Players wager against the house (liquidity provided by BetSwirl or their partners) in randomized games of chance, such as coin tosses or dice rolls.

Betswirl is a non-custodial betting platform built on Polygon, BNB Chain, and Avalanche. BetSwirl was co-founded by Romuald Hog and Nato, and was launched in March 2022.

The BetSwirl team own and govern the protocol. They and BETS holders currently receive all fees paid by players (revenue).

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BetSwirl

# BetSwirl Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Betswirl measures the number of unique users who played any game on the Betswirl gambling platform within a rolling 30-day period. This metric uses HyperLogLog (HLL) approximation algorithms for efficient computation of distinct user counts across large datasets and multiple blockchain networks. The calculation provides dimensional breakdowns by blockchain, user type, and product to enable detailed analysis of user engagement patterns.

## 2. Data sources

- **Betswirl game events**: Parsed `Roll` events from Betswirl smart contracts across multiple game types including CoinToss, Dice, Keno, Roulette, and RussianRoulette
- **Multi-chain coverage**: Events from BSC, Polygon, Avalanche, and Arbitrum blockchain networks
- **Contract versions**: Multiple versions of each game contract (v1 through v5) to capture the complete historical activity
- **Event parameters**: Player address information extracted from event logs, with special handling for RussianRoulette events that contain arrays of multiple player addresses per game
- **Blockchain metadata**: Block timestamps and transaction hashes for temporal and transactional context

## 3. Step-by-step calculation

### Component A: Raw event extraction and user identification
- **Purpose**: Extract player addresses from all Betswirl gaming activity across multiple blockchains and contract versions
- **Logic**: Parse `Roll` events from CoinToss, Dice, Keno, and Roulette contracts across BSC, Polygon, Avalanche, and Arbitrum. Extract the `user` parameter containing player addresses. For RussianRoulette events, special handling unnests the `seats` array to capture all players participating in multi-player games. Each event is tagged with chain identifier, user type as "Player", market sector as "prediction_markets", and business segment as "betting"
- **Key fields**: `block_timestamp` for temporal analysis, `user_address` for player identification, `chain` for blockchain context, `user_type` standardized as "Player"

### Component B: Daily active user aggregation
- **Purpose**: Calculate daily distinct active users as the foundation for MAU computation
- **Logic**: Group all user activity events by day using timestamp truncation to day granularity. Within each day, identify distinct user addresses to calculate Daily Active Users (DAU). Maintain dimensional attributes including chain, user type, market sector, business segment, and product identifiers. This creates the base metric that feeds into the rolling MAU calculation
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing distinct user addresses, dimensional attributes for breakdown analysis

### Component C: HyperLogLog sketch creation and rolling window preparation
- **Purpose**: Convert daily user data into HLL sketches and prepare for 30-day rolling window calculations
- **Logic**: Transform daily distinct user counts into HyperLogLog sketches using precision 15 for approximate but efficient counting. Create a comprehensive datespine covering the full time range from earliest data to current date. Generate join tables that map each day to its preceding 29 days, creating the foundation for 30-day rolling window calculations. This approach ensures no missing days and enables efficient HLL merging operations
- **Key fields**: `hll` sketches for each day's users, `timestamp` for temporal alignment, dimensional breakdowns maintained through the transformation

### Component D: Rolling MAU calculation using HLL merging
- **Purpose**: Calculate 30-day rolling MAU by merging HLL sketches across overlapping windows
- **Logic**: For each day, join with the HLL sketches from that day plus the previous 29 days. Merge all HLL sketches within each 30-day window using HLL merge operations, then extract the final distinct count approximation. This produces a daily time series where each point represents the unique users active in the trailing 30-day period ending on that date
- **Key fields**: Merged `hll` sketches across 30-day windows, extracted `user_mau` counts

### Final aggregation
- **Combining components**: The rolling MAU data is broken down across four dimensional hierarchies - project level (all data combined), chain level (BSC, Polygon, Avalanche, Arbitrum), user type level (all "Player"), and product level (currently null for Betswirl)
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily granularity with each day representing a 30-day rolling window ending on that date
- **Final calculation**: Each day's MAU value represents the approximate count of distinct users who were active in any Betswirl game during the 30-day period ending on that date, calculated by merging HyperLogLog sketches and extracting the final distinct count estimate

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

Unique revenue-generating users over a rolling 30-day window.

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Active users (monthly)

Active users (monthly)

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