Statisticsmaxxing Normie to Chad Theory

[cube]

The 20% number is largely used to refer to the number of men who are in the dating pool for the top 80% of women. I'd argue its likely more 90%-10% because of how pareto distributions tend to line up, although that may just be the limit hypergamy is approaching. On first thought top 10% sounds insane, Chad-elite status, but its actually not that rare once you take each factor into consideration. For example say you height fraud to at least 5'10, (shoe insoles), that's 50% or 1/2, then say you mog/gymmax, thats likely ~20% or 1/5 men that are as developed as you physically, then say you skinmaxx and have at least top 33% skin, or ~1/3, then if we assume you have a fucked up face, say you surgerymaxx/facepull/mew/whatever to atleast normie tier 50% 1/2. Totalled up thats

1/2 * 1/5 * 1/3 * 1/2

or top 1.6% of men.

All of this stuff is based on having extremely bad RNG genetics, (except for ethnicity, over for non-whites).

Note: I realize that its statistically inaccurate to just throw in factors without factoring their importance as factors for attraction, but even at worst the range of error is no more than top 10% which is what I hypothesized on.

Also Kind reminder to OHP at least 3x a week (unrelated)

 

[endofcope]

That's not how statistics works buddy. Lets assume all of that but that your hair is worse than 90% of people. Now you have 1.6% * 90% = 1.44%, meaning you're even better despite having bad features. The way you've done it, the more features you take into account the better you are, which we can see if just wrong

 

[cube]

That's not how statistics works buddy. Lets assume all of that but that your hair is worse than 90% of people. Now you have 1.6% * 90% = 1.44%, meaning you're even better despite having bad features. The way you've done it, the more features you take into account the better you are, which we can see if just wrong

See my endnote, I specifically tried to take the features that factor in most to attraction for my example, so their cummulative weight (face, height, skin, body) should probably account for atleast ~90% of what makes someone attractive, although to which extent each is effective is not taken into account. Which I mentioned

 

[endofcope]

See my endnote, I specifically tried to take the features that factor in most to attraction for my example, so their cummulative weight (face, height, skin, body) should probably account for atleast ~90% of what makes someone attractive, although to which extent each is effective is not taken into account. Which I mentioned

My bad, skimmed over that. Think that there's some merit here but i think that weighting each factor is important, as this method is very lenient towards having bad features. For example, to be top 10% of men, you would only need to be top 56% in each category here. Having shit features doesnt drag you down enough in this as it should.
For example if you're perfectly average in everything but one category you are shit, we'd get: .5*.5*.5*.9 = 11.25%
Potential way to counteract this: log(face)/2 * log(height)/2 * log(skin)/2 * log(body)/2

Back to the example from before, we'd use: log(50)/2 * log(50)/2 * log(50)/2 * log(90)/2 = 59.9% (log base 10) which seems a bit more accurate
Under this model, to be top 10% you'd need to be around top 13% in each feature, which might be a bit harsh but could probably be better accounted for with weighting each factor

 

[Jew_Supremacist]

I hate to ruin your cope but let's do the same exact calculation except the guy is dead average in every category you mentioned.
1/2 * 1/2 * 1/2 * 1/2

This means a guy who's exactly 50th percentile in every category would be top 6.25% overall. Think about it logically, if you don't account for how many categories you're multiplying, LET ALONE the weight of the categories, the more categories you add the higher percentile someone gets? So by your model, if a guy was bottom 15% in let's say ten categories, he'd be (0.85)^10 = 0.19687 or the top 20% of men. In fact, you could bullshit your way into thinking blackops2cel is a chad by increasing the number of categories you're judging him by. Even if he's bottom 10% (top 90%) in every category, having 20 categories like shirt, nose, lips, eyes, biceps, and so on would give him a score of (0.9^20) putting him in the top 12% of men.


Let's build a mathematical model that might not be the most accurate but at least makes sense for what you're trying to do. You need to average the traits. So let's say a top 1% height guy gets 99/100 points for the height category, a top 3% face guy gets 97/100 points for that category, and so on. Obviously this isn't fully accurate to irl because certain stuff matters more than others and not everything is a linear distribution, not to mention many factors are correlated but we're ignoring the specific weighting of the various categories for now.
An average guy would have 50 points for each of your four categories.
(50+50+50+50)/4 = 200/4 = 50. So an average guy with 50th percentile features would score 50/100 overall. This makes sense.
A second guy who, in a simplified model like this, is top 10% height, top 20% body, top 10% face, and top 50% skin, would be represented by
(90+80+90+50)/4 = 77.5. So this guy would score 77.5/100, or top 22.5% of men. This also makes sense. Note that if you add a fifth category to rank this guy, like let's say you add grooming as a fifth category, and the guy is top 30% at grooming, it'd be (90+80+90+50+70)/5 = 76. This is similar to the model you'd use to calculate what grade you need on a final.

Sp I covered the main issue with your post, and why it's COMPLETELY wrong, aside from just being inaccurate.
If we want to make this model even more accurate we can attach a weighting to each variable.
[(A_weighting)(A_score) + (B_weighting)(B_score) + (C_weighting)(C_score) + (D_weighting)(D_score) +... ] /
(total amount of categories)
To make it EVEN MORE accurate than that, you would have to address the fact that weighting itself is in some way a function of the scores of the other categories. This involves converting the model into a differential equation, but you'd probably have to do some kind of study to see how women rate guys and their morphs to fit this model, to give you a taste, let's imagine we only take height (A), body (B), and face (C) as variables. Let's pretend face is weighted twice as heavily as either body or height, just as an example.
Eric is top 85%, 40%, and 15% respectively. Eric's scores are then:
A = 10
B = 60
C = 85
Eric is represented by [(1)(10)+(1)(60)+2(85)]/3 = 80, meaning he's top 20% according to this corrected but still rudimentary linear model.
Mike is top 40%, 60%, and 30% respectively. Mike's scores are then:
A = 60
B = 40
C = 70
Mike is represented by [(1)(60)+(1)(40)+2(70)]/3 = 80, meaning he's also top 20%. No matter WHAT weighting you assign to the categories, Eric and Mike should not be this close to each other, let alone the same irl appeal, simply because being bottom 10% height is a death sentence regardless of having a chad face or decent body. The thing is, no matter how you balance the weighting, it'll be hard to account for anyone who is nearing an edge case, such as a gymmaxxed manlet with a Chad face, or a roided out tall guy with a subhuman face, but it doesn't even have to be that extreme to skew the linear model. All this is of course, superfluous and is just trying to make the model more accurate, the real issue with your thinking is you don't account for number of factors at ALL, not even a little bit, your calculations imply blackops2cel could become Chico if we consider more factors about blackops2cel.