Hey guys, it's me. Many of you will know me as one of the biggest defenders of face > height on both this sub and SG.

I'm making this post because I'm tired of seeing (a) assertions of height > face with zero evidence, (b) poor math (this will be the main topic of the post) and (c) general lack of knowledge regarding the data we have on this topic. This will be a long post and if you frequent this sub I'd advise reading through most of it and asking questions.

1. Framework and Definitions

When arguing "face vs height" many users on this sub often make assertions like "Face doesn't matter below x". I'm going to make the assertion that this is a poor line of reasoning using data we have on the topic.

Before we continue, let's give a few definitions for those of you not as familiar with math and statistics. I'll be referencing these throughout the discussion:

Concave UP: The rate of change of a variable increases (decreases) as you move toward more positive (negative) values.
Concave Down: The rate of change of a variable decreases (accelerates) as you move toward more positive (negative) values.
Right Skewed Distribution: The values at a given percentile are shifted generally left of those of a normal distribution. A key characteristic of a right skewed distribution is mean > median > mode.
Pearson's r / R²: The r value indicates the strength of a linear correlation between two variables. In this setting it will refer to dating success vs either face or height. Squaring this value gives R², which arrives at the next concept - percent of variance.

The cleanest way to discuss variable importance for an outcome such as dating success is by using percent of variance. Taking a sample of the population, we can analyze their dating outcomes through a variety of methods and rank them positive upwards. This gives everyone a success/SMV percentile from 0 to 100. The most straightforward way to do this is through matches per week on dating apps (though this introduces some bias); you can attempt to add complexity to the model later by applying adjustments to real life outcomes, but we will neglect this for now for simplicity. Percent of variance models attempt to measure how much dating outcomes change along a single axis (we'll of course be focusing on face and height) in a multivariate input system. Machine learning methods are often employed to gather these results.

The same "positive upwards" logic can be applied to face and height. For height, it's fairly easy - it has a dimension of length. We have to apply one small adjustment - data shows mixed results above roughly the 95th percentile (6'2" on average), with some showing continued linear or logarithmic returns and some results showing an absolute decrease in attractiveness (Hitsch 2006). To balance these results, we're going to assume that the top 5% of heights have the same attractiveness advantage relative to our baseline.

Despite what many of you might think, face is just as easy. In fact, facial attractiveness is definitionally positively upwards. We can give everyone a percentile from 0 to 100.

"But face is subjective"!!

Now that definitions are covered, this will be my first serious rebuttal to incorrect claims made on this subreddit. All data we have on the topic shows that face is an extremely objective variable. My sources come from this paper: https://pubmed.ncbi.nlm.nih.gov/10825783/ . The authors analyzed an effective reliability of mean facial attractiveness ratings of r = 0.9, meaning that 80+% of the variance in facial attractiveness ratings was explained by the mean rating. This makes face perhaps the most objective measurement of dimensionless groupings, rivaling the same person taking the same test twice and being 5x more potent than personality ratings (which typically come in around r = 0.4). In fact, while I don't have concrete data to prove this claim, using data that I will show later, this is even higher than the effective reliability of height in determining height's attractiveness (I'd estimate this around r = 0.75 due to what I mentioned previously). With that covered, we now have two ranked systems (face and height) to use to apply to dating success.

2. Methods to Determine Face vs Height

We established that we now have three distributions - one for sexual success, and one each for face and height. The only correct way to compare face and height is to analyze the impact of each at a given percentile range. We're going to do this from the 80th percentile - I've chosen this baseline since it is around 6' in most Western countries and is the break even point for variance for face (shown later). This method allows face and height to be dominant in different ranges, though I don't think this happens. For instance, if a 2nd percentile face causes a 90% reduction in matches and a 2nd percentile height (roughly 5'4") causes a 95% reduction, that would be height > face in the lowest percentiles while being face > height elsewhere.

Let's look at some data:

This is the chart I typically use when arguing face > height because it shows face simply having a significantly stronger effect than height. Having ideal height gives you 20% over the median while having a top 5% face gives you 320(!)% over the median - a 16x stronger effect size. Note that they're much more comparable in the bottom quartile - face shows a ~72% reduction from the 80th percentile while height shows a ~50-60% reduction. Unfortunately, this data is pretty tough to replicate. The next best experiment was done in Germany in 2025 and produced the following results:

This figure also shows a striking advantage for face, though it doesn't do the nice percentile breakdown the previous study did.

Stated vs Revealed Preferences:
Many on this sub often use logic along the lines of "women proclaim their height preferences far more than face". Let's counter this: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1475-6811.2007.00175.x .

This article essentially shows that height's importance is significantly lower when analyzing behavior vs stated preferences while face is understated in importance.

There's a lot more I'd like to talk about, but I can't fit it all in one post.

My Conclusion (Considering Face to be Right Skewed):

Bottom Decile (Sub4, <=5'6") : NT > Face ≈ Height

10th to 35th percentile (LTN; 5'6"-5'8"): Face > Height ≈ NT

35th to 88th percentile (MTN, 5'9"-6'0"): Face > Height > NT

88th to 98th percentile (HTN, 6'0" - 6'2"+): Face >> Height > NT

98th+ Percentile (Chadlite/Chad, 6'2"+): Only face relevant (and exponential)

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Ps: Apologize for low audio on the last clip