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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Polygon

# Polygon Average fees per user (AFPU) Methodology

## 1. Overview

Average Fees Per User (AFPU) measures the average amount of transaction fees paid per unique daily active user on the Polygon blockchain. This metric provides insights into user engagement intensity and the economic activity generated per user. The calculation divides total daily fees collected by the network by the number of unique users (transaction senders) active on that day.

## 2. Data sources

- **Polygon blockchain transaction records**: Complete transaction data including sender addresses, recipient addresses, gas consumption, and gas pricing information
- **End-of-day WPOL token price feeds**: Daily closing prices for Wrapped POL (WPOL) token used for USD conversion of native token fees
- **Chain availability configuration data**: Metadata indicating periods when Polygon blockchain data is considered reliable and complete for metric calculations

## 3. Step-by-step calculation

### Component A: Transaction data extraction
- **Purpose**: Extract core transaction information needed for both fee calculation and user identification
- **Logic**: Process Polygon blockchain transaction records to capture transaction hash, sender address (`from_address`), recipient address (`to_address`), actual gas consumed (`gas_used`), and effective gas price paid (`effective_gas_price`). Gas prices are converted from wei to token units by dividing by 10^18. Each transaction is classified with market sector as "blockchains_l1", business segment as "blockchain", and chain identifier as "polygon". The WPOL token address `0x0d500b1d8e8ef31e21c99d1db9a6444d3adf1270` is associated with each record for subsequent price conversion.
- **Key fields**: `block_timestamp`, `transaction_hash`, `from_address`, `to_address`, `gas_used`, `effective_gas_price`, `token_address`

### Component B: Daily fee calculation
- **Purpose**: Calculate total transaction fees collected by the Polygon network each day
- **Logic**: For each transaction, multiply the actual gas consumed (`gas_used`) by the effective gas price paid (`effective_gas_price`) to determine the total fee paid. Aggregate these individual transaction fees by day using timestamp truncation to daily boundaries. This produces the total fees collected by the network each day in native token units.
- **Key fields**: `timestamp` (daily), `fee_amount`

### Component C: USD fee conversion
- **Purpose**: Convert native token fees to USD for standardized reporting
- **Logic**: Apply the currency conversion macro using end-of-day WPOL token price feeds to convert daily fee amounts from native tokens to USD. The conversion uses the WPOL token address `0x0d500b1d8e8ef31e21c99d1db9a6444d3adf1270` to match with appropriate price data. No decimal correction is applied since gas calculations already account for token decimals.
- **Key fields**: `timestamp`, `fees` (in USD), `market_sector_id`, `business_segment_id`, `chain_id`, `token_address`

### Component D: Daily active user counting
- **Purpose**: Count unique users (transaction senders) active each day
- **Logic**: Extract distinct sender addresses (`from_address`) from all transactions within each day using timestamp truncation to daily boundaries. Users are classified as "Transaction sender" type. Apply HyperLogLog (HLL) functions to efficiently count unique addresses even with large datasets, creating sketches that can be merged across different dimensional breakdowns including project-level aggregation, chain-level, user type, and product-level groupings.
- **Key fields**: `timestamp`, `user_type`, `user_dau` (unique address), `chain`, `product_id`

### Component E: User count aggregation
- **Purpose**: Aggregate unique user counts across multiple dimensional breakdowns
- **Logic**: Use HyperLogLog merge operations to combine user count sketches across various dimensions including project-level totals, chain-specific counts, user type breakdowns, and product-specific counts. Extract final unique user counts from the merged HLL sketches. This approach enables efficient distinct counting while supporting multiple analytical perspectives.
- **Key fields**: `timestamp`, `dimensions`, `chain_id`, `product_id`, `user_type`, `user_dau` (final count)

### Final aggregation
- **Combining components**: Apply the ARPU calculation macro to divide daily USD fees by daily active user counts, creating the average fees per user metric for each day
- **Currency conversion**: Fees are converted to USD using end-of-day WPOL token price feeds, ensuring consistent currency representation across all calculations
- **Time aggregation**: Both fees and user counts are aggregated to daily boundaries using timestamp truncation, ensuring aligned time periods for the division operation
- **Final calculation**: The metric value equals total daily fees in USD divided by the count of unique daily active users (transaction senders). Missing days are filled with zero values to maintain complete time series continuity. Chain availability filters are applied to exclude periods with potentially unreliable data.

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

PolygonPOL

Average fees per user (AFPU)

Average fees per user (AFPU)

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