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 Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of distinct users who interacted with BetsWirl's gaming platform within any 7-day period. This metric answers the business question "How many unique players are actively using BetsWirl games on a weekly basis?" The calculation uses a rolling 7-day window approach, where each day's WAU represents the count of distinct users who were active on that day or any of the 6 preceding days. The metric uses HyperLogLog data structures for efficient computation of distinct user counts across large datasets.

## 2. Data sources

- **Blockchain events**: Parsed `Roll` events from BetsWirl gaming contracts across BSC, Polygon, Avalanche, and Arbitrum blockchains
- **Game contract events**: Event logs from multiple BetsWirl game types including `CoinToss`, `Dice`, `Keno`, `Roulette`, and `RussianRoulette` contracts
- **Contract versions**: Data from multiple contract versions (v1 through v5) deployed across different chains
- **Player interaction data**: User wallet addresses extracted from gaming event parameters, primarily from the `Player` field in Roll events
- **Special event handling**: Nested seat data from `RussianRoulette` events that contain multiple player addresses per event

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all user interactions with BetsWirl gaming contracts across supported blockchains
- **Logic**: Extracts player wallet addresses from Roll events emitted by various BetsWirl game contracts. The system processes events from four game types (CoinToss, Dice, Keno, Roulette, and RussianRoulette) across multiple contract versions on BSC, Polygon, Avalanche, and Arbitrum. For standard games, the user address is extracted from the `Player` parameter in Roll events. For RussianRoulette events, the system handles nested seat arrays by unnesting the `seats` field to capture multiple players per event. All users are classified as 'Player' type within the 'prediction_markets' sector and 'betting' business segment.
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `chain` for network classification, `user_type` standardized as 'Player'

### Component B: Daily active user calculation  
- **Purpose**: Aggregates user activity data into daily distinct user counts while preserving dimensional attributes
- **Logic**: Groups user interaction events by day using timestamp truncation to day granularity. Calculates distinct user addresses per day while maintaining dimensional breakdowns by chain, user type, market sector, business segment, and product ID. The result provides a foundation dataset of daily active users with all necessary dimensions for downstream WAU calculation.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing the user address, dimensional fields for grouping

### Component C: HyperLogLog initialization
- **Purpose**: Converts daily user data into HyperLogLog data structures for efficient set operations in WAU calculation
- **Logic**: Takes the daily active user data and initializes HyperLogLog sketches with precision parameter 15. Groups by timestamp, chain, user type, and product dimensions while creating HLL representations of the user sets. This enables efficient merging operations for calculating distinct users across multiple days without storing individual user addresses.
- **Key fields**: `hll` containing the HyperLogLog sketch, all dimensional grouping fields preserved

### Component D: Datespine and window creation
- **Purpose**: Creates a complete time series structure and defines 7-day rolling windows for WAU calculation
- **Logic**: Generates a datespine covering all days from the earliest available data through yesterday to ensure no gaps in the time series. For each date in the spine, creates a 7-day lookback window including the target date and the 6 preceding days. This approach ensures every day has a complete 7-day context for WAU calculation, even when some days have no user activity. The window structure enables joining with daily HLL data to capture all users active within each 7-day period.
- **Key fields**: `timestamp` for the target date, `joinday` representing each day within the 7-day window

### Component E: Window-based HLL aggregation
- **Purpose**: Combines HyperLogLog data across 7-day windows to calculate weekly active user counts
- **Logic**: Joins the windowed datespine with daily HLL data to capture all user activity within each 7-day period. For each target date, merges HLL sketches from all days in the corresponding 7-day window using `hll_count.merge_partial()`. Extracts the final distinct count using `hll_count.extract()` to produce the WAU metric. This approach efficiently handles the set union operations required for rolling window distinct counting.
- **Key fields**: HLL merging functions for set operations, dimensional preservation for breakdowns

### Final aggregation
- **Combining components**: Creates multiple dimensional views of the WAU data including project-level totals, chain-level breakdowns, user type segmentation, and product-level metrics. Each breakdown nullifies non-relevant dimensions while preserving the specific dimension being analyzed.
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses day-level granularity with 7-day rolling windows. Each day's WAU represents distinct users active in that day plus the 6 preceding days
- **Final calculation**: The WAU metric represents the distinct count of user addresses that appear in any Roll event within the 7-day rolling window, calculated using HyperLogLog merge operations for computational efficiency across large user datasets

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

Unique revenue-generating users over a rolling 7-day window.

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Active users (weekly)

Active users (weekly)

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