#
#
Slime Rush

is a monthly reward program that distributes 1M SLIME to players based on their monthly points earned via various in-game activities.

###
#
Eligible Game Modes

Players are granted with **Slime Rush Points** based on their activities on following game modes:

- Competitive Races
- Mega Races
- Microwave

###
#
Earning Points

**Example:** If a snail with **5 slime boost** finishes a **500 Slime entry** competitive race as a 1st place where 7 snails were attended, the snail collects 7 base points multiplied by 5 (slime boost value) and 2.5 (cost 500/200) = **87,5 points** from that single race.

###
#
Leaderboards

- Player and snail leaderboards are available via Slime Rush menu.
- Players can track their snails' current points on Snails Tab.
- Estimated rewards are displayed with respect to player points and month's distribution model.
- Estimated rewards becomes visible to players after
**7th day**of the month. - Keep in mind that these value may change as players earn more points. Its a PVP environment.

- Estimated rewards becomes visible to players after
- Players can collect points for a month in between first and last minute of a month in UTC. Races must end with in that month to count towards the final points.

###
#
Reward Distribution

- At the end of each month, rewards will be determined with one of the below distribution models.
- Players can use in-game claim menu to claim the rewards.
- Multiple month rewards can be claimable in a single-tx.

\text{Amount} = k \times (\text{Points})^2

The constant **k** adjusts the scale of the distribution to match the total amount of rewards available for the distribution where **n** represents the number of users;

\text{SumOfSquares} = \sum_{i=1}^{n} (\text{Points}_i)^2

k = \frac{\text{TotalAmount}}{\text{SumOfSquares}}

\text{Amount} = k \times (\text{Points} \cdot \log(Points))

The constant **k** adjusts the scale of the distribution to match the total amount of rewards available for the distribution where **n** represents the number of users;

\text{SumOfLogs} = \sum_{i=1}^{n} (\text{Points}_i \cdot \log({Points}_i))

k = \frac{\text{TotalAmount}}{\text{SumOfLogs}}

\text{Amount} = k \times \text{Points}

The constant **k** adjusts the scale of the distribution to match the total amount of rewards available for the distribution where **n** represents the number of users;

\text{SumOfPoints} = \sum_{i=1}^{n} (\text{Points}_i)

k = \frac{\text{TotalAmount}}{\text{SumOfPoints}}

###
#
Monthly Coefficients

Every month coefficients and distribution model might change with balancing patches. We are experimenting with different settings and try to provide an dynamic but fair field and for all players. Coefficients for each month will be published here before the slime rush month starts.

#####
#
What is the 10 top race limit for competitive racing?

For a single racing day(UTC), a snail can collect points from its top 10 performances from competitive racing. At the end of the day only top 10 scores of that snail is counted for the final result.

#####
#
What is the the purpose of quadratic distribution model?

This model indicates that as the number of points increases, the amount received increases quadratically, leading to a faster growth rate in the amount allocated to players with higher points. Thus, using a single account/wallet is being incentivized.

#####
#
What is the the purpose of logarithmic distribution model?

This model indicates that as the number of points increases, the amount received increases logarithmically, leading to a faster growth rate in the amount allocated to players with higher points. Thus, using a single account/wallet is being incentivized. It grows faster than linear but slower than quadratic.

#####
#
What is the the purpose of linear distribution model?

This model allows an environment where using several wallets become feasible. On quadratic & logarithmic distributions, best strategy is using a single wallet; but with linear distribution players may choose to use several wallets and not get affected by distribution model.