opBNB is built on the OP Stack, the same underlying framework as Optimism. It extends the Ethereum Virtual Machine (EVM) to provide faster and cheaper transactions, while leveraging BNB Smart Chain (BSC) for ecosystem integration.

opBNB is an Ethereum-compatible Layer 2 scaling solution designed to improve the performance and affordability of decentralized applications on BNB Chain. It provides high throughput and low fees, making it suitable for gaming, social, and DeFi applications.

opBNB is developed by BNB Chain, which is supported by contributors from the broader Binance ecosystem.

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opBNB

# opBNB Average fees per user (AFPU) Methodology

## 1. Overview

Average fees per user (AFPU) measures the daily average transaction fees paid per active user on the opBNB blockchain. This metric is calculated by dividing the total daily fees collected by the network by the number of daily active users. AFPU provides insights into user engagement economics and the average cost burden for network participants.

## 2. Data sources

The calculation relies on the following raw data sources:
- **opBNB transaction records**: Complete transaction data including gas usage, gas prices, Layer 1 fees, and user addresses from the opBNB blockchain
- **End-of-day price feeds**: WBNB token price data used for USD conversion of BNB-denominated fees
- **Token metadata**: Information about the native BNB token on the opBNB chain, including the standard token contract address

## 3. Step-by-step calculation

### Component A: Transaction data extraction
- **Purpose**: Collects raw transaction data and standardizes field formats
- **Logic**: Extracts transaction records from opBNB blockchain including block timestamps, transaction hashes, sender and recipient addresses, gas usage metrics, and gas pricing information. The native BNB token is identified with address `0x4200000000000000000000000000000000000006` and symbol 'BNB'. All numeric fields are cast to appropriate data types for downstream calculations.
- **Key fields**: `block_timestamp`, `transaction_hash`, `from_address`, `to_address`, `gas_used`, `l1_gas_used`, `l1_gas_price`, `l1_fee_scalar`, `gas_price`

### Component B: Gas fee calculation
- **Purpose**: Calculates total transaction fees combining Layer 2 and Layer 1 components
- **Logic**: Computes gas fees for each transaction using opBNB's dual-layer fee structure. Layer 2 fees are calculated as gas used multiplied by gas price, divided by 1e18 for decimal normalization. Layer 1 fees are calculated as L1 gas used multiplied by L1 gas price and L1 fee scalar, then divided by 1e18. The total fee per transaction is the sum of L2 and L1 components.
- **Key fields**: `l2_gas_fee` = (gas_used × gas_price) ÷ 1e18, `l1_gas_fee` = (l1_gas_used × l1_gas_price × l1_fee_scalar) ÷ 1e18

### Component C: Fee aggregation and USD conversion
- **Purpose**: Aggregates daily fees and converts to USD denomination
- **Logic**: Groups transactions by day using timestamp truncation and sums all transaction fees. Fees are converted from BNB to USD using end-of-day WBNB price feeds. The conversion process applies appropriate decimal corrections and price feed timing. Daily fee totals are calculated across all opBNB transactions for each day.
- **Key fields**: Daily aggregated `fees` in USD, `timestamp` (truncated to day), `chain_id` = 'opbnb'

### Component D: User identification and counting
- **Purpose**: Identifies and counts unique daily active users
- **Logic**: Extracts unique user addresses from transaction sender fields (`from_address`) for each day. Users are classified as 'Transaction sender' type and associated with the 'blockchains_l2' market sector. Daily active user counting uses HyperLogLog aggregation to efficiently handle large user sets while maintaining accuracy. Users are counted distinctly within each day boundary.
- **Key fields**: `user_address` (from transaction senders), `user_type` = 'Transaction sender', daily distinct `user_dau` counts

### Component E: HyperLogLog user aggregation
- **Purpose**: Efficiently aggregates distinct user counts across multiple dimensions
- **Logic**: Uses HyperLogLog probabilistic counting to aggregate daily active users across different breakdown dimensions including project-level, chain-level, user-type-level, and product-level views. The HLL algorithm provides approximate distinct counts with high accuracy and low memory usage. Daily HLL sketches are merged and extracted to produce final user counts for each dimension.
- **Key fields**: `hll` sketches for distinct counting, `user_dau` extracted counts, `dimensions` indicating aggregation level

### Final aggregation
- **Combining components**: Daily fee totals (in USD) are divided by daily active user counts to calculate average fees per user. The division uses the aggregated fee metric and the project-level user DAU count to ensure proper alignment of numerator and denominator.
- **Currency conversion**: All fees are converted to USD using end-of-day WBNB price feeds, with appropriate decimal corrections applied during the conversion process.
- **Time aggregation**: Both fees and user counts are aggregated to daily boundaries using timestamp truncation to day granularity, ensuring consistent time alignment between numerator and denominator.
- **Final calculation**: AFPU = Total daily fees (USD) ÷ Daily active users, with zero-filling applied for days with no activity to maintain continuous time series data.

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

opBNB

Average fees per user (AFPU)

Average fees per user (AFPU)

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