Validators process transactions and receive fees from users (supply-side fees).

Polygon is a smart contract platform for decentralized applications & interoperability platform for blockchains. Polygon was founded by Jaynti Kanani, Sandeep Nailwal & Anurag Arjun and launched in 2017.

MATIC holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). Polygon’s investors include Coinbase Ventures, ZBS Capital, MiH Ventures, etc.

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Blockchains (L1)

Polygon

# Polygon Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Polygon measures the number of unique addresses that sent at least one transaction on the Polygon network within any 7-day rolling window. This metric provides insight into user engagement and network adoption by counting distinct transaction senders over weekly periods. The calculation uses HyperLogLog data structures to efficiently handle large-scale unique user counting across rolling time windows.

## 2. Data sources

- **Polygon blockchain transactions**: Raw transaction records from the Polygon network including sender addresses, timestamps, gas usage, and transaction metadata
- **Transaction metadata**: Block timestamps, transaction hashes, block numbers, and gas pricing information
- **Address data**: Transaction sender addresses (`from_address`) representing network users
- **Network metadata**: Chain identifiers, market sector classifications, and business segment data

## 3. Step-by-step calculation

### Component A: Transaction processing
- **Purpose**: Extract and standardize transaction data with user addresses
- **Logic**: Process raw Polygon blockchain transactions by extracting sender addresses (`from_address`), recipient addresses (`to_address`), block timestamps, transaction hashes, gas usage (`receipt_gas_used`), and effective gas prices. Apply decimal corrections to gas pricing by dividing effective gas price by 10^18 to convert from Wei to ETH units. Add metadata including market sector classification as 'blockchains_l1', business segment as 'blockchain', chain identifier as 'polygon', and the wrapped POL token address as the native token reference
- **Key fields**: `from_address` (user identifier), `block_timestamp` (transaction time), `transaction_hash` (unique transaction ID), `gas_used` (transaction cost), `effective_gas_price` (corrected pricing)

### Component B: Daily active user identification  
- **Purpose**: Identify unique users per day from transaction activity
- **Logic**: Group transaction data by day using timestamp truncation and extract unique sender addresses as daily active users. Label all users as 'Transaction sender' user type since the metric focuses on addresses that initiate transactions. Apply chain availability filtering to ensure data quality and include dimensional metadata for product identification, market sectors, and business segments
- **Key fields**: `timestamp` (daily aggregation), `user_dau` (unique daily user addresses), `user_type` (transaction sender classification), `chain` (polygon identifier)

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets to efficient data structures for large-scale unique counting
- **Logic**: Transform daily active user data into HyperLogLog sketches using `hll_count.init` with precision parameter 15. Group data by timestamp, timestamp grain, chain ID, user type, and product ID to maintain dimensional breakdowns. HyperLogLog sketches enable memory-efficient approximate counting of unique users across large datasets while preserving the ability to merge counts across time periods
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (day identifier), dimensional grouping fields for breakdowns

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows for weekly active user calculation
- **Logic**: Generate a complete datespine covering all days from the earliest data through yesterday to avoid gaps in time series. For each date in the datespine, create a 7-day window by joining with the 6 preceding days using `generate_timestamp_array`. This creates a joinday array spanning from 6 days before through the current day. Join these rolling windows with the daily HLL sketches to capture user activity across each 7-day period
- **Key fields**: `timestamp` (window end date), `joinday` (dates within the 7-day window), joined HLL sketches for the window period

### Component E: Dimensional breakdowns
- **Purpose**: Provide multiple analytical perspectives on weekly active users  
- **Logic**: Create separate aggregations for different dimensional views. Generate project-level totals by merging all HLL sketches without dimensional filtering. Create chain-level breakdowns grouped by chain ID. Build user type breakdowns grouped by user classification. Develop product-level views grouped by product ID. Each breakdown uses `hll_count.merge_partial` to combine HLL sketches across the relevant dimensions, then `hll_count.extract` to produce final unique user counts
- **Key fields**: `dimensions` (breakdown type identifier), dimension-specific fields (`chain_id`, `user_type`, `product_id`), `user_wau` (extracted unique count)

### Final aggregation
- **Combining components**: Union all dimensional breakdown results (project, chain, user type, product) into a single dataset with standardized schema including timestamp, dimension type, dimension values, and weekly active user counts
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to standardize output format, add metadata fields, and implement the 'fill_zeros' strategy to populate missing days with zero values rather than nulls
- **Time boundaries**: Use daily boundaries with 7-day rolling windows, ensuring each date represents users active in that day plus the 6 preceding days
- **Final calculation**: The metric represents the count of unique addresses that sent transactions within any 7-day rolling period, calculated by merging HyperLogLog sketches across the window and extracting the approximate unique count

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

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

PolygonPOL

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown