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 Post subject: Boss Analysis
PostPosted: Thu Sep 15, 2016 1:22 am 
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Hi Guys,

I've been analysing the Bosses in Deathless mode after I thought that some perks appeared more often than others. I mentioned this on threads here and in epic games forums. I'll be tabulating what perk and what element they have from DM 201 to DM 250. So far as I've thought, Immune to magic appears quite frequently, but not as frequently as I imagined as immune to super was almost just as frequent and I hardly noticed that most of the time, there wasn't even a perk!! There isn't an even distribution of the perks either. SO far from DM 201 to 213, I've tabulated the following:-

Elemental 19/156 = 12.18%
Immune to magic 25/156 = 16.02%
Immune to Super 24/156 = 15.38%
Super Fast Attack 15/156 = 9.62%
Great Parry Only 11/156 = 7.05%
Perfect Block Only 6/156 = 3.85%
Normal Damage Resist = 10/156 = 6.41%
No Perk 46/156 = 29.49%

The link is here is you wish to follow me as I make my way to DM 264.
https://www.icloud.com/numbers/0R2I1pmH ... t_and_Perk

I've also noticed that each boss attack with the same element in a given play through. I hadn't noticed that before. There is a regular cycle to which element they attack with but so far that cycle has only been made up of 5 elements. I'm not sure if playing regular mode has an impact on this cycle as I haven't played regular mode in a long time, but I vaguely remember the other two members of Epic Games Forums who reached DM 185, all did so with a Worker that had Shock as his attack which means that the cycle exists for them and no DM bosses attack with ice, water or bright. How weird!

The reason I chose DM 264 was because there are 8 perks and 8 elements but it seem that only 5 elements are used. You might also notice that I highlighted DM 247 and DM 248. Based on my calculations of Ryth health when I reach that level, I expect the game to end. We will see what happens.

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Aegis Tourney Top 3 : BattleChips | Infinity Blade 3 The Movie : Kingdom Come | Skilled Warrior : Master of the Aegis Form


Last edited by ManOfSteel on Thu Mar 02, 2017 8:39 pm, edited 1 time in total.

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 Post subject: Re: Boss Analysis
PostPosted: Tue Oct 25, 2016 10:17 pm 
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I'm now about to fight Ausar in DM 226 which makes me 39.84375% complete in my analysis. Updated totals are as follows.

Elemental 45/307 = 14.65% ↑ 2.47%
Immune to magic 43/307 = 14.01% ↓ 2.01%
Immune to Super 49/307 = 15.96% ↑ 0.58%
Super Fast Attack 29/307 = 9.45% ↓ 0.17%
Great Parry Only 19/307 = 6.19% ↓ 0.86%
Perfect Block Only 22/307 = 7.17% ↑ 3.32%
Normal Damage Resist = 23/307 = 7.49% ↑ 1.08%
No Perk 77/307 = 25.08% ↓ 4.41%

Ryth still seems to be on track to hit max health between DM 247 and DM 248. Immune Magic seems not to be as popular as I thought. Maybe it seemed that way because I hate it. There is no pattern to the perks neither does there seem to be an even distribution of them.

Stay tuned.

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Aegis Tourney Top 3 : BattleChips | Infinity Blade 3 The Movie : Kingdom Come | Skilled Warrior : Master of the Aegis Form


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 Post subject: Boss Analysis
PostPosted: Wed Dec 07, 2016 6:27 am 
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Here is the old analysis, just for the sake of keeping it :roll:
Hello People,
there was a thread in the old forum about Boss Scaling in DM.
The old raw data can be found in https://docs.google.com/spreadsheets/d/10pr8n5gLdbQoC343ao8rklo0aNgQcculByiKUV2-riA/edit?usp=sharing
which was complete up to DM72.

ManOfSteel/AvalonWizard has shared with me his data up to DM233
(he sent me an Email with the extra points).

Anyway, I think I finally have a functional (albeit a bit convoluted) predictor for a boss level based on the DM level.

Well,
first some statistical analysis.
I always have had the 'perception' that the boss scaling was somewhat a Geometrical/exponential rule, but, to tell the true, my early attempts to 'fit an exponential curve' with the data was always a hit/miss.

This time around, I decided to make some tests first:
In the graph below, it's plotted the Worker level at DM(i) MINUS Worker level at DM(i-1)
Image


In the graph below, i's plotted the Worker level at DM(i) DIVIDED BY Worker level at DM(i-1)
Image


My first surprise was that, the geometric ratio was decreasing and tending to one (which means, it isn't really geometrical after all).
second, at first, from the arithmetic series, one could infer that we have TWO series in it.
In fact, those spikes were extremely periodical: DM5, DM10, DM15 and so on.

based on that, I separated the data set in two series, one with DM5,10,15... and the second with the rest.
My Second surprise: with the remaining points, I observed the same behavior (now, with DM4,9,13 as spikes)...
Same as above, after removing points related to DM5, DM10, DM15 ...
Arithmetic Progression Analysis
Image

Geometric Progression Analysis
Image
As one can see, the 'same behavior'.
So,

I further removed points related to DM4, DM9, DM14, DM19...
Arithmetic Progression Analysis
Image

Geometric Progression Analysis
Image
The pattern repeats.

Just to be sure.
Now removing, further yet, points related to DM3, DM8, DM13, DM18
Arithmetic Progression Analysis
Image

Geometric Progression Analysis
Image


So, that's why, I ended separating the data points in FIVE distinct series:
Series 1: DM1, DM6, DM11, DM16 (1+5n)
Series 2: DM2, DM7, DM12, DM17 (2+5n)
Series 3: DM3, DM8, DM13, DM18 (3+5n)
Series 4: DM4, DM9, DM14, DM19 (4+5n)
Series 5: DM5, DM10, DM15, DM20 (5+5n)

With those series, the Arithmetic/Geometric analysis results in
Arithmetic Progression Analysis
Image
Image
Image
Image
Image

Geometric Progression Analysis
Image
Image
Image
Image
Image


And, as anyone can see, those graphs are, now, a lot smoother :D



The consequence of this is: it is possible to have a good predictor for every Boss, better said 5 predictors, one for each series !!





For the sake of comparison, ignoring that 'find' above, and trying,
anyway, to fit all data with a SINGLE predictor, the best I got for the worker was:

X=(level-96.0521739130435)/83.2434438152045

Worker(X)=
-449104.474808685*(X^20) +1904317.45809381*(X^19) +667770.399230391*(X^18) -12061382.8345623*(X^17) +6250497.83131167*(X^16) +33310660.0131204*(X^15) -26333691.9169831*(X^14) -53329905.607176*(X^13) +46024465.3558975*(X^12) +55411416.3268994*(X^11) -43835024.9078939*(X^10) -38989547.6063569*(X^9) +23471709.8882979*(X^8) +18115674.0353134*(X^7) -6472912.4492147*(X^6) -4960052.47707711*(X^5) +686571.605238863*(X^4) +624234.796754685*(X^3) +1413698.99556902*(X^2) +3213864.82478856*(X^1) +1843018.33644502*(X^0)

Yes, You are reading it right, this is an order 20th polynomial
with the above predictor, I can 'trace' the difference between the real points and the predicted ones:
Image
Please, note the graph scale (10^4), this means the 'predictor' was wrong by as much as 12000+ in some cases !!!


OK, it's also possible to have a 2nd order predictor (albeit "weaker"), like:
X=(level-96.0521739130435)/83.2434438152045
Worker(X)=
+1418482.86230611*(X^2) +3233989.50019826*(X^1) +1842888.7278009*(X^0)

which results in
Image
Note that the 'predictor' was wronger overall.








Now the real ones. I mean, separating the data in 5 series and having a 2nd oreder predictor for each series, the end result is:
Worker
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Worker(X1)=+1472096.3449248*(X1^2) +3315159.89305075*(X1^1) +1866649.33611373*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Worker(X2)=+1498161.66500911*(X2^2) +3420981.34180257*(X2^1) +1953178.40436627*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Worker(X3)=+1507004.43404048*(X3^2) +3344587.96329596*(X3^1) +1856015.32396128*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Worker(X4)=+1532997.73050088*(X4^2) +3159135.15671389*(X4^1) +1627902.15602417*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Worker(X5)=+1348911.5058102*(X5^2) +3183977.98782829*(X5^1) +1878959.80693189*(X5^0)

which results in
Image

which is a far better predictor overall (most of predicted values are less than 100 points off the real values).

Oh, the complete set for all bosses is, therefore
Worker
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Worker(X1)=+1472096.3449248*(X1^2) +3315159.89305075*(X1^1) +1866649.33611373*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Worker(X2)=+1498161.66500911*(X2^2) +3420981.34180257*(X2^1) +1953178.40436627*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Worker(X3)=+1507004.43404048*(X3^2) +3344587.96329596*(X3^1) +1856015.32396128*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Worker(X4)=+1532997.73050088*(X4^2) +3159135.15671389*(X4^1) +1627902.15602417*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Worker(X5)=+1348911.5058102*(X5^2) +3183977.98782829*(X5^1) +1878959.80693189*(X5^0)

Raidriar
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Raidriar(X1)=+736048.1724624*(X1^2) +1657579.94652538*(X1^1) +933274.668056866*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Raidriar(X2)=+749080.832504553*(X2^2) +1710490.67090129*(X2^1) +976539.202183136*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Raidriar(X3)=+753502.21702024*(X3^2) +1672293.98164798*(X3^1) +927957.66198064*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Raidriar(X4)=+766498.865250439*(X4^2) +1579567.57835695*(X4^1) +813901.078012083*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Raidriar(X5)=+674455.7529051*(X5^2) +1591988.99391415*(X5^1) +939429.903465945*(X5^0)

Therin
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Therin(X1)=+736048.1724624*(X1^2) +1657579.94652538*(X1^1) +933299.668056866*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Therin(X2)=+749080.832504554*(X2^2) +1710490.67090129*(X2^1) +976564.202183136*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Therin(X3)=+753502.21702024*(X3^2) +1672293.98164798*(X3^1) +927982.661980639*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Therin(X4)=+766498.865250439*(X4^2) +1579567.57835695*(X4^1) +813926.078012083*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Therin(X5)=+674455.7529051*(X5^2) +1591988.99391415*(X5^1) +939454.903465945*(X5^0)

Oslim
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Oslim(X1)=+736048.1724624*(X1^2) +1657579.94652538*(X1^1) +933349.668056866*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Oslim(X2)=+749080.832504554*(X2^2) +1710490.67090129*(X2^1) +976614.202183136*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Oslim(X3)=+753502.21702024*(X3^2) +1672293.98164798*(X3^1) +928032.66198064*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Oslim(X4)=+766498.865250439*(X4^2) +1579567.57835695*(X4^1) +813976.078012083*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Oslim(X5)=+674455.7529051*(X5^2) +1591988.99391415*(X5^1) +939504.903465945*(X5^0)

Melek
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Melek(X1)=+736048.172462401*(X1^2) +1657579.94652538*(X1^1) +933324.668056866*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Melek(X2)=+749080.832504554*(X2^2) +1710490.67090129*(X2^1) +976589.202183136*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Melek(X3)=+753502.21702024*(X3^2) +1672293.98164798*(X3^1) +928007.66198064*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Melek(X4)=+766498.865250439*(X4^2) +1579567.57835695*(X4^1) +813951.078012083*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Melek(X5)=+674455.752905101*(X5^2) +1591988.99391415*(X5^1) +939479.903465945*(X5^0)

Ausar
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Ausar(X1)=+1472096.3449248*(X1^2) +3315159.89305075*(X1^1) +1866624.33611373*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Ausar(X2)=+1498161.66500911*(X2^2) +3420981.34180257*(X2^1) +1953153.40436627*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Ausar(X3)=+1507004.43404048*(X3^2) +3344587.96329596*(X3^1) +1855990.32396128*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Ausar(X4)=+1532997.73050088*(X4^2) +3159135.15671389*(X4^1) +1627877.15602417*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Ausar(X5)=+1348911.5058102*(X5^2) +3183977.9878283*(X5^1) +1878934.80693189*(X5^0)

Raidriar2
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Raidriar2(X1)=+1472096.3449248*(X1^2) +3315159.89305075*(X1^1) +1866599.33611373*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Raidriar2(X2)=+1498161.66500911*(X2^2) +3420981.34180257*(X2^1) +1953128.40436627*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Raidriar2(X3)=+1507004.43404048*(X3^2) +3344587.96329596*(X3^1) +1855965.32396128*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Raidriar2(X4)=+1532997.73050088*(X4^2) +3159135.15671389*(X4^1) +1627852.15602417*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Raidriar2(X5)=+1348911.5058102*(X5^2) +3183977.9878283*(X5^1) +1878909.80693189*(X5^0)

Ryth
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
Ryth(X1)=+3680240.862312*(X1^2) +8287899.73262688*(X1^1) +4667248.34028433*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
Ryth(X2)=+3745404.16252277*(X2^2) +8552453.35450643*(X2^1) +4883571.01091568*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
Ryth(X3)=+3767511.0851012*(X3^2) +8361469.90823989*(X3^1) +4640663.3099032*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
Ryth(X4)=+3832494.3262522*(X4^2) +7897837.89178473*(X4^1) +4070380.39006041*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
Ryth(X5)=+3372278.7645255*(X5^2) +7959944.96957074*(X5^1) +4698024.51732973*(X5^0)

TheDarkKnight
Series :: level is 1+5*n
X1=(level-96.625)/84.7799261002581
TheDarkKnight(X1)=+368024.0862312*(X1^2) +828789.973262688*(X1^1) +466649.834028433*(X1^0)
Series :: level is 2+5*n
X2=(level-98.875)/85.5266612385208
TheDarkKnight(X2)=+374540.416252277*(X2^2) +855245.335450643*(X2^1) +488282.101091568*(X2^0)
Series :: level is 3+5*n
X3=(level-96.4782608695652)/85.7776563220905
TheDarkKnight(X3)=+376751.10851012*(X3^2) +836146.990823989*(X3^1) +463991.33099032*(X3^0)
Series :: level is 4+5*n
X4=(level-90.5)/86.5128527110887
TheDarkKnight(X4)=+383249.432625219*(X4^2) +789783.789178473*(X4^1) +406963.039006041*(X4^0)
Series :: level is 5+5*n
X5=(level-96.875)/81.1575561056608
TheDarkKnight(X5)=+337227.87645255*(X5^2) +795994.496957074*(X5^1) +469727.451732973*(X5^0)



To round it up

Here you can find an excel file, with the raw data, and the final predictor.
Just type in (in the first table) the desired DM level, and take a look ;)








As a "pièce de résistance", comparing the effect, just for the sake of comparison.
I further tested "which predictor's order" would deliver a best compromise.
I will spare you all from the details (the actual polynomial coefficients),
so below you will find a series of plots, each of them being the resulting "predictors err".
Image
Image
Image
Image
Image
Image
Image
Image
Image
Image
Image
Image
Image
Image
Image
Image
Image


Obviously, the higher the polynomial order, the better the result, but the second order still delivers the better compromise.
More objectively, just summing the errs in each of the above graph (for each predictor), it is easy to compare them
Image
As one can see, a 11th order predictor is 'twice as good' as the second order, but at an incredible cost.






If someone would be interested in, I have used MatLab for the above analysis (this a standard math package for engineering).
Octave should be compatible with it. anyway, the 'source code' is:
Graphs=false;

Worker=1;
Raidriar=2;
Terrovax=2;
Therin=3;
Thane=3;
Oslim=4;
Zuorsara=4;
Minnoch=4;
Melek=5;
Lelindre=5;
Ausar=6;
Raidriar2=7;
Ryth=8;
TheDarkKnight=9;

names={'Worker','Raidriar','Therin','Oslim','Melek','Ausar','Raidriar2','Ryth','TheDarkKnight'};

lvl=transpose([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,85,93,100,103,125,131,136,137,162,185,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233]);
M(:,Worker)=transpose([550,950,1550,2350,3150,4950,6950,9350,12150,16350,19950,23950,28150,32950,39750,45150,51150,57350,63950,73350,80750,88550,96750,105150,117150,126550,136150,146350,156750,171150,182550,194150,206150,218550,235550,248750,262350,276150,290550,310150,325150,340750,356550,372750,394950,411950,429350,447150,465150,489950,508950,528150,547950,567950,595150,616150,637350,658950,680950,710750,733550,756750,780150,804150,836550,861150,886350,911750,937550,972550,999150,1026150,1441950,1722950,2003550,2119150,3144350,3452950,3723950,3775950,5294350,6926750,8178950,8256150,8333550,8411150,8515150,8593750,8672550,8751750,8831350,8937950,9018350,9099150,9180150,9261750,9370950,9453150,9535950,9618950,9702350,9814150,9898350,9982950,10067950,10153150,10267550,10353750,10440150,10527150,10614350,10731150,10819350,10907750,10996550]);
M(:,Raidriar)=transpose([225,425,725,1125,1525,2425,3425,4625,6025,8125,9925,11925,14025,16425,19825,22525,25525,28625,31925,36625,40325,44225,48325,52525,58525,63225,68025,73125,78325,85525,91225,97025,103025,109225,117725,124325,131125,138025,145225,155025,162525,170325,178225,186325,197425,205925,214625,223525,232525,244925,254425,264025,273925,283925,297525,308025,318625,329425,340425,355325,366725,378325,390025,402025,418225,430525,443125,455825,468725,486225,499525,513025,720925,861425,1001725,1059525,1572125,1726425,1861925,1887925,2647125,3463325,4089425,4128025,4166725,4205525,4257525,4296825,4336225,4375825,4415625,4468925,4509125,4549525,4590025,4630825,4685425,4726525,4767925,4809425,4851125,4907025,4949125,4991425,5033925,5076525,5133725,5176825,5220025,5263525,5307125,5365525,5409625,5453825,5498225]);
M(:,Therin)=transpose([250,450,750,1150,1550,2450,3450,4650,6050,8150,9950,11950,14050,16450,19850,22550,25550,28650,31950,36650,40350,44250,48350,52550,58550,63250,68050,73150,78350,85550,91250,97050,103050,109250,117750,124350,131150,138050,145250,155050,162550,170350,178250,186350,197450,205950,214650,223550,232550,244950,254450,264050,273950,283950,297550,308050,318650,329450,340450,355350,366750,378350,390050,402050,418250,430550,443150,455850,468750,486250,499550,513050,720950,861450,1001750,1059550,1572150,1726450,1861950,1887950,2647150,3463350,4089450,4128050,4166750,4205550,4257550,4296850,4336250,4375850,4415650,4468950,4509150,4549550,4590050,4630850,4685450,4726550,4767950,4809450,4851150,4907050,4949150,4991450,5033950,5076550,5133750,5176850,5220050,5263550,5307150,5365550,5409650,5453850,5498250]);
M(:,Oslim)=transpose([300,500,800,1200,1600,2500,3500,4700,6100,8200,10000,12000,14100,16500,19900,22600,25600,28700,32000,36700,40400,44300,48400,52600,58600,63300,68100,73200,78400,85600,91300,97100,103100,109300,117800,124400,131200,138100,145300,155100,162600,170400,178300,186400,197500,206000,214700,223600,232600,245000,254500,264100,274000,284000,297600,308100,318700,329500,340500,355400,366800,378400,390100,402100,418300,430600,443200,455900,468800,486300,499600,513100,721000,861500,1001800,1059600,1572200,1726500,1862000,1888000,2647200,3463400,4089500,4128100,4166800,4205600,4257600,4296900,4336300,4375900,4415700,4469000,4509200,4549600,4590100,4630900,4685500,4726600,4768000,4809500,4851200,4907100,4949200,4991500,5034000,5076600,5133800,5176900,5220100,5263600,5307200,5365600,5409700,5453900,5498300]);
M(:,Melek)=transpose([275,475,775,1175,1575,2475,3475,4675,6075,8175,9975,11975,14075,16475,19875,22575,25575,28675,31975,36675,40375,44275,48375,52575,58575,63275,68075,73175,78375,85575,91275,97075,103075,109275,117775,124375,131175,138075,145275,155075,162575,170375,178275,186375,197475,205975,214675,223575,232575,244975,254475,264075,273975,283975,297575,308075,318675,329475,340475,355375,366775,378375,390075,402075,418275,430575,443175,455875,468775,486275,499575,513075,720975,861475,1001775,1059575,1572175,1726475,1861975,1887975,2647175,3463375,4089475,4128075,4166775,4205575,4257575,4296875,4336275,4375875,4415675,4468975,4509175,4549575,4590075,4630875,4685475,4726575,4767975,4809475,4851175,4907075,4949175,4991475,5033975,5076575,5133775,5176875,5220075,5263575,5307175,5365575,5409675,5453875,5498275]);
M(:,Ausar)=transpose([525,925,1525,2325,3125,4925,6925,9325,12125,16325,19925,23925,28125,32925,39725,45125,51125,57325,63925,73325,80725,88525,96725,105125,117125,126525,136125,146325,156725,171125,182525,194125,206125,218525,235525,248725,262325,276125,290525,310125,325125,340725,356525,372725,394925,411925,429325,447125,465125,489925,508925,528125,547925,567925,595125,616125,637325,658925,680925,710725,733525,756725,780125,804125,836525,861125,886325,911725,937525,972525,999125,1026125,1441925,1722925,2003525,2119125,3144325,3452925,3723925,3775925,5294325,6926725,8178925,8256125,8333525,8411125,8515125,8593725,8672525,8751725,8831325,8937925,9018325,9099125,9180125,9261725,9370925,9453125,9535925,9618925,9702325,9814125,9898325,9982925,10067925,10153125,10267525,10353725,10440125,10527125,10614325,10731125,10819325,10907725,10996525]);
M(:,Raidriar2)=transpose([500,900,1500,2300,3100,4900,6900,9300,12100,16300,19900,23900,28100,32900,39700,45100,51100,57300,63900,73300,80700,88500,96700,105100,117100,126500,136100,146300,156700,171100,182500,194100,206100,218500,235500,248700,262300,276100,290500,310100,325100,340700,356500,372700,394900,411900,429300,447100,465100,489900,508900,528100,547900,567900,595100,616100,637300,658900,680900,710700,733500,756700,780100,804100,836500,861100,886300,911700,937500,972500,999100,1026100,1441900,1722900,2003500,2119100,3144300,3452900,3723900,3775900,5294300,6926700,8178900,8256100,8333500,8411100,8515100,8593700,8672500,8751700,8831300,8937900,9018300,9099100,9180100,9261700,9370900,9453100,9535900,9618900,9702300,9814100,9898300,9982900,10067900,10153100,10267500,10353700,10440100,10527100,10614300,10731100,10819300,10907700,10996500]);
M(:,Ryth)=transpose([2000,3000,4500,6500,8500,13000,18000,24000,31000,41500,50500,60500,71000,83000,100000,113500,128500,144000,160500,184000,202500,222000,242500,263500,293500,317000,341000,366500,392500,428500,457000,486000,516000,547000,589500,622500,656500,691000,727000,776000,813500,852500,892000,932500,988000,1030500,1074000,1118500,1163500,1225500,1273000,1321000,1370500,1420500,1488500,1541000,1594000,1648000,1703000,1777500,1834500,1892500,1951000,2011000,2092000,2153500,2216500,2280000,2344500,2432000,2498500,2566000,3605500,4308000,5009500,5298500,7861500,8633000,9310500,9440500,13236500,17317500,20448000,20641000,20834500,21028500,21288500,21485000,21682000,21880000,22079000,22345500,22546500,22748500,22951000,23155000,23428000,23633500,23840500,24048000,24256500,24536000,24746500,24958000,25170500,25383500,25669500,25885000,26101000,26318500,26536500,26828500,27049000,27270000,27492000]);
M(:,TheDarkKnight)=transpose([125,225,375,575,775,1225,1725,2325,3025,4075,4975,5975,7025,8225,9925,11275,12775,14325,15975,18325,20175,22125,24175,26275,29275,31625,34025,36575,39175,42775,45625,48525,51525,54625,58875,62175,65575,69025,72625,77525,81275,85175,89125,93175,98725,102975,107325,111775,116275,122475,127225,132025,136975,141975,148775,154025,159325,164725,170225,177675,183375,189175,195025,201025,209125,215275,221575,227925,234375,243125,249775,256525,360475,430725,500875,529775,786075,863225,930975,943975,1323575,1731675,2044725,2064025,2083375,2102775,2128775,2148425,2168125,2187925,2207825,2234475,2254575,2274775,2295025,2315425,2342725,2363275,2383975,2404725,2425575,2453525,2474575,2495725,2516975,2538275,2566875,2588425,2610025,2631775,2653575,2682775,2704825,2726925,2749125]);

%%%
%%% normalizing the series
%%%

lvlmean=mean(lvl);
lvlstd=std(lvl);
lvladj=(lvl-lvlmean)/lvlstd;
[N nn]=size(lvl);
Mlog=log(M);


%%
%% Arithmetic/Geometric pre-Analysis
%%
%%

Mpa(1)=1;
Mpg(1)=1;
for i=2:N
Mpa(i)=(M(i,Worker)-M(i-1,Worker))/(lvl(i)-lvl(i-1));
Mpg(i)=exp(log(M(i,Worker)/M(i-1,Worker))/(lvl(i)-lvl(i-1)));
end
if Graphs
figure
plot(lvl,Mpa,lvl,Mpa,'o','MarkerSize',5);
title(sprintf('Worker AP - ( Worker(DM) - Worker(DM-1) ) - All data Points'))
figure
plot(lvl,Mpg,lvl,Mpg,'o','MarkerSize',5);
title('Worker GP - Worker(DM)/Worker(DM-1) - All data Points')
end

file=fopen('COEF','w');

%%
%% Obtaining the coeficients, predictors of 2nd and 20th order.
%%
for z=[2 20]
clear C Clog
for i=1:9
C(i,:)=polyfit(lvladj,(M(:,i)),z);
Cdif(:,i)=(M(:,i)-polyval(C(i,:),lvladj));
% Cz(i,z)=sum(Cdif(:,i));
Clog(i,:)=polyfit(lvladj,Mlog(:,i),z);
Clogdif(:,i)=(M(:,i)-exp(polyval(Clog(i,:),lvladj)));
% Clogz(i,z)=sum(Clogdif(:,i));
end
if Graphs
figure
plot(lvl,Cdif(:,Worker),lvl,Cdif(:,Worker),'o','MarkerSize',5);
title(sprintf('Worker err/diff \\partial(%d) - ( Worker(DM),real - Worker(DM),predictor )',z))
end

%%%
%%% writing the polynomials to a file (single series)
%%%



fprintf(file,'\n\n------------------------------\n\n');
fprintf(file,'\n\n------------------------------\n\n');


fprintf(file,'X=(level-%.15G)/%.15G\n',lvlmean,lvlstd);
for i=1:9
fprintf(file,'\n%s(X)=\n',char(names(i)));
for j=1:z+1
if(C(i,j)<0)
fprintf(file,'%.15G*(X^%d) ',C(i,j),z+1-j);
else
fprintf(file,'+%.15G*(X^%d) ',C(i,j),z+1-j);
end
end
fprintf(file,'\n');
end


fprintf(file,'\n\n------------------------------\n\n');
fprintf(file,'\n\n------------------------------\n\n');

fprintf(file,'X=(level-%.15G)/%.15G\n',lvlmean,lvlstd);
for i=1:9
fprintf(file,'\n%s(X)=\nexp(',char(names(i)));
for j=1:z+1
if(Clog(i,j)<0)
fprintf(file,'%.15G*(X^%d) ',Clog(i,j),z+1-j);
else
fprintf(file,'+%.15G*(X^%d) ',Clog(i,j),z+1-j);
end
end
fprintf(file,')\n');
end



end




%%%
%%%
%%%

%%
%% classifying data points
%% further analysis
%%
idx2=[]; %those 1+5n AND 2+5n
idx3=[]; %those 1+5n AND 2+5n AND 3+5n
idx4=[]; %those 1+5n AND 2+5n AND 3+5n AND 4+5n

for i=1:N
rst=rem(lvl(i)-1,5)+1;

if rst == 1
idx2 = [idx2 i];
idx3 = [idx3 i];
idx4 = [idx4 i];
elseif rst == 2
idx2 = [idx2 i];
idx3 = [idx3 i];
idx4 = [idx4 i];
elseif rst == 3
idx3 = [idx3 i];
idx4 = [idx4 i];
elseif rst == 4
idx4 = [idx4 i];
end;
end
[nn Nx2]=size(idx2);
[nn Nx3]=size(idx3);
[nn Nx4]=size(idx4);

lvlx2=lvl(idx2);
Mx2=M(idx2,:);
lvlx3=lvl(idx3);
Mx3=M(idx3,:);
lvlx4=lvl(idx4);
Mx4=M(idx4,:);


Mpa2(1)=1;
Mpg2(1)=1;
for i=2:Nx2
Mpa2(i)=(Mx2(i,Worker)-Mx2(i-1,Worker))/(lvlx2(i)-lvlx2(i-1));
Mpg2(i)=exp(log(Mx2(i,Worker)/Mx2(i-1,Worker))/(lvlx2(i)-lvlx2(i-1)));
end
if Graphs
figure
plot(lvlx2,Mpa2,lvlx2,Mpa2,'o','MarkerSize',5);
title(sprintf('Worker AP - ( Worker(DM) - Worker(DM-1) ) - Only data Points 1 AND 2 +5n'))
figure
plot(lvlx2,Mpg2,lvlx2,Mpg2,'o','MarkerSize',5);
title('Worker GP - Worker(DM)/Worker(DM-1) - Only data Points 1 AND 2 +5n')
end

Mpa3(1)=1;
Mpg3(1)=1;
for i=2:Nx3
Mpa3(i)=(Mx3(i,Worker)-Mx3(i-1,Worker))/(lvlx3(i)-lvlx3(i-1));
Mpg3(i)=exp(log(Mx3(i,Worker)/Mx3(i-1,Worker))/(lvlx3(i)-lvlx3(i-1)));
end
if Graphs
figure
plot(lvlx3,Mpa3,lvlx3,Mpa3,'o','MarkerSize',5);
title(sprintf('Worker AP - ( Worker(DM) - Worker(DM-1) ) - All data Points except 5 AND 4 +5n'))
figure
plot(lvlx3,Mpg3,lvlx3,Mpg3,'o','MarkerSize',5);
title('Worker GP - Worker(DM)/Worker(DM-1) - All data Points except 5 AND 4+5n')
end

Mpa4(1)=1;
Mpg4(1)=1;
for i=2:Nx4
Mpa4(i)=(Mx4(i,Worker)-Mx4(i-1,Worker))/(lvlx4(i)-lvlx4(i-1));
Mpg4(i)=exp(log(Mx4(i,Worker)/Mx4(i-1,Worker))/(lvlx4(i)-lvlx4(i-1)));
end
if Graphs
figure
plot(lvlx4,Mpa4,lvlx4,Mpa4,'o','MarkerSize',5);
title(sprintf('Worker AP - ( Worker(DM) - Worker(DM-1) ) - All data Points except 5+5n'))
figure
plot(lvlx4,Mpg4,lvlx4,Mpg4,'o','MarkerSize',5);
title('Worker GP - Worker(DM)/Worker(DM-1) - All data Points except 5+5n')
end




%%%
%%% 5 Series
%%%

%%
%% classifying data points
%%

vec=[1 2 3 4 5];
[nn V]=size(vec);
idx=cell(size(vec));
for i=1:N
idx{rem(lvl(i)-1,V)+1}=[idx{rem(lvl(i)-1,V)+1} i];
end
for i=1:V
[nn Nx(i)]=size(idx{i});
end

lvlx=cell(size(vec));
for i=1:V
lvlx{i}=lvl(idx{i});
end
Mx=cell(size(vec));
for i=1:V
Mx{i}=M(idx{i},:);
end


%%%
%%% normalizing each series
%%%

lvlxadj=cell(size(vec));
Mxlog=cell(size(vec));
for i=1:V
lvlxmean(i)=mean(lvlx{i});
lvlxstd(i)=std(lvlx{i});

lvlxadj{i}=(lvlx{i}-lvlxmean(i))/lvlxstd(i);
Mxlog{i}=log(Mx{i});
end


%%
%% Arithmetic/Geometric pre-Analysis
%%
%%

Mxpa=cell(size(vec));
Mxpg=cell(size(vec));
for i=1:V
Mxpa{i}(1)=1;
Mxpg{i}(1)=1;
for j=2:Nx(i)
Mxpa{i}(j)=(Mx{i}(j,Worker)-Mx{i}(j-1,Worker))/(lvlx{i}(j)-lvlx{i}(j-1));
Mxpg{i}(j)=exp(log(Mx{i}(j,Worker)/Mx{i}(j-1,Worker))/(lvlx{i}(j)-lvlx{i}(j-1)));
end
if Graphs
figure
plot(lvlx{i},Mxpa{i},lvlx{i},Mxpa{i},'o','MarkerSize',5);
title(sprintf('Worker AP - ( Worker(DM) - Worker(DM-5) )/5 - series:%d+5*n',i))
figure
plot(lvlx{i},Mxpg{i},lvlx{i},Mxpg{i},'o','MarkerSize',5);
title(sprintf('Worker GP - ( Worker(DM)/Worker(DM-5) )^{1/5} - series:%d+5*n',i))
end
end


Cx=cell(size(vec));
Cxlog=cell(size(vec));
Cxdif=cell(size(vec));
Cxlogdif=cell(size(vec));
% This is to generate from 2 up to 18
% Zord=[2:18];

% this is the one that matters.
Zord=[2];

for z=Zord
clear Cx Cxdif Cxlog Cxlogdif CCxdif
for j=1:V
for i=1:9
Cx{j}(i,:)=polyfit(lvlxadj{j},Mx{j}(:,i),z);
Cxdif{j}(:,i)=(Mx{j}(:,i)-polyval(Cx{j}(i,:),lvlxadj{j}));
Cxlog{j}(i,:)=polyfit(lvlxadj{j},Mxlog{j}(:,i),z);
Cxlogdif{j}(:,i)=(Mx{j}(:,i)-exp(polyval(Cxlog{j}(i,:),lvlxadj{j})));
CCxdif(idx{j},i)=Cxdif{j}(:,i);
end
SumDiff(z,:)=sum(abs(Cxdif{j}));
end
if Graphs
figure
plot(lvl,CCxdif(:,Worker),lvl,CCxdif(:,Worker),'o','MarkerSize',5);
title(sprintf('Worker err/diff 5 series of \\partial(%d) - ( Worker(DM),real - Worker(DM),predictor )',z))
end
end
if Graphs
figure
plot(Zord,SumDiff(Zord,Worker),Zord,SumDiff(Zord,Worker),'o','MarkerSize',5)
title(sprintf('Worker sum of abs(err/diff), 5 series, as a function of predictor`s polynomial order'))
end

%%%
%%% writing the polynomials to a file (5 series)
%%%


fprintf(file,'\n\n------------------------------\n\n');
fprintf(file,'\n\n------------------------------\n\n');
for i=1:9
fprintf(file,'\n%s\n',char(names(i)));
for j=1:V
fprintf(file,'\tSeries :: level is %d+5*n\n',j);
fprintf(file,'\t\tX%d=(level-%.15G)/%.15G\n',j,lvlxmean(j),lvlxstd(j));
fprintf(file,'\t\t%s(X%d)=',char(names(i)),j);
for k=1:Xord+1
if(Cx{j}(i,k)<0)
fprintf(file,'%.15G*(X%d^%d) ',Cx{j}(i,k),j,Xord+1-k);
else
fprintf(file,'+%.15G*(X%d^%d) ',Cx{j}(i,k),j,Xord+1-k);
end
end
fprintf(file,'\n');
end
end


%%%
%%% writing the polynomials for a .csv file (5 series)
%%%



fprintf(file,'\n\n------------------------------\n\n');
fprintf(file,'\n\n------------------------------\n\n');

fprintf(file,',mean,std\n');
for j=1:V
fprintf(file,'Series %d , %.15G , %.15G\n',j,lvlxmean(j),lvlxstd(j));
end

fprintf('\n');

for i=1:9
fprintf(file,'\n%s\n',char(names(i)));
for j=1:V
fprintf(file,'Series %d , ',j);
for k=1:Xord+1
fprintf(file,'%.15G , ',Cx{j}(i,k));
end
fprintf(file,'\n');
end
fprintf(file,'DM :: \n');
fprintf(file,'Series = \n');
fprintf(file,'Level =\n');
fprintf(file,'\n');
end



fclose(file);










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Not downloaded yet

Attachment:
File comment: Worker AP Analysis,
All Points, except series (4 AND 5) + 5n

Worker AP analysis All points except 5 AND 4 +5n.png [11.33 KiB]
Not downloaded yet

Attachment:
File comment: Worker GP Analysis,
All Points, except series (4 AND 5) + 5n

Worker GP analysis All points except 5 AND 4 +5n.png [9.15 KiB]
Not downloaded yet

Attachment:
File comment: Worker AP Analysis,
All Points, except series (3 AND 4 AND 5) + 5n
that is only series (1 AND 2) + 5n

Worker AP analysis ONLY points 1 AND 2 +5n.png [10.17 KiB]
Not downloaded yet

Attachment:
File comment: Worker GP Analysis,
All Points, except series (3 AND 4 AND 5) + 5n
that is only series (1 AND 2) + 5n

Worker GP analysis ONLY points 1 AND 2 +5n.png [8.53 KiB]
Not downloaded yet

Attachment:
File comment: Worker AP Analysis,
Series 1+5n

Worker AP analysis series 1+5n.png [8.61 KiB]
Not downloaded yet

Attachment:
File comment: Worker AP Analysis,
Series 2+5n

Worker AP analysis series 2+5n.png [8.8 KiB]
Not downloaded yet

Attachment:
File comment: Worker AP Analysis,
Series 3+5n

Worker AP analysis series 3+5n.png [8.61 KiB]
Not downloaded yet

Attachment:
File comment: Worker AP Analysis,
Series 4+5n

Worker AP analysis series 4+5n.png [8.31 KiB]
Not downloaded yet

Attachment:
File comment: Worker AP Analysis,
Series 5+5n

Worker AP analysis series 5+5n.png [8.83 KiB]
Not downloaded yet

Attachment:
File comment: Worker GP Analysis,
Series 1+5n

Worker GP analysis series 1+5n.png [7.82 KiB]
Not downloaded yet

Attachment:
File comment: Worker GP Analysis,
Series 2+5n

Worker GP analysis series 2+5n.png [8.3 KiB]
Not downloaded yet

Attachment:
File comment: Worker GP Analysis,
Series 3+5n

Worker GP analysis series 3+5n.png [8.36 KiB]
Not downloaded yet

Attachment:
File comment: Worker GP Analysis,
Series 4+5n

Worker GP analysis series 4+5n.png [8.09 KiB]
Not downloaded yet

Attachment:
File comment: Worker GP Analysis,
Series 5+5n

Worker GP analysis series 5+5n.png [8.16 KiB]
Not downloaded yet

Attachment:
File comment: Worker Diff/Err
All Points, single series, Predictor 20th order

Worker Diff, ALL points, predictor of 20th order.png
Worker Diff, ALL points, predictor of 20th order.png [ 13.29 KiB | Viewed 1804 times ]

Attachment:
File comment: Worker Diff/Err
All Points, single series, Predictor 2nd order

Worker Diff, ALL points, predictor of 2nd order.png
Worker Diff, ALL points, predictor of 2nd order.png [ 13.28 KiB | Viewed 1804 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 2nd order

Worker Diff, 5 series, predictors  of 2nd order.png
Worker Diff, 5 series, predictors of 2nd order.png [ 12.99 KiB | Viewed 1804 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 2nd order

Worker Diff, 5 series, predictors  of 2nd order.png
Worker Diff, 5 series, predictors of 2nd order.png [ 12.99 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 3rd order

Worker Diff, 5 series, predictors  of 3rd order.png
Worker Diff, 5 series, predictors of 3rd order.png [ 13.48 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 4th order

Worker Diff, 5 series, predictors  of 4th order.png
Worker Diff, 5 series, predictors of 4th order.png [ 13.45 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 5th order

Worker Diff, 5 series, predictors  of 5th order.png
Worker Diff, 5 series, predictors of 5th order.png [ 13.84 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 6th order

Worker Diff, 5 series, predictors  of 6th order.png
Worker Diff, 5 series, predictors of 6th order.png [ 14.67 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 7th order

Worker Diff, 5 series, predictors  of 7th order.png
Worker Diff, 5 series, predictors of 7th order.png [ 15.01 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 8th order

Worker Diff, 5 series, predictors  of 8th order.png
Worker Diff, 5 series, predictors of 8th order.png [ 14.97 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 9th order

Worker Diff, 5 series, predictors  of 9th order.png
Worker Diff, 5 series, predictors of 9th order.png [ 14.91 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 10th order

Worker Diff, 5 series, predictors  of 10th order.png
Worker Diff, 5 series, predictors of 10th order.png [ 14.12 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 11th order

Worker Diff, 5 series, predictors  of 11th order.png
Worker Diff, 5 series, predictors of 11th order.png [ 14.72 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 12th order

Worker Diff, 5 series, predictors  of 12th order.png
Worker Diff, 5 series, predictors of 12th order.png [ 15.35 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 13th order

Worker Diff, 5 series, predictors  of 13th order.png
Worker Diff, 5 series, predictors of 13th order.png [ 15.03 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 14th order

Worker Diff, 5 series, predictors  of 14th order.png
Worker Diff, 5 series, predictors of 14th order.png [ 14.56 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 15th order

Worker Diff, 5 series, predictors  of 15th order.png
Worker Diff, 5 series, predictors of 15th order.png [ 13.81 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 16th order

Worker Diff, 5 series, predictors  of 16th order.png
Worker Diff, 5 series, predictors of 16th order.png [ 14.53 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 17th order

Worker Diff, 5 series, predictors  of 17th order.png
Worker Diff, 5 series, predictors of 17th order.png [ 13.43 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker Diff/Err
5 Series, Predictor 18th order

Worker Diff, 5 series, predictors  of 18th order.png
Worker Diff, 5 series, predictors of 18th order.png [ 12.34 KiB | Viewed 1779 times ]

Attachment:
File comment: Worker's total Diff/Err as a function of predictor's order
Worker Diff, 5 series, err sum, as a function of predictor's order.png [8.69 KiB]
Not downloaded yet









Final Edit (I think), after some further analysis, I have also a predictor for the Worker's level in Normal Game Mode (no DM)

Here the raw data, starting with play-trough 1
Level-Worker's Level-Diff. to previous one-auxiliary
115000
23502001
35502002
47502003
59502004
611502000
715504001
819504002
923504003
1027504004
1131504000
1237506001
1343506002
1449506003
1555506004
1661506000
1769508001
1877508002
1985508003
2093508004
21101508000
221115010001
231215010002
241315010003
251415010004
261515010000
271635012001
281755012002
291875012003
301995012004
312115012000
322255014001
332395014002
342535014003
352675014004
362815014000
372975016001
383135016002
393295016003
403455016004
413615016000
423795018001
433975018002
444155018003
454335018004
464515018000
474715020001
484915020002
495115020003
505315020004
515515020000
525735022001
535955022002
546175022003
556395022004
566615022000
576855024001
587095024002
597335024003
607575024004
617815024000
628075026001
638335026002
648595026003
658855026004
669115026000
679395028001
689675028002
699955028003
7010235028004
7110515028000
7210815030001
7311115030002
7411415030003
7511715030004
7612015030000
7712335032001
7812655032002
7912975032003
8013295032004
8113615032000
8213955034001
8314295034002
8414635034003
8514975034004
8615315034000
8715675036001
8816035036002
8916395036003
9016755036004
9117115036000
9217495038001
9317875038002
9418255038003
9518635038004
9619015038000
9719415040001
9819815040002
9920215040003
10020615040004
10121015040000
10221435042001
10321855042002
10422275042003
10522695042004
10623115042000
10723555044001
10823995044002
10924435044003
11024875044004
11125315044000
11225775046001
11326235046002
11426695046003
11527155046004
11627615046000
11728095048001
11828575048002
11929055048003
12029535048004
12130015048000
12230515050001
12331015050002
12431515050003
12532015050004
12632515050000
12733035052001
12833555052002
12934075052003
13034595052004
13135115052000
13235655054001
13336195054002
13436735054003
13537275054004
13637815054000
13738375056001
13838935056002
13939495056003
14040055056004
14140615056000
14241195058001
14341775058002
14442355058003
14542935058004
14643515058000
14744115060001
14844715060002
14945315060003
15045915060004
15146515060000
15247135062001
15347755062002
15448375062003
15548995062004
15649615062000
15750255064001
15850895064002
15951535064003
16052175064004
16152815064000
16253475066001
16354135066002
16454795066003
16555455066004
16656115066000
16756795068001
16857475068002
16958155068003
17058835068004
17159515068000
17260215070001
17360915070002
17461615070003
17562315070004
17663015070000
17763735072001
17864455072002
17965175072003
18065895072004
18166615072000
18267355074001
18368095074002
18468835074003
18569575074004
18670315074000
18771075076001
18871835076002
18972595076003
19073355076004
19174115076000
19274895078001
19375675078002
19476455078003
19577235078004
19678015078000
19778815080001
19879615080002
19980415080003
20081215080004
20182015080000
20282835082001
20383655082002
20484475082003
20585295082004
20686115082000
20786955084001
20887795084002
20988635084003
21089475084004
21190315084000
21291175086001
21392035086002
21492895086003
21593755086004
21694615086000
21795495088001
21896375088002
21997255088003
22098135088004
22199015088000
22299915090001
223100815090002
224101715090003
225102615090004
226103515090000
227104435092001
228105355092002
229106275092003
230107195092004
231108115092000
232109055094001
233109995094002
234110935094003
235111875094004
236112815094000
237113775096001
238114735096002
239115695096003
240116655096004
241117615096000
242118595098001
243119575098002
244120555098003
245121535098004
246122515098000
2471235150100001
2481245150100002
2491255150100003
2501265150100004
2511275150100000


Based on those data points, it's possible both to predict the Worker's level based on the Play Through, and the Play Through based on Worker's level:
Worker's Level = 129599/6480 * Play-Through^2 + 6184/103*Play-Through + 3553/24
Play-Through = ( - 6184/103 + sqrt((6184/103)^2 - 4 * 129599/6480 * (3553/24 - Worker's)))/(2*129599/6480)


The excel sheet above contains, also, this one.

_________________
"freedom is cheap! . . . Now, Luxury . . . It's hard to come by. . ."
Aegis Collector20: God KingThe CompendiumNCF104 : Crusher2
ISA 824/2427DM:25 NG:196SIRIS 827/2428


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 Post subject: Boss Analysis
PostPosted: Wed Dec 07, 2016 6:27 am 
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Joined: Tue Aug 23, 2016 11:27 am
Posts: 1108
Location: Hideout when not hunting
Hello people,
well after quite a long time, and lots of back and forth, now I can say "how the worker's level progress", with certainty, both in Normal Game Mode (NG) and/or Deathless Mode (DM).

NG
The first worker is at level 150, thereafter, the next FIVE workers are the previous one plus 200.
So,
NG 1 : 150
NG 2 : 350
NG 3 : 550
NG 4 : 750
NG 5 : 950
the next FIVE ones the diff. will be 400 (1350, 1750, 2150, 2550, 2950), the next FIVE ones the diff. will be 600, then 800, then 1000, and so on.
That way it is possible to know the worker's level for a given play through, and at which play through a player is based on the worker's level.

DM
Thanks to AvalonWizard/ManOfSteel, we now know that the worker's level at DM and NG are 'the same' !!!
Better said, the worker at DM 1 is equal the worker at NG 3, the worker at DM 2 is equal the worker at NG 5...
Well, the progress, though, is a bit different, in the following way: for the first 4 workers (after DM 1) the next worker is the 'NG equivalent' plus 2 levels (step of 2):
DM 1 : NG 3
DM 2 : NG 5
DM 3 : NG 7
DM 4 : NG 9
DM 5 : NG 11
NOW, thereafter, the 'step pattern' is the following: 3 3 3 3 4, repeat
DM 6 : NG 14
DM 7 : NG 17
DM 8 : NG 20
DM 9 : NG 23
DM 10 : NG 27
---
DM 11 : NG 30
DM 12 : NG 33
DM 13 : NG 36
DM 14 : NG 39
DM 15 : NG 43
---
so, it's possible to know exactly the level of any boss at any DM, and vice-versa, given a worker's level, is possible to know at which DM the player's is.

DMNGWorker'sStepSequenceNGWorker'sDiff.Sequence
13550115000
259502-23502001
3715502-35502002
4923502-47502003
51131502-59502004
614495031611502000
717695032715504001
820935033819504002
9231215034923504003
102716350401027504004
113019950311131504000
123323950321237506001
133628150331343506002
143932950341449506003
154339750401555506004
164645150311661506000
174951150321769508001
185257350331877508002
195563950341985508003
205973350402093508004
2162807503121101508000
22658855032221115010001
23689675033231215010002
247110515034241315010003
257511715040251415010004
267812655031261515010000
278113615032271635012001
288414635033281755012002
298715675034291875012003
309117115040301995012004
319418255031312115012000
329719415032322255014001
3310020615033332395014002
3410321855034342535014003
3510723555040352675014004
3611024875031362815014000
3711326235032372975016001
3811627615033383135016002
3911929055034393295016003
4012331015040403455016004
4112632515031413615016000
4212934075032423795018001
4313235655033433975018002
4413537275034444155018003
4513939495040454335018004
4614241195031464515018000
4714542935032474715020001
4814844715033484915020002
4915146515034495115020003
5015548995040505315020004
5115850895031515515020000
5216152815032525735022001
5316454795033535955022002
5416756795034546175022003
5517159515040556395022004
5617461615031566615022000
5717763735032576855024001
5818065895033587095024002
5918368095034597335024003
6018771075040607575024004
6119073355031617815024000
6219375675032628075026001
6319678015033638335026002
6419980415034648595026003
6520383655040658855026004
6620686115031669115026000
6720988635032679395028001
6821291175033689675028002
6921593755034699955028003
70219972550407010235028004
71222999150317110515028000
722251026150327210815030001
732281053550337311115030002
742311081150347411415030003
752351118750407511715030004
762381147350317612015030000
772411176150327712335032001
782441205550337812655032002
792471235150347912975032003
802511275150408013295032004
812541305750318113615032000
822571336550328213955034001
832601367750338314295034002
842631399350348414635034003
852671441950408514975034004
862701474350318615315034000
872731507150328715675036001
882761540150338816035036002
892791573750348916395036003
902831618950409016755036004
912861653150319117115036000
922891687950329217495038001
932921722950339317875038002
942951758350349418255038003
952991806150409518635038004
963021842350319619015038000
973051878950329719415040001
983081915950339819815040002
993111953150349920215040003
10031520035504010020615040004
10131820417503110121015040000
10232120801503210221435042001
10332421191503310321855042002
10432721583503410422275042003
10533122111504010522695042004
10633422513503110623115042000
10733722917503210723555044001
10834023325503310823995044002
10934323737503410924435044003
11034724291504011024875044004
11135024711503111125315044000
11235325135503211225775046001
11335625561503311326235046002
11435925993503411426695046003
11536326573504011527155046004
11636627011503111627615046000
11736927455503211728095048001
11837227901503311828575048002
11937528351503411929055048003
12037928957504012029535048004
12138229415503112130015048000
12238529877503212230515050001
12338830343503312331015050002
12439130811503412431515050003
12539531443504012532015050004
12639831921503112632515050000
12740132401503212733035052001
12840432887503312833555052002
12940733375503412934075052003
13041134031504013034595052004
13141434529503113135115052000
13241735029503213235655054001
13342035533503313336195054002
13442336041503413436735054003
13542736723504013537275054004
13643037239503113637815054000
13743337759503213738375056001
13843638281503313838935056002
13943938809503413939495056003
14044339517504014040055056004
14144640051503114140615056000
14244940591503214241195058001
14345241133503314341775058002
14445541679503414442355058003
14545942413504014542935058004
14646242967503114643515058000
14746543525503214744115060001
14846844087503314844715060002
14947144651503414945315060003
15047545411504015045915060004
15147845985503115146515060000
15248146561503215247135062001
15348447143503315347755062002
15448747727503415448375062003
15549148511504015548995062004
15649449105503115649615062000
15749749701503215750255064001
15850050301503315850895064002
15950350905503415951535064003
16050751715504016052175064004
16151052327503116152815064000
16251352943503216253475066001
16351653561503316354135066002
16451954185503416454795066003
16552355021504016555455066004
16652655651503116656115066000
16752956287503216756795068001
16853256925503316857475068002
16953557567503416958155068003
17053958429504017058835068004
17154259079503117159515068000
17254559733503217260215070001
17354860391503317360915070002
17455161051503417461615070003
17555561939504017562315070004
17655862609503117663015070000
17756163281503217763735072001
17856463959503317864455072002
17956764639503417965175072003
18057165551504018065895072004
18157466241503118166615072000
18257766933503218267355074001
18358067629503318368095074002
18458368329503418468835074003
18558769267504018569575074004
18659069975503118670315074000
18759370687503218771075076001
18859671401503318871835076002
18959972121503418972595076003
19060373085504019073355076004
19160673811503119174115076000
19260974543503219274895078001
19361275277503319375675078002
19461576015503419476455078003
19561977005504019577235078004
19662277751503119678015078000
19762578501503219778815080001
19862879255503319879615080002
19963180011503419980415080003
20063581027504020081215080004
20163881793503120182015080000
20264182561503220282835082001
20364483335503320383655082002
20464784111503420484475082003
20565185151504020585295082004
20665485937503120686115082000
20765786725503220786955084001
20866087517503320887795084002
20966388313503420988635084003
21066789379504021089475084004
21167090183503121190315084000
21267390991503221291175086001
21367691801503321392035086002
21467992617503421492895086003
21568393709504021593755086004
21668694531503121694615086000
21768995359503221795495088001
21869296189503321896375088002
21969597023503421997255088003
22069998141504022098135088004
22170298983503122199015088000
22270599829503222299915090001
2237081006795033223100815090002
2247111015315034224101715090003
2257151026755040225102615090004
2267181035375031226103515090000
2277211044015032227104435092001
2287241052715033228105355092002
2297271061435034229106275092003
2307311073115040230107195092004
2317341081935031231108115092000
2327371090775032232109055094001
2337401099655033233109995094002
2347431108575034234110935094003
2357471120515040235111875094004
2367501129515031236112815094000
2377531138555032237113775096001
2387561147615033238114735096002
2397591156735034239115695096003
2407631168935040240116655096004
2417661178115031241117615096000
2427691187355032242118595098001
2437721196615033243119575098002
2447751205915034244120555098003
2457791218375040245121535098004
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150145150150600000



Other Bosses
well, given the worker's level is always possible to know the other ones:
Raidriar 1:Worker/2-50
Terrovax:Worker/2-50
Therin:Worker/2-25
Thane:Worker/2-25
Oslim:Worker/2+25
Zuorsara:Worker/2+25
Minnoch:Worker/2+25
Melek:Worker/2
Lelindre:Worker/2
Ausar:Worker-25
Raidriar 2:Worker-50
Worker:Worker
Ryth:Worker*2.5 +625
Dark Knight:Worker/4 + 12.5




I made an excel spreadsheet with those results (if you download it, you can edit it, and use it to obtain the worker's level, etc...)
https://drive.google.com/open?id=0BxK0sc4X31i_eGNMT2EyNUZfSWs

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 Post subject: Re: Boss Analysis
PostPosted: Wed Dec 07, 2016 6:53 am 
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Hmmmm. Interesting. This looks awesome. I'll devour this information in a minute. Thanks for the analysis THX!!!

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 Post subject: Re: Boss Analysis
PostPosted: Wed Dec 07, 2016 11:26 am 
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ManOfSteel wrote:
Hmmmm. Interesting. This looks awesome. I'll devour this information in a minute. Thanks for the analysis THX!!!


Thanks,

By the way, take notice that those last three graphs are "diff/err ones"
In each and everyone of them, what is plotted is the difference between the original/real value and the value given by the predictor.

Notice that those "single series predictor" the error was diverging,
While with the 5 series approach, the error is bound to less than 100 (in general)

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 Post subject: Re: Boss Analysis
PostPosted: Wed Dec 07, 2016 10:38 pm 
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THX1138 wrote:
ManOfSteel wrote:
Hmmmm. Interesting. This looks awesome. I'll devour this information in a minute. Thanks for the analysis THX!!!


Thanks,

By the way, take notice that those last three graphs are "diff/err ones"
In each and everyone of them, what is plotted is the difference between the original/real value and the value given by the predictor.

Notice that those "single series predictor" the error was diverging,
While with the 5 series approach, the error is bound to less than 100 (in general)


I wonder if ChAIR actually has a formula or we are telling them stuff about their game that they don't even know. LOL. Maybe the game has an AI engine that is evolving on its own.

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 Post subject: Re: Boss Analysis
PostPosted: Thu Dec 08, 2016 12:37 am 
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ManOfSteel wrote:
THX1138 wrote:
ManOfSteel wrote:
Hmmmm. Interesting. This looks awesome. I'll devour this information in a minute. Thanks for the analysis THX!!!


Thanks,

By the way, take notice that those last three graphs are "diff/err ones"
In each and everyone of them, what is plotted is the difference between the original/real value and the value given by the predictor.

Notice that those "single series predictor" the error was diverging,
While with the 5 series approach, the error is bound to less than 100 (in general)


I wonder if cHair actually has a formula or we are telling them stuff about their game that they don't even know. LOL. Maybe the game has an AI engine that is evolving on its own.


They certainly know it better,
although, in the past, many have tried to 'get it straight from cHair', to no avail.


I'm going out to lunch now, but I should update the post above with a bit better, and more, graphs ;)
when I return
8-)

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 Post subject: Re: Boss Analysis
PostPosted: Thu Dec 08, 2016 2:06 am 
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Graphics update done !
the next thing: I will see if I can come up with an excel table just to compute any boss level using the final expressions above.

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 Post subject: Re: Boss Analysis
PostPosted: Thu Dec 08, 2016 3:31 am 
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DONE !


Here you can find an excel file, with the raw data, and the final predictor.
Just type in (in the first table) the desired DM level, and take a look ;)

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