A Call to Arms  MoP Mechanics Testing
Moderators: Fridmarr, Worldie, Aergis, theckhd
Re: A Call to Arms  MoP Mechanics Testing
quick check of build 15752 (current) shows that holy wrath still appears to reset the swing timer.
Haven't been able to snag another windsong enchant the few times I've logged in  either absurd price (last one was over 75K) or lack of auction.
Haven't been able to snag another windsong enchant the few times I've logged in  either absurd price (last one was over 75K) or lack of auction.
 benebarba
 Posts: 2469
 Joined: Tue Oct 19, 2010 7:30 am
Re: A Call to Arms  MoP Mechanics Testing
Not a test, per se, but a quote that will save us from doing a lot of annoying testing:
"Theck, Bringer of Numbers and Pounding Headaches," courtesy of GrehnSkipjack.
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty

theckhd  Moderator
 Posts: 7658
 Joined: Thu Jul 31, 2008 3:06 pm
 Location: Harrisburg, PA
Re: A Call to Arms  MoP Mechanics Testing
Armor and physical damage reduction :
Base stats/attributes : level 90, different races. Being fully naked, report primary stats (Str/Agi/Int/Stam), total AP and physical/spell crit chance (character sheet), total HP and MP.
 Player level  90
 Talents/Glyphs  irrelevant
 Gear  Prot/Ret, with high Str/AP; you should have a few spare parts available, as you'll need to shift the total AP (e.g. base total : 10k, and then {10.5k 11k 11.5k 12k})
 Weapon  Club (must be exactly this one), purchasable from vendors
 Buffs/Debuffs  ideally none
 Target  level 93 dummy (ideally less crowded)
 Attack sequence  only autoattacks, from the back
 Goal  equip the base AP gear (note the value somewhere), attack the dummy for 1 min; add/replace one piece (changing the AP, noting the new value), attack for another 1 min; then repeat the last step three more times, to have five data sets
Base stats/attributes : level 90, different races. Being fully naked, report primary stats (Str/Agi/Int/Stam), total AP and physical/spell crit chance (character sheet), total HP and MP.

tlitp  Posts: 554
 Joined: Mon Jul 27, 2009 3:25 pm
Re: A Call to Arms  MoP Mechanics Testing
Another potentially useful quote:
The two links you mention are old, but the ideas are accurate. White attacks are still ‘one roll’ (Miss Dodge Parry Block Glance Crit Hit), and yellow attacks are still ‘two roll’ (Miss Dodge Parry Block, Crit). The only change to that is that Block against players is an additional separate roll now. Glances can still only occur on white attacks. I believe our recent blog post about this stuff included all off the relevant chances for these, but if you have more specific questions, feel free to ask.
"Theck, Bringer of Numbers and Pounding Headaches," courtesy of GrehnSkipjack.
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty

theckhd  Moderator
 Posts: 7658
 Joined: Thu Jul 31, 2008 3:06 pm
 Location: Harrisburg, PA
Re: A Call to Arms  MoP Mechanics Testing
I need data regarding Str>Parry conversions and dodge/parry DR equations. All of that can be had by equipping different amounts of gear and reporting the dodge and parry tooltips (i.e. "X rating gives Y% dodge (before DR)" as well as reporting your overall dodge chance and Strength value).
lvl 90 belf
Str  4914
Dodge chance of 6.03%
Dodge of 2896 adds 2.80%
Parry Chance  10.19%
Parry of 1460 adds 1.41%
Str  5308
Dodge chance of 6.21%
Dodge of 3077 adds 2.97%
Parry chance of 10.73%
Parry of 1559 adds 1.51%
Str  5483
Dodge chance of 6.54%
Dodge of 3416 adds 3.30%
Parry chance of 10.68%
Parry of 1318 adds 1.27%
Str  5956
Dodge Chance of 5.45%
Dodge of 2322 adds 2.24%
Parry Chance of 11.19%
Parry of 1305 adds 1.26%
Str  6123
Dodge chance of 6.26%
Dodge of 3128 adds 3.02%
Parry chance of 11.50%
Parry of 1429 adds 1.38%
Str  6371
Dodge chance of 5.80%
Dodge of 2668 adds 2.85%
Parry Chance of 11.67
Parry of 1326 adds 1.28%
Str  6589
Dodge Chance  6.60%
Dodge of 3476 adds 3.36%
Parry Chance  11.40%
Parry of 825 adds 0.80%
Str  6751
Ddoge chance  6.43%
Ddoge of 3306 adds 3.19%
Parry Chance  11.58%
Parry of 823 adds 0.80%
The Element of Forum Hyperbole

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Klaudandus  Posts: 10825
 Joined: Thu Apr 02, 2009 7:08 am
 Location: Texas' Armpit
Re: A Call to Arms  MoP Mechanics Testing
<edit> I should note that I believe there's a typo in your data. The dodge from data set #6 should be 2.58%, not 2.85%. It's the only outlier, and it can't be reconciled with the rest in any way. If I calculate the results without that data point, I get a result that's consistent with 2668 dodge rating giving 2.58% dodge preDR.
Based on the rating to percentage conversions, the coefficient for both dodge and parry seems to be the same. The dodge data puts the range between 1034.7 and 1036.0, the parry data puts it between 103.8 and 105.2. Together, we can narrow this range to 1034.7105.2, making 1035.0 a pretty reasonable estimate (given the accuracy limitations inherent in rounded data). I came about this through simple unit testing, but curve fitting gives a similar result:
The rounding is really the limitation on the accuracy, I think. So we'll need more data to be able to pin it down to better than +/ 0.5.
Based on the rating to percentage conversions, the coefficient for both dodge and parry seems to be the same. The dodge data puts the range between 1034.7 and 1036.0, the parry data puts it between 103.8 and 105.2. Together, we can narrow this range to 1034.7105.2, making 1035.0 a pretty reasonable estimate (given the accuracy limitations inherent in rounded data). I came about this through simple unit testing, but curve fitting gives a similar result:
General model:
f(x) = x/(1035+a)
Coefficients (with 95% confidence bounds):
a = 0.2393 (0.4219, 0.9005)
Goodness of fit:
SSE: 0.0001106
Rsquare: 1
Adjusted Rsquare: 1
RMSE: 0.002715
The rounding is really the limitation on the accuracy, I think. So we'll need more data to be able to pin it down to better than +/ 0.5.
"Theck, Bringer of Numbers and Pounding Headaches," courtesy of GrehnSkipjack.
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty

theckhd  Moderator
 Posts: 7658
 Joined: Thu Jul 31, 2008 3:06 pm
 Location: Harrisburg, PA
Re: A Call to Arms  MoP Mechanics Testing
I'm having trouble curvefitting the dodge data. There are a few peculiarities. For example, with 2.8% preDR dodge and 3% base dodge, you'd expect less than 5.8% total dodge after DR. Yet we have 6.03%.
It's almost as if they removed diminishing returns completely. I can fit the data pretty well with
I can also fit it with
which is the old formula. k comes out to 0.9517+/0.01, which is reasonable, but C is 562.1+/517, which I don't trust (for obvious reasons). C is the cap on avoidance, and is currently around 65ish. That falls within the confidence bounds, but it's not at all confidenceinspiring to have such a large range.
I was hoping to isolate dodge individually to figure out the DR equation, and then use those results to figure out the STR>Parry ratio, but if I'm going to do that I'll need more data at much smaller dodge rating values (i.e. several data points between 1002000 rating). Or much larger values, for that matter, but that may be harder to do.
It's almost as if they removed diminishing returns completely. I can fit the data pretty well with
 Code: Select all
total_dodge = (predr_dodge_from_rating+3)/0.9623
I can also fit it with
 Code: Select all
1/total_dodge = 1/C + k/(3+predr_dodge_from_rating)
which is the old formula. k comes out to 0.9517+/0.01, which is reasonable, but C is 562.1+/517, which I don't trust (for obvious reasons). C is the cap on avoidance, and is currently around 65ish. That falls within the confidence bounds, but it's not at all confidenceinspiring to have such a large range.
I was hoping to isolate dodge individually to figure out the DR equation, and then use those results to figure out the STR>Parry ratio, but if I'm going to do that I'll need more data at much smaller dodge rating values (i.e. several data points between 1002000 rating). Or much larger values, for that matter, but that may be harder to do.
"Theck, Bringer of Numbers and Pounding Headaches," courtesy of GrehnSkipjack.
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty

theckhd  Moderator
 Posts: 7658
 Joined: Thu Jul 31, 2008 3:06 pm
 Location: Harrisburg, PA
Re: A Call to Arms  MoP Mechanics Testing
so, what do you need me to do? i can sit next to a reforger and try to get as many dodge and parry ratings... would that help?
lvl 90 belf  no buffs
Dodge Chance 3.91%
Dodge of 833 adds 0.80%
Dodge chance 4.25%
Dodge of 1162 adds 1.12%
Dodge chance 4.56%
Dodge of 1457 adds 1.41%
Dodge chance of 4.85%
Dodge of 1738 adds 1.68%
Dodge chance of 5.04%
Dodge of 1924 adds 1.86%
Dodge chance of 5.28%
Dodge of 2155 adds 2.08%
Dodge chance of 5.39%
Dodge of 2261 adds 2.18%
Dodge chance of 5.68%
Dodge of 2553 adds 2.47%
Dodge chance of 5.98%
Dodge of 2853 adds 2.76%
Dodge chance of 6.31%
Dodge of 3184 sffd 3.08%
Dodge chance of 6.58%
Dodge of 3461 adds 3.34%
Dodge chance of 6.86%
Dodge of 3747 adds 3.62%
Dodge chance of 7.07%
Dodge of 3965 adds 3.83%

Str  6430 (will stay the same for the rest of this list)
Parry chance of 10.96%
Parry of 569 adds 0.55%
Parry chance of 11.24%
Parry of 843 adds 0.81%
Parry chance of 11.53%
Parry of 1126 adds 1.09%
Parry chance of 11.79%
Parry of 1380 adds 1.33%
Parry chance of 12.14%
Parry of 1730 adds 1.67%
Parry chance of 12.41%
parry of 1997 adds 1.93%
Parry chance of 12.76%
Parry of 2349 adds 2.27%
Parry chance of 13.07%
Parry of 2658 adds 2.57%
Parry chance of 13.26%
Parry of 2844 adds 2.75%

That's as much as I can squeeze out of Parry without changing my str at any point.
lvl 90 belf  no buffs
Dodge Chance 3.91%
Dodge of 833 adds 0.80%
Dodge chance 4.25%
Dodge of 1162 adds 1.12%
Dodge chance 4.56%
Dodge of 1457 adds 1.41%
Dodge chance of 4.85%
Dodge of 1738 adds 1.68%
Dodge chance of 5.04%
Dodge of 1924 adds 1.86%
Dodge chance of 5.28%
Dodge of 2155 adds 2.08%
Dodge chance of 5.39%
Dodge of 2261 adds 2.18%
Dodge chance of 5.68%
Dodge of 2553 adds 2.47%
Dodge chance of 5.98%
Dodge of 2853 adds 2.76%
Dodge chance of 6.31%
Dodge of 3184 sffd 3.08%
Dodge chance of 6.58%
Dodge of 3461 adds 3.34%
Dodge chance of 6.86%
Dodge of 3747 adds 3.62%
Dodge chance of 7.07%
Dodge of 3965 adds 3.83%

Str  6430 (will stay the same for the rest of this list)
Parry chance of 10.96%
Parry of 569 adds 0.55%
Parry chance of 11.24%
Parry of 843 adds 0.81%
Parry chance of 11.53%
Parry of 1126 adds 1.09%
Parry chance of 11.79%
Parry of 1380 adds 1.33%
Parry chance of 12.14%
Parry of 1730 adds 1.67%
Parry chance of 12.41%
parry of 1997 adds 1.93%
Parry chance of 12.76%
Parry of 2349 adds 2.27%
Parry chance of 13.07%
Parry of 2658 adds 2.57%
Parry chance of 13.26%
Parry of 2844 adds 2.75%

That's as much as I can squeeze out of Parry without changing my str at any point.
The Element of Forum Hyperbole

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Klaudandus  Posts: 10825
 Joined: Thu Apr 02, 2009 7:08 am
 Location: Texas' Armpit
Re: A Call to Arms  MoP Mechanics Testing
Realized I was making a dumb mistake  I was putting base dodge into the DR formula, when traditionally base dodge isn't affected by DR. That makes a world of difference in the results. Combining the earlier data set with the one that Klaud just provided, here's what I get:
If I fix C at it's cataclysm value (near the upper end of the confidence bound on C), I get:
At this point, it's not completely conclusive  we have two free parameters, and the data doesn't deviate enough from linearity over this range to feel too confident about either, but if I had to guess I'd say they kept the old caps and simply changed the constant k. Note that since k<1, this means that you actually gain avoidance from the DR equation at low levels (like we are here). It isn't until you stack an appreciable amount (probably 1015%) that it starts to fall behind the preDR values. In Cataclysm, k was about 0.965, so this effect was still present at very low dodge/parry (<2%), but decreasing k will make it extend up to higher values.
Working on the strength/parry data now. I was fooling around with a surface fit of the earlier parry data, and the indication is that the parry gained from STR isn't affected by DR. But now that I have some new data to play with, I should be able to tell for sure.
 Code: Select all
General model:
f(x) = 3+1/(1/C+k/(x))
Coefficients (with 95% confidence bounds):
C = 61.84 (57.08, 66.59)
k = 0.8791 (0.8753, 0.8829)
Goodness of fit:
SSE: 0.0005156
Rsquare: 1
Adjusted Rsquare: 1
RMSE: 0.005209
If I fix C at it's cataclysm value (near the upper end of the confidence bound on C), I get:
 Code: Select all
General model:
f(x) = 3+1/(1/65.631440+k/(x))
Coefficients (with 95% confidence bounds):
k = 0.8819 (0.8811, 0.8827)
Goodness of fit:
SSE: 0.0005827
Rsquare: 1
Adjusted Rsquare: 1
RMSE: 0.005398
At this point, it's not completely conclusive  we have two free parameters, and the data doesn't deviate enough from linearity over this range to feel too confident about either, but if I had to guess I'd say they kept the old caps and simply changed the constant k. Note that since k<1, this means that you actually gain avoidance from the DR equation at low levels (like we are here). It isn't until you stack an appreciable amount (probably 1015%) that it starts to fall behind the preDR values. In Cataclysm, k was about 0.965, so this effect was still present at very low dodge/parry (<2%), but decreasing k will make it extend up to higher values.
Working on the strength/parry data now. I was fooling around with a surface fit of the earlier parry data, and the indication is that the parry gained from STR isn't affected by DR. But now that I have some new data to play with, I should be able to tell for sure.
"Theck, Bringer of Numbers and Pounding Headaches," courtesy of GrehnSkipjack.
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty

theckhd  Moderator
 Posts: 7658
 Joined: Thu Jul 31, 2008 3:06 pm
 Location: Harrisburg, PA
Re: A Call to Arms  MoP Mechanics Testing
I'll try to get more gear, I'm barely 422 ilvl  that should help me get more dodge/parry rating
The Element of Forum Hyperbole

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Klaudandus  Posts: 10825
 Joined: Thu Apr 02, 2009 7:08 am
 Location: Texas' Armpit
Re: A Call to Arms  MoP Mechanics Testing
tlitp wrote:Base stats/attributes : level 90, different races. Being fully naked, report primary stats (Str/Agi/Int/Stam), total AP and physical/spell crit chance (character sheet), total HP and MP.
From SimC's guts :
 Code: Select all
Str Agi Sta Int Spi
178 105 169 114 123 (human)
183 101 170 113 122 (dwarf)
179 102 169 114 125 (draenei)
175 107 169 117 121 (blood elf)
183 101 170 110 125 (tauren)
base hp : 146663
base mp : 60000
Conversion factors, from SimC's guts :
 Code: Select all
agi>ph.crit
1259.51806640
int>sp.crit
2533.66357421
dodge, parry
1035
block
345
phys/spell hit, exp
400
phys/spell crit, mast
700
phys/spell haste
500
The above are DBC data from b15781.
EDIT : gief dataz foar amrorz NAO.

tlitp  Posts: 554
 Joined: Mon Jul 27, 2009 3:25 pm
Re: A Call to Arms  MoP Mechanics Testing
Parry/Str data is being... confusing. With a curve fit to just the most recent data set (fixed STR), I can get a very good fit from either model:
However, the extreme difference in s suggests that we should be able to tell the difference between these by simply varying strength. So I took the entire data set and used the surface fit tool. That gives me these situations:
If I fix C in the last two versions, I get:
Neither of the versions with strengthbased parry affected by DR fit very well. One easy way to check would be to have a few data points at exactly the same parry rating but different Strength values (i.e., full gear, then take off pieces of gear that don't have parry rating on them one by one). That would probably give me a clear strength scaling to work off of. This could be done in ret gear, even, to give the largest STR range possible.
 Code: Select all
General model:
f(x) = 3+s*6430+1/(1/65.631440+k/(x))
Coefficients (with 95% confidence bounds):
k = 0.9079 (0.9025, 0.9132)
s = 0.001144 (0.001142, 0.001146)
Goodness of fit:
SSE: 0.0002031
Rsquare: 1
Adjusted Rsquare: 1
RMSE: 0.005387
 Code: Select all
General model:
f(x) = 3+1/(1/65.631440+k/(x+s*6430))
Coefficients (with 95% confidence bounds):
k = 0.7108 (0.7061, 0.7154)
s = 0.000915 (0.0009074, 0.0009227)
Goodness of fit:
SSE: 0.0002806
Rsquare: 0.9999
Adjusted Rsquare: 0.9999
RMSE: 0.006331
However, the extreme difference in s suggests that we should be able to tell the difference between these by simply varying strength. So I took the entire data set and used the surface fit tool. That gives me these situations:
 Code: Select all
General model:
f(x,y) = 3+s*y+1/(1/C+k/(x))
Coefficients (with 95% confidence bounds):
C = 30.5 (19.26, 41.74)
k = 0.8448 (0.8034, 0.8862)
s = 0.001136 (0.001129, 0.001142)
Goodness of fit:
SSE: 0.003198
Rsquare: 0.9997
Adjusted Rsquare: 0.9997
RMSE: 0.01511
 Code: Select all
General model:
f(x,y) = 3+1/(1/C+k/(x+s*y))
Coefficients (with 95% confidence bounds):
C = 4.404e+004 (9.616e+006, 9.705e+006)
k = 1.016 (0.9207, 1.111)
s = 0.001184 (0.0011, 0.001268)
Goodness of fit:
SSE: 0.03785
Rsquare: 0.9967
Adjusted Rsquare: 0.9963
RMSE: 0.052
If I fix C in the last two versions, I get:
 Code: Select all
General model:
f(x,y) = 3+s*y+1/(1/65.631440+k/(x))
Coefficients (with 95% confidence bounds):
k = 0.9024 (0.8882, 0.9166)
s = 0.001144 (0.001139, 0.001148)
Goodness of fit:
SSE: 0.005202
Rsquare: 0.9995
Adjusted Rsquare: 0.9995
RMSE: 0.01862
 Code: Select all
General model:
f(x,y) = 3+1/(1/65.631440+k/(x+s*y))
Coefficients (with 95% confidence bounds):
k = 0.726 (0.6867, 0.7653)
s = 0.0009355 (0.0008722, 0.0009987)
Goodness of fit:
SSE: 0.06458
Rsquare: 0.9944
Adjusted Rsquare: 0.994
RMSE: 0.06561
Neither of the versions with strengthbased parry affected by DR fit very well. One easy way to check would be to have a few data points at exactly the same parry rating but different Strength values (i.e., full gear, then take off pieces of gear that don't have parry rating on them one by one). That would probably give me a clear strength scaling to work off of. This could be done in ret gear, even, to give the largest STR range possible.
"Theck, Bringer of Numbers and Pounding Headaches," courtesy of GrehnSkipjack.
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty

theckhd  Moderator
 Posts: 7658
 Joined: Thu Jul 31, 2008 3:06 pm
 Location: Harrisburg, PA
Re: A Call to Arms  MoP Mechanics Testing
I'll see what I can do about that  another alternative is to get some pieces of spirit plate gear and reforge into parry.
Gimme a few and I'll do just that, need to find a fricking vendor first AND get more bagspace >=/
Edit: Actually, give me a while  getting a really bad migraine...
Gimme a few and I'll do just that, need to find a fricking vendor first AND get more bagspace >=/
Edit: Actually, give me a while  getting a really bad migraine...
The Element of Forum Hyperbole

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Klaudandus  Posts: 10825
 Joined: Thu Apr 02, 2009 7:08 am
 Location: Texas' Armpit
Re: A Call to Arms  MoP Mechanics Testing
tlitp wrote:Conversion factors, from SimC's guts :
 Code: Select all
block
345
The above are DBC data from b15781.
EDIT : gief dataz foar amrorz NAO.
Updating the code with these now, but I don't understand the block entry. It looks like mastery rating to mastery is 700, but we get 1% block per mastery unless something's changed. Is the "block" entry for block rating, in case it's still floating around on lowlevel gear?
Also, if anyone's level 90 on beta and can get the armor data set that tlitp wants, please do so. You wouldn't like him when he's angry.
"Theck, Bringer of Numbers and Pounding Headaches," courtesy of GrehnSkipjack.
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty
MATLAB 5.x, Call to Arms 5.x, Talent Spec & Glyph Guide 5.x, Blog: Sacred Duty

theckhd  Moderator
 Posts: 7658
 Joined: Thu Jul 31, 2008 3:06 pm
 Location: Harrisburg, PA
Re: A Call to Arms  MoP Mechanics Testing
working on tiltp's logs
Edit: DONE
Working on squeezing varying amounts of parry without changing my STR
Edit: DONE
Working on squeezing varying amounts of parry without changing my STR
Last edited by Klaudandus on Fri Jun 22, 2012 6:14 pm, edited 1 time in total.
The Element of Forum Hyperbole

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Flüttershy  Draenei Protection Paladin, Aerie Peak
Klaudandus  BE Protection Paladin, Feathermoon (Semiretired)

Klaudandus  Posts: 10825
 Joined: Thu Apr 02, 2009 7:08 am
 Location: Texas' Armpit
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