Building quest rewards is more than picking winners by rank. A common approach is to structure rewards by rank buckets, then add a small “random allocation” pool to keep the experience balanced and engaging.
This article outlines the strategy described in the video: use rank buckets for most payouts, and allocate about 10% of total rewards to random participants outside the top 100.
Reward distribution by rank buckets
One of the most common ways to distribute rewards is to divide participants into rank “buckets.” Instead of rewarding everyone the same, you define rank ranges and attach different reward amounts to each range.
For example, you might define buckets such as:
- ranks 4–10
- ranks 10–25
Each bucket can receive a different reward amount, allowing you to shape how much value you attach to different performance levels.
The key idea is that bucket-based distribution creates an intentional gradient: participants understand that higher ranks align with different payout sizes than lower ranks.
How to set different reward amounts per bucket
Once you choose your buckets, the next step is deciding how much each bucket receives. The transcript summary emphasizes that you should assign different reward amounts per bucket, based on the rank ranges you define.
In practice, the system design goal is straightforward: make the reward structure reflect performance tiers.
The video’s framing supports this by treating bucket allocation as the primary mechanism—most rewards follow the rank bucket rules, while a smaller portion is reserved for a different purpose (random allocation).
Using a smaller portion for random participants
A purely rank-driven reward system can feel deterministic: the top ranks always take the best share, and everyone else sees little variation.
To address this, the strategy described in the video recommends reserving a smaller portion of the overall reward pool for randomness. Instead of selecting only by rank, you use a random draw for a portion of rewards.
Importantly, this “random pool” is not described as a replacement for bucket logic—it’s an additional layer.
Recommended 10% random allocation outside top 100
The standout recommendation is specific: allocate around 10% of the total rewards to random participants outside the top 100 bucket.
In other words:
- Keep the majority of the reward allocation tied to your bucket system.
- Take roughly 10% and pick random participants from beyond the top 100 ranks.
The transcript summary highlights that the random allocation should be outside the top 100 bucket, reinforcing the idea that the random pool is meant to spread value beyond the very highest tier without undermining the bucket structure.
A simple way to think about it is a “mostly merit-based, slightly unpredictable” model:
- Most rewards: determined by rank buckets.
- A smaller share (~10%): awarded to random participants, specifically outside the top 100.
Why random allocation matters in rewards strategy
The video summary presents the random allocation as an important part of the overall rewards strategy. Its purpose is to improve fairness and engagement by ensuring that rewards are not only granted to the very top-ranked participants.
This matters because a bucket system can concentrate rewards among participants who already place near the top. Adding a random component introduces variation for those outside the top tier.
As described, the combined approach helps make the program feel:
- merit-based (because bucket allocation still drives most rewards), and
- engaging (because a portion of rewards can go to people outside the top ranks via randomness).
Practical takeaways for Zealy/Galxe-style quests
If you’re designing quest rewards for communities like Zealy or Galxe, this strategy offers a clear baseline:
- Use rank buckets to define reward tiers.
- Assign different reward amounts to each bucket.
- Reserve a smaller portion of the total rewards for randomness.
- Allocate about 10% to random participants outside the top 100.
- Keep the bucket system as the primary driver of reward distribution.
This creates a structured rewards plan where performance matters, while a small random pool helps keep the system feel less purely deterministic.
Conclusion
A smart quest rewards strategy can combine structure with controlled randomness. Use rank buckets to distribute the majority of rewards, then allocate around 10% of the total rewards to random participants outside the top 100. This mixed approach—primarily bucket-driven with a small random layer—aims to support fairness and engagement while maintaining a clear rewards gradient.
