I've only been able to find lvl 85 dummies in orgrimmar
but i just tested Judgement on a lvl 93 and a lvl 85 dummy and the difference is -/+ 1, as you said, so should I stick to lvl 93 dummies instead?
Klaudandus
Thu Jul 05, 2012 6:39 am

set #1 set #2 set #3 set #4
Str 1624 2953 4481 5931
Agi 107
Sta 3244 5675 8587 11365
Int 117
AP 3498 6156 9212 12112
SP 1749 3078 4606 6056
Haste 0 0.76 3.07 3.07
Hit 0 0.62 0.62 1.97
Crit 5.36 5.36 6.30 7.31
Exp 2.55 3.89 3.89 3.89 set #5 set #6 set #7
Str 7580 8690 10566
Sta 15355 17365
AP 15410 17630 21382
SP 7705 8815 10691
Haste 4.40 4.40
Hit 1.97 3.37
Crit 8.53 8.53
Exp 3.89 3.89
Klaudandus wrote:It's possible I fucked up something on the previous sets -- or rather, it's almost certain -- considering I just logged in back to beta and verified that CS is giving me ~4778 damage
I'm thinking I probably equipped the epic pvp mace by accident
EDIT: Just went back and equipped the mace and the shield, and removed my str trinkets... I hit CS and almost matches the results of #5 from last week... I fail orz
Q:SOTR hits for about 1/4th of what it hits for on live. CS/HOTR also hit for much, much less.
A:If you post approximate damage numbers for those attacks along with your character level and average ilevel (just the average is fine), we can compare them to our numbers.
theckhd wrote:Your SotR data exactly matched the tooltip, 617+0.54*AP.
General model:
f(x) = 0.6545*(a*x+1123)
Coefficients (with 95% confidence bounds):
a = 1.676 (1.648, 1.704)
Goodness of fit:
SSE: 1.591e+004
R-square: 0.9988
Adjusted R-square: 0.9988
RMSE: 47.67Yeah, we will try to add dodge from Agi to the Agi and dodge tooltips. Currently they’re just included in the title of each of those tooltips, which are final values after summing all of the sources and applying DR. The “(before diminishing returns)” line in the dodge tooltip, refers just to the “dodge of X adds Y% dodge” line, not the total. It’s somewhat confusing right now. All of this goes for Strength / parry as well.
hanova_fit =
General model:
hanova_fit(x) = a*x+b
Coefficients (with 95% confidence bounds):
a = 0.4685 (0.4676, 0.4694)
b = 1254 (1248, 1260)
hanova_gof =
sse: 181.8413
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 4.2643
hanova_fit2 =
General model:
hanova_fit2(x) = a*x+b
Coefficients (with 95% confidence bounds):
a = 0.468 (0.4665, 0.4696)
b = 847.9 (837.1, 858.7)
hanova_gof2 =
sse: 576.4841
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 7.5927
sot_fit =
General model:
sot_fit(x) = a*x
Coefficients (with 95% confidence bounds):
a = 0.14 (0.14, 0.1401)
sot_gof =
sse: 0.0533
rsquare: 1.0000
dfe: 7
adjrsquare: 1.0000
rmse: 0.0872cens_fit =
General model:
cens_fit(x) = a*x+b
Coefficients (with 95% confidence bounds):
a = 0.6901 (0.69, 0.6901)
b = 790.2 (789.6, 790.8)
cens_gof =
sse: 1.6780
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 0.4096j_fit =
General model:
j_fit(x) = 702+a*x+b
Coefficients (with 95% confidence bounds):
a = 1.429 (1.429, 1.429)
b = -0.2869 (-1.154, 0.5804)
j_gof =
sse: 3.7250
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 0.6103hw_fit =
General model:
hw_fit(x) = a*x+b
Coefficients (with 95% confidence bounds):
a = 0.91 (0.91, 0.9101)
b = 4301 (4300, 4301)
hw_gof =
sse: 1.3406
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 0.3661cons_fit =
General model:
cos_fit(x) = a*x+b
Coefficients (with 95% confidence bounds):
a = 0.12 (0.12, 0.12)
b = 68.98 (68.89, 69.07)
cons_gof =
sse: 0.0400
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 0.0632as_fit =
General model:
as_fit(x) = a*x+b
Coefficients (with 95% confidence bounds):
a = 1.299 (1.296, 1.303)
b = 4059 (4033, 4085)
as_gof =
sse: 3.2986e+003
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 18.1621
as_fit2 =
General model:
as_fit2(x) = a*x+b
Coefficients (with 95% confidence bounds):
a = 1.302 (1.297, 1.307)
b = 4895 (4861, 4930)
as_gof2 =
sse: 5.9482e+003
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 24.3889sotr_fit =
General model:
sotr_fit(x) = a*x+b
Coefficients (with 95% confidence bounds):
a = 1.08 (1.08, 1.08)
b = 616.8 (616.1, 617.5)
sotr_gof =
sse: 2.6125
rsquare: 1.0000
dfe: 10
adjrsquare: 1.0000
rmse: 0.5111melee_fit =
General model:
melee_fit(x) = a*x
Coefficients (with 95% confidence bounds):
a = 0.6545 (0.6453, 0.6637)
melee_gof =
sse: 2.5220e+003
rsquare: 0.9991
dfe: 7
adjrsquare: 0.9991
rmse: 18.9814
cs_fit =
General model:
cs_fit(x) = 0.6545*(a*x+b)
Coefficients (with 95% confidence bounds):
a = 1.707 (1.661, 1.753)
b = 1043 (942.4, 1143)
cs_gof =
sse: 9.7370e+003
rsquare: 0.9993
dfe: 6
adjrsquare: 0.9991
rmse: 40.2844
hotr_fit =
General model:
hotr_fit(x) = 0.6545*a*x
Coefficients (with 95% confidence bounds):
a = 0.1992 (0.1989, 0.1995)
hotr_gof =
sse: 0.8558
rsquare: 1.0000
dfe: 7
adjrsquare: 1.0000
rmse: 0.3496