BOB batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

BOB is an L2 smart contract platform for decentralized applications on Ethereum. BOB was founded by Alexei Zamyatin and Dominik Harz and launched in 2024.

BOB team & investors own and govern the protocol. BOB's investors include Castle Island Ventures, IOSG Ventures, Coinbase Ventures, etc.

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Blockchains (L2)

BOB (Build on Bitcoin)

# BOB (Build on Bitcoin) Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that initiated at least one transaction on the Bob blockchain within a rolling 30-day period. For blockchain protocols, active addresses are equivalent to active users since each transaction sender represents a unique user. This metric provides insight into Bob's user adoption and engagement trends by tracking how many distinct addresses are actively using the network each month.

## 2. Data sources

- **Bob blockchain transactions**: Complete transaction records from the Bob Layer 2 blockchain including transaction timestamps, hashes, and sender addresses
- **Chain availability configuration**: Token Terminal's internal data quality filters to ensure complete and reliable data coverage

## 3. Step-by-step calculation

### Component A: User identification from transactions
- **Purpose**: Extract and standardize all unique transaction senders from Bob blockchain
- **Logic**: All transactions from the Bob blockchain are processed to identify sender addresses. Each transaction sender is classified as a "Transaction sender" user type. The model assigns Bob to the Layer 2 blockchain market sector and blockchain business segment. Transaction timestamps and sender addresses are cast to standard formats for downstream processing.
- **Key fields**: `block_timestamp` (transaction timing), `from_address` (sender address), `transaction_hash` (unique transaction identifier)

### Component B: Daily active user calculation  
- **Purpose**: Create daily snapshots of unique active addresses
- **Logic**: Transaction data is grouped by day using timestamp truncation to day boundaries. For each day, distinct sender addresses are counted to identify unique active users. This creates a foundation dataset where each record represents a unique address that was active on a specific day. The process includes incremental loading logic to efficiently process only recent data updates.
- **Key fields**: `timestamp` (daily boundary), `user_address` (distinct sender), `chain` (Bob identifier)

### Component C: HyperLogLog sketch creation
- **Purpose**: Enable efficient distinct counting across large time windows
- **Logic**: Daily active user data is converted into HyperLogLog sketches using BigQuery's HLL functions. Each day's unique addresses are processed into an HLL sketch with precision 15. These sketches allow for memory-efficient merging and distinct counting operations across rolling time windows. Data is grouped by timestamp, chain, user type, and product dimensions.
- **Key fields**: `timestamp` (daily date), `hll` (HyperLogLog sketch of addresses)

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for monthly active user calculation
- **Logic**: A complete datespine is generated covering all days from the earliest transaction date to yesterday. For each day in the datespine, a 30-day lookback window is created by generating dates from 29 days prior through the current day. This creates join relationships between each target day and its corresponding 30-day historical period. The datespine ensures no days are skipped even if no transactions occurred.
- **Key fields**: `timestamp` (target day), `joinday` (days within 30-day window)

### Component E: Window aggregation with HLL merging
- **Purpose**: Calculate monthly active users by merging HLL sketches across 30-day windows
- **Logic**: For each day, HLL sketches from the corresponding 30-day window are merged using HyperLogLog merge operations. The merged sketch represents all unique addresses that were active within that 30-day period. Final distinct counts are extracted from the merged HLL sketches. This process is repeated for each day to create a continuous time series of 30-day rolling monthly active user counts.
- **Key fields**: `timestamp` (target day), `user_mau` (30-day unique address count)

### Component F: Dimensional breakdowns
- **Purpose**: Create multiple analytical perspectives of monthly active users
- **Logic**: The monthly active user calculation is broken down across four dimensional views. The `project_dbt_tag` dimension provides protocol-level aggregation. The `chain` dimension shows Bob-specific metrics. The `user_type` dimension segments by transaction sender categories. The `product_id` dimension allows for product-level analysis. Each breakdown uses the same HLL merging logic but groups by different dimensional attributes.
- **Key fields**: `dimensions` (breakdown category), `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: The multiple dimensional breakdowns are combined using union operations to create a unified dataset with different analytical perspectives of the same underlying monthly active user calculation
- **Schema standardization**: The finalization macro applies consistent schema formatting, data type casting, and standardized column naming conventions across all dimensional views
- **Missing data handling**: Zero-filling logic ensures that periods with no activity are represented with zero values rather than missing records, maintaining complete time series continuity
- **Final calculation**: Monthly active addresses are extracted by selecting only the `chain` and `project_dbt_tag` dimensional breakdowns from the monthly active user dataset, with the `user_mau` field renamed to `active_addresses_monthly` to reflect the specific metric being calculated

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

Unique addresses active over a rolling 30-day window.

BOB (Build on Bitcoin)BOB

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown