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Playing Hardball
Saturday, 21 January 2006
Probability Is More Than Probable!

It is time for me to toss simple math equations out of the window and introduce my ego to the concept of Logarithm Probability. According to my overly snotty opinion, adding probability to these numbers help to increase accuracy in numbers.

How do I add this new form of math to my baseball studies repretaur? Not so simply, I calculate the binomial probability of both an individual player's bases per out and the league's bases per out. A big difference compared to other studies [which I am in on the details] is that instead of using the straight league mean, I incorporate finding the league average without the individual player's numbers being rated.

So why should I go through all this trouble of crunching all these numbers in this probability equation instead of the more familiar and much easier simple version? Simply said, pre-probability baseball stats in the rawest form are a display of streaks which usually do not display any definitive results to most statisticians.The reason? Our easy to reach data sample pool is so small that it really shows how lucky or unlucky a certain individual is. Results are shown in a chaotic fashion, which in reality do not follow the laws of probability. Therefore, it is best to bang your head against the wall a few times before starting to personally compute these equations. [Word of warning: either get a good computer program which can compute this mess of numbers, or at least purchase a scientific calculator.]

Viewing the results show that a lot more individuals are viewed as being right on the mean or even below this level, while they were actually above this line in a previous statistical life. Additionally, some lower tier performers have been boosted to at least a semi-marginal status. Through the laws of probability, it is not possible to be performing on a machine like level year [or a broken down human human being] year in and year out.

2005 Total Independent Bases Index

[The mean is .000, while stats are adjusted for a Swiss {neutral} ballpark. 500 plate appearance minimum.]

Total Independent Bases Index [2005]

TIB Index | TIB Dividends

B. Abbreau .373 | 22.5

M. Alou .370 | 16.8

G. Anderson -.119 | 0

G. Atkins -.119 | 0

R. Baldelli .171 | 4.0

J. Bay .427 | 29.6

D. Bell -.313 | 0

R. Belliard-.400 | 0

C. Beltran .192 | 5.5

A. Beltre -.163 | 0

L. Berkman .423 | 23.6

A. Berroa -.283 | 0

C. Biggio .207 | 6.6

C. Blake .071 | .7

H. Blalock -.166 | 0

A. Boone -.222 | 0

E. Brown .245 | 9.0

P. Burrell .311 | 14.9

M. Cabrera .391 | 24.1

O. Cabrera-.218 | 0

R. Cano -.138 | 0

J. Cantu .180 | 4.9

S. Casey .111 | 1.7

V. Castilla-.003 | 0

L. Castillo .244 | 7.3

Er. Chavez .165 | 4.4

B. Clark .154 | 3.4

R. Clayton-.270 | 0

C. Counsell .115 | 2.0

C. Crawford .293 | 13.4

C. Crisp .264 | 10.1

D DeJesus .227 | 6.3

J. Damon .219 | 7.1

D. Dellucci .323 | 13.4

A. Dunn .372 | 22.2

R. Durham .219 | 6.8

J. Dye .224 | 7.3

D. Eckstein-.001 | 0

J. Edmonds .383 | 20.8

J. Encarnicion .218 | 6.7

M. Ensberg .232 | 8.9

A. Everitt-.234 | 0

C. Everitt -.001 | 0

P. Feliz-.130 | 0

C. Figgins .293 | 13.5

S. Finley-.296 | 0

C. Floyd .340 | 17.3

L. Ford-.192 | 0

R. Furcal .258 | 10.
3
J. Giambi .418 | 24.2

J. Gibbons .250 | 8.7

B. Giles .430 | 29.2

M. Giles .259 | 10.2

T. Glaus .330 | 16.2

L. Gonzalez .143 | 8.7

S. Green .249 | 9.8

K. Griffey, Jr .376 | 16.4

M. Grudzeilanik -.176 | 0

V. Guerrero .425 | 25.5

J. Guillen .208 | 6.4

T. Hafner .429 | 25.2

B. Hall .288 | 12.1

S. Hatteberg-.282 | 0

T. Helton .402 | 23.3

S. Hillenbrand-.108 | 0

E. Hinske .068 | .7

M. Holliday .252 | 8.0

A. Huff .131 | 2.6

R. Ibanez .246 | 9.8

T. Iguchi .249 | 8.4

B. Inge .147 | 3.5

G. Jenkins .13.2

N. Johnson .372 | 18.1

A. Jones .337 | 18.2

J. Jones .118 | 2.0

D. Jeter .259 | 10.7

J. Kendell-.326 | 0

J. Kent .364 | 19.7

R. Klesco .256 | 9.0

P. Kenerko .333 | 17.5

M. Kotsay-.153 | 0

J. Lane .251 | 9.1

M. Lawton .166 | 3.9

C. Lee .247 | 9.9

D. Lee .435 | 29.9

F. Lopez .265 | 10.4

M. Lowell-.232 | 0

J. Lugo .261 | 10.8

R. Mackowiak-.133

V. Martinez .265 | 10.3

H. Matsui .269 | 11.7

G Matthews, Jr .075 | .7

J. Mauer .208 | 5.6

K. Mench .208 | 3.0

K. Millar-.002 | 0

C. Monroe .169 | 4.5

M. Mora .217 | 7.0

J. Morneau-.138 | 0

B. Mueller .194 | 5.6

D. Ortiz .432 | 29.7

L. Overbay .091 | 1.3

N. Perez-.288 | 0

J. Pierre .171 | 4.8

P. Polonco .218 | 6.1

A. Pujols .472 | 35.9

A. Ramirez .336 | 13.8

M. Ramirez .402 | 24.6

J. Randa .130 | 2.6

J. Reed -.181 | 0

E. Rentaria-.177 | 0

J. Reyes .131 | 2.8

A. Rios-.210 | 0

A. Rodriguez .455 | 33.5

B. Roberts .402 | 23.2

D. Roberts .308 | 10.9

I. Rodriguez -.121 | 0

J. Rollins .170 | 4.5

A. Rowand-.193 | 0

R. Sexson .380 | 22.5

G. Sheffield .325 | 16.2

G. Sizemore .300 | 14.2

A. Soriano .237 | 8.7

S. Stewert-.234 | 0

I. Suzuki .258 | 10.7

M. Sweeney .303 | 11.2

N. Swisher .071 | .7

W. Taveras-.188 | 0

M. Teixeira .362 | 21.3

C. Tracy .314 | 14.3

J. Uribe -.220 | 0

C. Utley .352 | 18.2

J. Varitek .285 | 10.8

O. Vizquel .013 | 0

V. Wells .131 | 2.7

B. Wilkerson .216 | 6.9

B. Williams-.242 | 0

P. Wilson .160 | 3.5

J. Wilson-.306 | 0

R. Winn .333 | 17.7

D. Wright .400 | 25.7

D. Young .170 | 3.7

M. Young .207 | 6.8

G. Zaun .046 | .3

Posted by blues/blueblood at 10:48 PM CST
Updated: Thursday, 9 February 2006 2:21 AM CST
Friday, 18 November 2005
While the first decade of the new millenium is already halfway finished at a record pace, results here will not solve the deficit or any groundbreaking problems. I have decided to show results of my geeky, yet not so exciting Sandwich Season Team Runs Rating[SSTRR].

THE FORMULA

1) Determine team and year to do calculations.
2) Team Runs For^/Team Runs Against^ = [a]
3) Total League Runs^-Team Runs For^ = [b]
4) Total League Runs^-Team Runs Against^ = [c]
5) [b/c] Rest of League Run Ratio = [d]
6) [a-d]= Team Rating
7) Repeat steps 1-6 for the year previous to and the year after the target year.
8) Add up the three seperate results to create what I call a "Sandwich Season."

What Is SSTRR?

The Sandwich Season Team Runs Rating determines what team has the best results simply through runs for and runs against. These runs are calculated using ratio stats [which I favor as oppossed to a percentage for many reasons], and then are formulated through squared results. The reason for the squared results? It not so simply causes the results to show better accuracy because squaring numbers greatly improves the laws of probability.

The Target Season Team Run Rating [TSTRR] is simply one season applied to the SSTRR.

Why do I prefer to compare against league leaders rather than total individual results? Simply said, individual results shows talent, which is only half the story. What I am looking for is value. Value is what documents wins, while talent is comparable to a checklist of good qualities.

Why "sandwich seasons?" One season could be a fluke, and the next one not so hot. A fluke is another way of showing talent, bypassing long-term value.

Top 10 SSTRR 2000-2005 (W-L)

1) Seattle [2001-03] 2.1069 (302-184 .621)
2) Seattle [2000-02] 1.9011 (300-186 .617)
3) A's [2001-2003] 1.8076 (301-185 .619)
4) A's [2000-02] 1.7379 (296-186 .614)
5) Boston [2002-04] 1.6140 (286-200 .588)
6) Atlanta [2002-2004] 1.5221 (298-186 .616)
7) St. Louis [2003-05] 1.5114 (290-196 .597)
8) A's [2002-04] 1.4523 (296-189 .610)
9) Yankees [2001-03] 1.4339 (299-184 .619)
10. St. Louis [2002-04] 1.3739 (287-199 .591)

Top 5 TSTRR 2000-2005 (W-L)

1. Seattle [2001] 1.1898 (116-46 .716)
2. Oakland [2001] .8814 (102-60 .630)
3. Angels [2002] .7488 (99-63 .611)
4. St. Louis [2004] .6854 (105-57 .648)
5. Boston [2002] .6714 (93-69 .574)

Top 10 SSTRR of the 1990's (W-L)

1. Atlanta [1997-99] 2.4963 (310-176 .638)
2. NY Yankees [1997-99] 2.3654 (308-178 .634)
3. Atlanta [1996-98] 2.3044 (303-183 .623)
4. Atlanta [1998-2000] 2.1677 (304-182 .626)
5. Yankees [1996-98] 2.0734 (302-184 .621)
6. Cleveland [1994-96] 1.9148 (265-153 .634)
7. Yankees [1998-2000] 1.8313 (299-186 .616)
8. Atlanta [1992-94] 1.7949 (270-168 .616)
9. Atlanta [1993-95] 1.7788 (262-158 .624)
10. Atlanta [1995-97] 1.7089 (287-181 .613)

Top 5 TSTRR 1990's (W-L)

1. Yankees [1998] 1.1678 (114-48 .704)
2. Atlanta [1998] 1.0236 (106-56 .654)
3. Cleveland [1995] .9183 (100-44 .694)
4. Atlanta [1997] .8562 (101-61 .623)
5. Arizona [1999] .8067 (100-62 .617)

TO BE CONTINUED.

Posted by blues/blueblood at 8:46 PM CST
Updated: Saturday, 3 December 2005 7:37 AM CST
Tuesday, 23 August 2005
Positional Marginal Value Through Standard Deviation
Not all ball players are created equal. But then again, a player positioned at one defensive area has a different offensive value if he is to switch positions.

Why is the above statement true? Because these position's offensive values are radically different depending on the offensive average of each position. Historically the positions with a higher offensive average include the First Base, Third Base, Left Field, and Right Field, while positions up the middle [Catcher, Second Base, Shortstop, and Center Field] tend to be documented as being weaker.

One method for documenting positional marginal value is to first find the data for league positional average, [which I found on the Baseball Prospectus website. With the on base percentage, slugging percentage, and total plate appearances, I determine runs created percentage [which I simply multiply the on base percentage by slugging percentage].

Then I dig until I locate the individual player's stats and multiply the on base percentage by the slugging percentage and multiply this figure by plate appearances to calculate a runs created figure. Adjust runs created with the ballpark factor and divide runs created by total plate appearances. Then subtract the player's runs created average from the league average and square this figure to give us a standard deviation version of an average.

So you now have a whole series of numbers in front of you, but do not know how to read all of this. Below is an explanation how to read this set of numbers.

1. Dominent [Bonds .109561]
2. Superstar [Pujols .010609]
3. Excellent [Beltre .009025]
4. Good [Soriano .000576]
5. Above Average [Burrell .000036]
6. Marginal [Jenkins .000004]
7. Exactly Average [.000000 Roberts]
8. Below Average [Cintron -.000256]

Lastly, remember, do not compare players from different positions against each other. Simply said, the average positional data pool differs amongst each position.

2004 Positional Marginal Value [Standard Deviation]

Catcher
1) I. Rodriguez .004356
3) J. Lopez .002601
4) J. Varitek .002601
5) V. Martinez .002025

First Base
1) A. Pujols .010609
2) T. Helton .009801
3) J. Thome .002809
4) M. Teixeira .002025

Second Base
1) M. Loretta .003136
2) J. Kent .001849
3) J. Uribe .001156
4) R. Durham .001024
5) A. Soriano .000576

Shortstop
1) Ca. Guillen .005329
3) K. Greene .001369
4) J. Rollins .001296
5) D. Jeter .001156

Third Base
1) A. Beltre .009025
2) S. Rolen .009025
3) M. Mora .006241
4) A. Ramirez .003249
5) A. Rodriguez .001764

Outfield
1) B. Bonds .109561
2) J. Edmonds .01404
3) L. Berkman .008649
4) M. Ramirez .007921
5) P. Guerrero .007744
6) J.D. Drew .007569
7) B. Abbreau .005041
8) A. Dunn .004096
9) G. Sheffield .003844
10) H. Matsui .003025
11) C. Beltran .002601
12) Er. Chavez .002209
13) A. Rowand .001600
14) M. Alou .001444
15) I. Suzuki .001296

Posted by blues/blueblood at 12:20 AM CDT
Updated: Wednesday, 24 August 2005 9:05 PM CDT
Friday, 19 August 2005
Runs Created Over League Average
Many moons ago,sabermetric magician Bill James introduced us w/the idea of a new statistic called runs created." Basically, this mess of numbers is suppose to represent the amount of estimated runs the individual creates, while other team members are not taken into account ...read team independent statistics.

To take things even further, a stat whizzing through my caffein soaked brain suddenly took form of the name Runs Created Over League Average . After wracking my brain w/my over-simplified version of runs created [ob% times Slg% times plate appearances and adjust the runs created by the park factor again before dividing by plate appearances], I toss the figure located exactly up from this spot into the statistical blender [figure out the league average and subtract you figure from the league average]. When an individual's RC% is higher than the league average, add the points above average to .500, while subtracting points below average from .500.

Most of you would wonder how this set of numbers should be examined. Simply, when the percentage reads above .500, the player is above average, while in reverse the player is below average. A player near the .500 mark either positively or negatively is marginal, while a player above .550 is great. Someone below .490 ought to think about hanging up their cleats or must play way above the margin defensively. To make it easier for the average fan to comprehend this systematic series of numbers, I have created a tier system to classify the quality of the player. Players in tier one are the elite players of the league, while tier two represents the slightly worse, but not terrible horrible players to marginal players. The third teir are players who risk being sent to the minors.

2004 Runs Created Above League Average
[Minimum 500 Plate Appearances]

Tier I [.550+]
.845 B. Bonds
.639 J. Edmonds
.639 A. Pujols
.619 T. Helton
.614 S. Rolen
.614 A. Beltre
.612 L. Berkman
.606 J.D. Drew
.605 T. Hafner
.599 V.Guerrero
.589 M. Ramirez
.589 B. Abbreau
.586 J. Thome
.584 M. Mora
.584 A. Dunn
.574 D. Ortiz
.573 G. Sheffield
.572 S. Casey
.569 C. Beltran
.568 C. Guillen
.567 A. Ramirez
.566 H. Matsui
.560 M. Loretta
.558 I. Rodreguez
.558 E. Durazo
.556 C. Lee
.554 M. Cabrerra
.554 M. Alou
.554 A. Rodriguez
.553 B. Wilkerson
.551 M. Lowell
.551 L. Overbay

Tier II [.500-.549]
.549 M. Teixeira
.547 P. Nevin
.545 P. Konerko
.545 B. Giles
.543 J. Kent
.542 C. Wilson
.541 I. Suzuki
.541 A. Rowand
.539 A. Huff
.538 V. Martinez
.537 Javey Lopez
.537 J. Burnitz
.536 Jo Guillen
.536 J. Varitek
.534 J. Blake
.534 J. Bagwell
.533 C. Jones
.532 R. Durham
.531 D. Lee
.530 K. Millar
.529 J. Damon
.528 O. Infante
.528 D. Jeter
.527 T. Martinez
.527 S. Green
.526 M. Piazza
.526 A. Jones
.525 P. Burrell
.525 M. Kotsay
.524 S. Sosa
.523 S. Finley
.522 J. Kendell
.522 D. Bell
.521 L. Ford
.521 K. Greene
.520 J. Wilson
.520 H. Blalock
.519 V. Castilla
.519 S. Hillenbrand
.519 G. Jenkins
.519 C. Pena
.518 R. Ibanez
.517 J. Pierre
.517 M. Cameron
.517 B. Williams
.516 M. Biggio
.516 J. Rollins
.514 M. Young
.514 M. Lawton
.514 M. Bellhorn
.514 E. Byrnes
.514 C. Crisp
.513 J. Uribe
.512 J. Conine
.510 C. Crawford
.509 R. Belliard
.509 P. Polonco
.508 T. Hunter
.508 J. Jiminez
.507 M. Lieberthall
.506 T. Wiggington
.506 S. Hatteberg
.506 R. Palmeiro
.505 J. Dye
.504 R. Winn
.504 R. Freel
.504 P. Feliz
.504 J. Cruz, Jr
.502 M. Tucker
.502 A. Kennedy
.501 P. Lo Duca
.501 M. Grissom
.501 R. Furcal
.500 T. Womack
.500 J. Randa
.500 A. Soriano

Tier III [.499-.000]
.498 O. Hudson
.498 C. Patterson
.497 Mackowiak
.497 J. Vizquel
.496 B. Higginson
.495 R. Baldelli
.495 K. Matsui
.495 J. Gerut
.495 E. Rentaria
.495 C. Tracy
.494 L. Castilla
.494 L. Bigbie
.494 J. Lugo
.493 R. Hildago
.492 C. Figgons
.491 S. Burroughs
.489 T. Batista
.489 E. Alfonzo
.489 D. Bautista
.489 Bo Crosby
.487 J. Pierzenski
.485 J. Jones
.485 Br. Boone
.483 J. Payton
.483 E. Martinez
.482 O. Cabrera
.482 Ed. Chavez
.479 T. Redman
.479 R. Clayton
.479 B. Roberts
.477 A. Berroa
.476 A. Gonzalez
.475 J. Crede
.471 R. Johnson
.471 D. Eckstein
.470 C. Guzman
.468 C. Counsell
.466 E. Hinske
.466 A. Miles
.465 A. Cintron
.461 S. Podsednik

Posted by blues/blueblood at 10:08 PM CDT
Updated: Saturday, 20 August 2005 12:55 AM CDT
Saturday, 9 July 2005
The Importance of Independent Stats
In the world of insider baseball, known to most as Major League Baseball, most of the statistics posted by this group of individuals end up resembling a mess of illogical mathematics.

So how do you make a set of numbers resemble a set of logical calculations? The answer is simply using independent statistics in which other batters or baserunners are not to be included in the calculations. Some examples of these incomplete, team orientated stats include what most purists consider essential components, runs, and runs batted in. The problem is that these are a set of numbers which only tell half the picture of what an individual is doing on the field.

A quick, yet well thought out stat I have assembled is what I call, "Independent Bases Percentage." I originally used this set of numbers to be included in a stat very close to Bill Jame's Secondary percentage. The big difference from James is that I include base on balls as part of the primary stat, while James beleives that bases on ball should be part of the secondary stat. Another difference is that I have added on an extra bases : stolen bases ratio to document how the player achieves the secondary bases.

In the end, this stat ends up resembling a revised version of OPS, but instead of counting bases twice, I only count the extra bases and stolen bases in the secondary portion of the stat. Also, stolen bases are included in this part of the stat while not being included in the ops.

Primary Independent Base Percentage: On Base Percentage adjusted to the ballpark factor.

Secondary Independent Base Percentage: All extra bases [which means not counting first base with the extra base calculations] plus stolen bases divided by at bats plus stolen bases plus caught stealing. Also, adjust this with the ballpark factor. Additionally, I document the extra bases to stolen bases by showing a ratio between these two ways of base advancement.

Total Independent Base Percentage: Simply add up the primary and secondary category to display the final calculation.

2004 Stats

Primary Independent Base Percentage
Barry Bonds .600
Lance Berkman .450
Jim Edmonds .438
J.D. Drew .436
Albert Pujols .435
Travis Hafner .430
Scot Rolen .429
Bobby Abbreau .426
Tod Helton .422

Secondary Independent Base Percent [w/extra base : stolen base ratio]
Barry Bonds .451 [97:3]
Jim Edmonds .367 [96:4]
Albert Pujols .345 [98:2]
Carlos Beltron .342 [80:20]
Jim Thome .304 [100:0]
Scot Rolen .302 [97:3]
Manny Ramirez .296 [99:1]

Total Independent Base Percentage
Bobby Bonds 1.051
Jim Edmonds .805
Albert Pujols .780
Scot Rolen .731
Carlos Beltron .728
Travis Hafner .718
J.D. Drew .716
Bobby Abbreau .714

Posted by blues/blueblood at 1:38 AM CDT
Updated: Saturday, 9 July 2005 3:05 AM CDT
Monday, 27 June 2005
After reading a writeup by Bill James in his "Historical Baseball Abstract," I have come to realize that the statistic "secondary average" has some major flaws [bb+ extra bases + stolen bases-caught stealing divided by plate appearances].

My biggest gripe is including walks in the secondary average. I consider receiving base on balls a primary part of batting, not a secondary statistic. The player's primary goal is to get on base anyway possible, including a hit, a base on ball or score a run via home run. Even though hits are worth more than base on balls, batters have been trained and disciplined to take a substancial amount of base on balls because this increases the value of a player. Therefore, I advocate removing the base on balls statistic from the secondary average.

Another misconception used not only in secondary bases, but many other statistics is that "all bases are created equal." This is another of many numerous baseball myths. Hits are of more value than base on balls because they have a much higher potential to create a run for a team. The only way to create a run w/a walk while at home plate is w/the bases loaded [a walked in run]. Stolen bases have very little value unless you sucessfully steal 75% or more of these bases prolifically. Only a few elite players have ever reached this stature [ie; Rickey Henderson, Lou Brock, Ty Cobb, Billy Hamilton, etc]. Most frequent base stealers do so because they lack power or other batting qualities. On the other hand, the above statement is not always true, [see Ty Cobb's and Barry Bond's stats].

What I have done to correct this all too simple "napkin statistic" is to keep the original total bases count, yet reduce the value of the base on ball, stolen bases, and caught stealing. This reads as an approximate total bases per/27 outs. Another way of saying this is that this stat is calculated as if the player is batting one through nine on a batting card in a nine inning game [27 outs].

The statistical formula starts: [h + .75 bb] divided by at bats minus hits and multiply by twenty seven, which represents the amount of adjusted base on balls and regular hits per twenty seven outs [a complete game]. As always, I adjust the base totals to the park factor. [This factor is necessary since every ballpark has different nooks and crannies, some fences are longer and/or higher than others, some climates are more humid than others, and in one case, the altitude has a major impact on the field of play.]

Next, extra bases are added up to the adjusted stolen base count. Each stolen base is worth .667 of a stolen base, while each time a player is caught stealing is worth minus 1.333 stolen base. Once again I adjust the extra bases and stolen bases with the ballpark factor. Then I divide this figure by at bats minus hits. This number represents the number of extra bases and adjusted stolen bases per 27 outs.

To sum things up, I add up the above two calculations, which gives you total bases plus the adjusted base on balls, stolen bases and caught stealing.

So what is this data suppossed to represent? It is meant to document the exact amount of bases off of a hit, while attempting to adjust the base on balls, stolen base and caught stealing calculations. Since walks, stolen bases, and caught stealing are worth considerably a lot less than a hit, it deserves a lesser value than a total base.

Here are the 2004 MLB Adjusted Base Value top ten in the following categories.

Primary Base Value [h x .75 bb divided by ab-hits x 27]per 27/outs

Bonds 34.036
Berkman 19.379
Pujols 19.364
Edmonds 19.241
Drew 18.525
Rollen 18.184
Loretto 18.022
Hafner 17.963
Suzuki 17.898
Guererro 17.889

Secondary Base Value [extra bases + .667 stolen bases - 1.333 caught stealing divided by at bats minus hits x 27] p/27 outs

Bonds 18.832
Edmonds 14.586
Pujols 14.114
Beltre 13.296
Beltron 12.997
Dunn 12.367
Rollen 12.201
Guererro 12.103
Hafner 11.630
Thome 11.049

Total Adjusted Base Value [Primary Base Value + Secondary Base Value]

Bonds 52.886
Edmonds 33.827
Beltre 31.138
Rollen 30.385
Guererro 29.992
Hafner 29.603
Drew 29.100
Berkman 29.032
Dunn 28.748
Beltron 28.414

Posted by blues/blueblood at 9:42 AM CDT
Updated: Thursday, 30 June 2005 12:23 AM CDT