Collators aggregate parachain transactions block candidates and producing state transition proofs for validators based on those blocks.

Mythos is a specialized gaming blockchain powered by Polkadot Relay chain.

MYTH holders own and govern the protocol.

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Mythos

# Mythos Active users (weekly) Methodology

## 1. Overview

The Mythos weekly active users (WAU) metric measures the number of unique users who interact with any part of the Mythos ecosystem within a 7-day rolling window. This metric aggregates user activity across three main Mythos products: the Mythos blockchain itself, the Mythos marketplace for NFT trading, and the DMarket integration. The calculation uses HyperLogLog sketches for efficient approximate distinct counting and provides breakdowns by project level, chain, user type, and individual products.

## 2. Data sources

- **Mythos blockchain event logs**: Raw event data from the Mythos blockchain containing structured event information with JSON-formatted parameters
- **ProxyExecuted events**: Events from the `mythProxy` pallet capturing general blockchain transactions and their senders
- **OrderExecuted events**: Events from the `marketplace` pallet capturing NFT marketplace transactions with buyer and seller information
- **Trade events**: Events from the `dmarket` pallet capturing DMarket NFT trading activity with buyer and seller details
- **Event metadata**: Block timestamps, extrinsic IDs, and event classification data (pallet names, method names)

## 3. Step-by-step calculation

### Component A: Mythos blockchain users extraction
- **Purpose**: Identifies users who perform general transactions on the Mythos blockchain
- **Logic**: Extracts user addresses from `ProxyExecuted` events in the `mythProxy` pallet. These events capture when users execute transactions through proxy contracts. The `user_address` is parsed from the first element of the JSON event data array. All users are classified as "Transaction sender" type and assigned to the "Mythos Substrate" product category.
- **Key fields**: `block_timestamp`, `user_address` (from event data), `user_type` ("Transaction sender"), `product_id` ("Mythos Substrate")

### Component B: Marketplace users extraction  
- **Purpose**: Identifies users participating in NFT trading on the Mythos marketplace
- **Logic**: Processes `OrderExecuted` events from the `marketplace` pallet to extract both buyers and sellers. Creates separate records for each role, with buyers classified as "NFT buyer" and sellers as "NFT seller". User addresses are extracted from the fourth position (buyer) and third position (seller) in the JSON event data array. All users are assigned to the "marketplace" product category.
- **Key fields**: `block_timestamp`, `buyer` and `seller` addresses (from event data), `user_type` ("NFT buyer" or "NFT seller"), `product_id` ("marketplace")

### Component C: DMarket users extraction
- **Purpose**: Identifies users participating in DMarket NFT trading integration
- **Logic**: Processes `Trade` events from the `dmarket` pallet, extracting both buyer and seller addresses from the first two positions in the JSON event data array. Like marketplace data, creates separate records for buyers ("NFT buyer") and sellers ("NFT seller"). All users are assigned to the "dmarket" product category.
- **Key fields**: `block_timestamp`, `buyer` and `seller` addresses (from event data), `user_type` ("NFT buyer" or "NFT seller"), `product_id` ("dmarket")

### Component D: Daily active users aggregation
- **Purpose**: Combines all user activity into daily distinct user counts
- **Logic**: Unions user data from all three components and truncates timestamps to daily granularity. Removes duplicates to ensure each unique combination of date, chain, user type, product, and user address appears only once per day. This creates the foundation dataset showing which unique users were active on each day.
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` ("day"), `chain` ("mythos"), `user_type`, `product_id`, `user_address`

### Component E: HyperLogLog sketch creation
- **Purpose**: Converts daily user data into efficient data structures for approximate distinct counting
- **Logic**: Groups daily user data by date, chain, user type, and product, then initializes HyperLogLog sketches using `hll_count.init()` with precision parameter 15. This creates compact representations of user sets that can be efficiently merged for rolling window calculations.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch)

### Component F: Rolling window generation
- **Purpose**: Creates 7-day rolling windows for each date to calculate weekly active users
- **Logic**: Generates a complete datespine from the earliest user activity date to the current date. For each date in the spine, creates a 7-day window by generating an array of dates from 6 days prior through the current date. This ensures consistent weekly windows even when some days have no user activity.
- **Key fields**: `timestamp` (window end date), `joinday` (each day within the 7-day window)

### Component G: Window-based HLL aggregation  
- **Purpose**: Aggregates user activity across 7-day windows using HyperLogLog sketches
- **Logic**: Joins the rolling window dates with daily HLL sketches, matching each `joinday` to the corresponding daily sketch. This creates a dataset where each row represents one day's user activity within a specific 7-day window, allowing for efficient aggregation of unique users across the full window period.
- **Key fields**: `timestamp` (window end date), `timestamp_grain`, `chain_id`, `user_type`, `product_id`, `hll`

### Component H: Dimensional breakdowns
- **Purpose**: Calculates WAU across different analytical dimensions
- **Logic**: Creates four separate aggregation views by grouping the windowed data differently. Project-level breakdown aggregates all users regardless of chain, user type, or product. Chain-level breakdown groups by chain while nullifying other dimensions. User type breakdown groups by user type classification. Product breakdown groups by individual products. Each breakdown merges HLL sketches within groups and extracts the final distinct count.
- **Key fields**: `timestamp`, `dimensions` (breakdown type), dimension-specific fields (`chain_id`, `user_type`, `product_id`), `user_wau` (distinct user count)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a single result set with multiple analytical perspectives of the same underlying user activity
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows with daily granularity, where each date represents the end of a 7-day period
- **Final calculation**: Weekly active users are calculated by merging HyperLogLog sketches across 7-day windows using `hll_count.merge_partial()` and extracting the approximate distinct count with `hll_count.extract()`. The result represents unique users who were active at least once during any 7-day period ending on the specified date.

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 7-day window.

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Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown