The Dark Truth Behind Lichess Studies (You are in the Matrix)

And ye shall know the truth and the truth shall make you free. - John 8:32.

Hell is Truth Seen Too Late. - Thomas Hobbes.

You take the blue pill, the story ends, you wake up in your bed and believe whatever you want to believe. You take the red pill, you stay in Wonderland, and I show you how deep the rabbit hole goes. - Morpheus.

Lichess Studies were introduced in 2016. They allow people to share information by annotating chess moves. You can see the most 'hot' studies on the study page.

https://lichess.org/study

The current hot studies.

But how does a study get hot? Clearly likes are a factor, but you'll notice that it isn't ordered according to likes exactly. The most hot studies have a lot of likes but they aren't ranked in descending order of likes. The reason is that time also plays a role in determining the ranking of studies on the hot list.

A newer study with 20 likes could be ranked higher than an older study with 500 likes.

But what is the formula that controls this?

How to find the formula?

Now the only place it could be is on the Lichess Github Page. Lichess is open source and its code is publicly available. There must be code which implements a formula for assigning a ranking value based on the likes and the time created of a particular study. Then the hot list would place studies in order of descending ranking value.

The Code

Here is the code which carries out the formula:

This code implements the formula below:

The Secret Formula

Rank Value = Time Created + Likes Converted to Hours

More Detailed Formula is Rank Value = Time Created + 24 X (5 X ln(likes) +1)

24 X (5 X ln(likes) +1) is a simple formula, you can solve it with a calculator. This formula converts the Likes to a time format (hours). So that the format is the same as the Time Created variable.

This means that a study gets an initial ranking value (based on time elapsed since Jan 1st 1970, this means that newer studies have a higher initial ranking value as more time has elapsed). This is done to ensure that new studies can make it on to the hot list, otherwise old hot studies would stay there forever.

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The likes are put into a logarithmic function. This means that early likes increase the ranking value much more than later likes. This is done to ensure that rising studies can get to the top of the hot list quicker, and also to ensure that popular hot studies cannot stay at the top forever.

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ln is the natural log with base e. e=2.718...

Here's an example: 2^3 = 2 X 2 X 2 = 8. That's easy. But can we reverse it? log2(8) = A number that tells how many times we can multiply 2 on itself to make 8 = 3.

So ln tells us how many times we can multiply e (2.718...) by itself to make the amount of likes.

A logarithm is the reverse of a exponent.

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A exponential graph means that the curve is first low but then skyrockets.

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A logarithmic graph skyrockets first and then flattens out (to infinity actually).

Early likes are worth much more than later likes. Each like is worth less than the previous like. The function is 24 X (5 X ln(likes) +1).

Study ranking values can be viewed as a 'pot' (time elapsed since Jan 1st 1970) which the likes can grow from (like a plant). A taller pot allows a smaller plant to stand taller. A newer study can have a greater rank value with less likes. This is because it gets a larger initial ranking value (more time has elapsed since Jan 1st 1970).

The Code Explained

Let's unwrap this:

opaque type Rank = Instant

Instant refers to time elapsed since January 1st 1970. This is called Unix Time. It is a standardized way for websites to timestamp things and calculate elapsed time. Websites use this common reference as it allows timestamps to be transmitted across websites in a common language.

Because it would be chaos if each website had their own individual way of measuring time, because then it would need to converted into another format when transmitting info to another website.

object Rank extends OpaqueInstant[Rank]:
def compute(likes: Likes, createdAt: Instant) =
Rank(createdAt.plusHours(likesToHours(likes)))

Calculates Rank Value by adding Likes Converted to Hours to Time Created.

private def likesToHours(likes: Likes): Int =

Function which coverts the Likes to Hours (we need a common format). So that the Likes can be added to Time Created.

if likes < 1 then 0

ln(0) is an error (the natural log of 0 is not mathematically possible) so we deal with this by simply saying that if there is 0 likes then the added hours are 0. We deal with this first before the main formula which has an natural log function.

else (5 * math.log(likes) + 1).toInt.min(likes) * 24

24 X (5 X ln(likes) +1). This is the Likes to Hours conversion function.

Gaming The System

You are in the Matrix.

Study ranking values are manipulated by 'those who know'. They message people to like their studies as soon as possible to game the formula. Some of these people are names that you may have seen before. They may play amongst you, seen but at the same time unseen. They may be friends, training partners or coaches.

Common practice is to 'clone' a copy of the study first, because new studies get that boost as they have a larger time value (time elapsed since Jan 1st 1970 as mentioned earlier). The best time to get likes is when the study is first released. Otherwise the new likes won't compensate for the time since the study was released and then the study will fall behind.

Also the best time to break through to the top is when there are a bunch of 1 week old studies still hanging around, this is because there is a bigger advantage for new studies as their initial ranking value will be higher due to more time having elapsed relative to the old studies.

Some Lichess Teams also promote studies to their members through team messages. This allows hundreds/thousand of people from that team to be messaged with a study which allows the study creator to gain much more likes.

Some team leaders benefitted from this as they were paid by a chess company to advertise their site on their studies. And they would get more cash for more likes.

Now you know how deep the rabbit hole goes.

Hypocrisy is the audacity to preach integrity from a den of corruption. - Wes Fesler.

Beyond a doubt, truth bears the same relation to falsehood as light to darkness. - Leonardo da Vinci.

I know you're out there. I can feel you now. I know that you're afraid. You're afraid of us. You're afraid of change. I don't know the future. I didn't come here to tell you how this is going to end. I came here to tell you how it's going to begin. I'm going to hang up this phone, and then I'm going to show these people what you don't want them to see. I'm going to show them a world without you. A world without rules or controls, borders or boundaries. A world where anything is possible. Where we go from there is a choice I leave to you. - Neo.

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