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Blockchains (L2)

Boba Network

# Boba Network Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique wallet addresses that initiated at least one transaction on the Boba Network within a rolling 30-day period. This metric provides insight into user engagement and adoption on the Boba Network layer 2 blockchain. The calculation uses probabilistic counting techniques (HyperLogLog) to efficiently track distinct addresses across rolling monthly windows while providing dimensional breakdowns for comprehensive analysis.

## 2. Data sources

The metric relies on the following raw data sources:

- **Boba Network transaction records**: Raw transaction data from the Boba Network blockchain containing transaction metadata including timestamps, transaction hashes, sender addresses, and block information
- **Chain availability configuration**: Metadata defining data availability periods for the Boba Network to ensure accurate historical reporting boundaries

## 3. Step-by-step calculation

### Component A: Transaction user identification
- **Purpose**: Extract and classify unique transaction senders from raw Boba Network transaction data
- **Logic**: Process raw Boba Network transaction records to identify unique sender addresses. Each transaction record provides the `from_address` field representing the wallet that initiated the transaction. Records are enriched with business context including chain identifier ('bobanetwork'), market sector classification ('blockchains_l2'), business segment ('blockchain'), and user type classification ('Transaction sender'). All transactions on Boba Network are treated equally regardless of transaction type or value.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for uniqueness, `from_address` for user identification, plus enriched context fields

### Component B: Daily active user calculation
- **Purpose**: Aggregate unique transaction senders by day to create daily active user records
- **Logic**: Group transaction records by day using timestamp truncation to the day boundary. For each day, identify all distinct `from_address` values that initiated transactions. Each unique address per day becomes a daily active user record. This creates the foundation for rolling window calculations by establishing daily user activity snapshots.
- **Key fields**: `timestamp` (day-truncated), `user_dau` (the unique address), plus dimensional attributes for downstream grouping

### Component C: HyperLogLog sketch creation and rolling window aggregation
- **Purpose**: Build probabilistic data structures for efficient distinct counting across 30-day rolling windows
- **Logic**: Create HyperLogLog sketches for each day's unique users to enable efficient approximate distinct counting. Generate a comprehensive datespine covering all days in the analysis period. For each target day, construct a 30-day rolling window by joining the target day with the previous 29 days of data. Use HyperLogLog merge operations to combine sketches across the 30-day window and extract the distinct user count. This approach efficiently handles the computational complexity of rolling distinct counts across large datasets.
- **Key fields**: HyperLogLog sketches containing compressed user address information, `timestamp` for window boundaries

### Component D: Dimensional breakdown and aggregation
- **Purpose**: Calculate monthly active users across multiple business dimensions
- **Logic**: Apply the rolling 30-day calculation across four dimensional breakdowns: project-level aggregation (combining all Boba Network activity), chain-level aggregation (specifically for Boba Network), user-type aggregation (all users are 'Transaction sender' type), and product-level aggregation (currently null for Boba Network). Each dimension provides a different analytical perspective while maintaining the same core 30-day rolling distinct user calculation. Results from all dimensional breakdowns are combined into a unified dataset.
- **Key fields**: `dimensions` field indicating breakdown type ('project_dbt_tag', 'chain', 'user_type', 'product_id'), `user_mau` containing the distinct user count

### Final aggregation
- **Combining components**: Select monthly active user records specifically for 'chain' and 'project_dbt_tag' dimensions, which represent the most relevant breakdowns for the active addresses metric. The `user_mau` field is renamed to `active_addresses_monthly` to reflect blockchain-appropriate terminology.
- **Currency conversion**: Not applicable - this metric counts addresses rather than monetary values
- **Time aggregation**: Uses rolling 30-day windows calculated daily, meaning each day represents the count of unique addresses active in that day plus the previous 29 days
- **Final calculation**: The final metric value represents the distinct count of wallet addresses that initiated at least one transaction on Boba Network within the rolling 30-day period ending on each date. Missing periods are filled with zero values, and results are filtered based on Boba Network data availability periods to ensure accurate historical reporting.

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

Unique addresses active over a rolling 30-day window.

Boba NetworkBOBA

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown