Snook
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Resurrection
Snook can be a highly rewarding game but at the same time an unforgiving one. You can play Snook for a long time, increasing in size and collecting Traits and Special Skins, all to suddenly die because you lost concentration for a second.
We feel you. That's why we came up with a resurrection workaround.
When your snook dies in-game, you can say your goodbyes, which means the snook-NFT will be burned. Alternatively, you have 1 hour to resurrect it (with almost all of its pre-death attributes). But, it'll cost you.

## Resurrection cost

The resurrection cost is proportional to the number of Traits a snook has when it dies. The more Traits it had, the higher the cost. This cost is part FT ($SNK) and part in-kind - hold your horses - here's the explanation: When a player resurrects a snook for the first time she has to sacrifice one random Trait. That means the resurrected snook will lose a Trait. If it is resurrected for the second time, it will lose 2 Traits, etc. Now you see why it's always good to collect and have spare Traits? Let $R$ [$SNK] be the resurrection price;
and let
$b$
be (:D) the number of Traits;
and let
$k$
[\$SNK] be the price for a single minted snook;
and let
$d_b$
be the level of rarity/difficulty of the snook;
and let
$_{s_b}$
be the ranking of the snook, then:
$R = kd_b\\d_b = b^2e^{s_b}$

## ​$d$ is for difficulty

$d_b$
adjusts the resurrection price of the snook based on the number of Traits the snook has in the moment of death and his ranking relative to all other snooks.

## Example

Let the standing
$_{s_{10}} = 0.7$
of a given snook with 10 Traits means that 70% of all the snooks have a number of Traits equal to or smaller than the number of Traits that snook has.
To calculate
$d_b$
we will use mock data for 10 different snooks as shown in the table below:
In the table we can see that we have 5 snooks with 1 property (i.e. Trait), 4 snooks with 2 properties, and 1 snook with 4 properties:
Based on the above, we can build a probability density function:
$f(b) = \frac{n_b}{\Sigma_{b=1}^{b=N}{n_b}}$
wherein
$N$
in the maximum number of properties. In our example
$N=4$
so
$\Sigma_{b=1}^{N=4}{n_b}=n_1 + n_2 + n_3 + n_4 = 5 + 4 + 0 + 1 = 10$
Thus,
$f(b)$
for the example is:
Let
$_{s_b}$
be a cumulative probability function:
$s_b = s(b) = \Sigma_{i=1}^{i=b}{f(i)} \\ 0 < {s(b)} \leq{1}$
so for our example:
The following tables show values of
$d_b$
for
$b = [1..4]$
: