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
$b = [1..4]$
: 