Users bet on the prediction market and receive winnings from other users.

SX is a non-custodial, community-owned sports betting and political prediction market. It is built on SX Network, an EVM-compatible Polygon Edge application blockchain. SX was founded by Andrew Young, Julian Wilson, Jake Hannah, and launched in 2019.

SX tokenholders own and govern the protocol. They currently take all of the total betting fees paid by users (revenue). SX's investors include CMCC Global, Nascent, Sybil Capital, etc.

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Prediction markets

# SX Average revenue per user (ARPU) Methodology

## 1. Overview

Average Revenue Per User (ARPU) for SX measures the daily revenue generated per active user on the SX prediction market platform. This metric answers the business question: "How much revenue does each active user generate on average per day?" The calculation divides total daily revenue by the number of daily active users, providing insight into user monetization effectiveness and platform revenue efficiency.

## 2. Data sources

- **External API data**: Daily metrics from SX's prediction market platform API including revenue amounts and active user counts
- **Token price feeds**: End-of-day price feeds for USD conversion of revenue amounts
- **Token metadata**: Mapping data to convert denomination fields to standardized token addresses
- **Business classification data**: Standard categorizations for prediction market business lines and segments

## 3. Step-by-step calculation

### Component A: Revenue data extraction
- **Purpose**: Extract and standardize daily revenue amounts from SX's prediction market platform
- **Logic**: Pull daily revenue data from SX API scraper starting from December 7, 2020. Each record contains a timestamp, denomination (token type), and revenue amount. Cast revenue amounts to numeric format and map denomination fields to standardized token addresses. Classify all revenue under 'Prediction market' business line and 'betting' business segment with 'prediction_markets' market sector. Set chain as 'unknown' since this is aggregated platform data rather than blockchain-specific.
- **Key fields**: `timestamp` for daily boundaries, `denomination` mapped to `token_address`, `revenue` cast as `amount`

### Component B: Daily active user calculation  
- **Purpose**: Calculate daily active users across multiple dimensional breakdowns
- **Logic**: Extract user counts from the same SX API scraper source, classifying all users as 'Transaction sender' type. Create three dimensional views: project-level aggregation (no breakdown), chain-level breakdown (using 'sx' as chain identifier), and user-type breakdown. Apply data cutoff at January 18, 2024 due to API adapter limitations. Fill missing dates with zero user counts to ensure complete time series.
- **Key fields**: `timestamp` for daily boundaries, `users` cast as `user_dau`, `user_type` classified as 'Transaction sender'

### Component C: Fees to revenue mapping
- **Purpose**: Standardize revenue data through fees classification
- **Logic**: Take the extracted revenue amounts and process them as fees data, then map fees directly to revenue. This intermediate step ensures consistency with Token Terminal's standard revenue classification methodology. Apply the same date filtering (starting December 7, 2020) and gap-filling strategy (zeros for missing days).
- **Key fields**: `fees` mapped to `revenue`, maintaining timestamp and dimensional structure

### Final aggregation
- **Combining components**: Join daily revenue totals with daily active user counts by matching timestamp values. Both datasets cover the same time period with aligned daily boundaries.
- **Currency conversion**: Convert revenue amounts to USD using end-of-day price feeds based on the token denomination identified in the revenue data
- **Time aggregation**: Data is already aggregated at daily level from the API source. Time boundaries follow standard daily intervals with missing dates filled with zeros.
- **Final calculation**: Divide total daily revenue (in USD) by daily active user count to produce ARPU. Handle division by zero cases by setting ARPU to zero when no active users are present. Apply final schema validation and ensure complete time series coverage through gap-filling strategy.

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

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Average revenue per user (ARPU)

Average revenue per user (ARPU)

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