristics, or their formats. They don't, except in a very existential sense, have a relationship to falseness. FALSE is a machine thing.  
 

The relationally astute will note that the five-table version is the fully-normalized representation that would support an SQL-based implementation of my new form as a display abstraction, and presumably my pseudo-query-code could be preprocessed into the messy SQL so I'd never see it. But that's my point: SQL is too low an abstraction, which makes it too hard to do things naturally, which makes it too hard to pass along natural behavior to the victims of the system. I'm mainly arguing for a better abstraction. (Although I also expect it's still always going to be bad that there's no better underlying way to represent lists, so I'm also probably arguing for the better abstraction to not merely be layered on top of the bad one.)  
 

The discographically astute will note that Altar is available on iTunes, and that I have vastly undermodeled the rich and complicated space of Sunn O))) release formats. But if I'd done the examples in real detail, I'd still be typing out the bad versions...
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