Autonity's Forecastathon ran four seasons of forecasting competitions on AFP markets. Seasons 1–3 used a four-component composite that rewarded performance, liquidity provision, accuracy, and community growth. Season 4 simplified to raw PnL.
Mechanics documented for educational record. NTN was the native token at the time of Forecastathon; it had no guaranteed liquidity and scoring marked NTN_ILLIQUID on every output. Not financial advice.
A simple PnL leaderboard favours participants who get lucky on one big position, not participants who help the market function. Autonity designed the S1–S3 composite to reward four distinct contributions to market quality:
Every Forecastathon output carried the NTN_ILLIQUID flag. NTN (Autonity's native token) had no established external market at the time of the competition. Reported NTN balances and rewards could not be reliably converted to any reference currency. This flag is preserved in every historical scoring record to prevent misrepresentation of results.
Enter a participant's season statistics. The calculator produces the composite score and component breakdown for the selected season format.
The S1–S3 composite created measurable market quality benefits: participants competed on volume and accuracy, not just luck. But it also created complexity that made results harder for participants to predict and verify. By Season 4, Autonity simplified to pure PnL percentage: easier to audit, easier to communicate.
The tradeoff: S4's simple measure re-exposed the platform to luck-based strategies. A participant who opened one large position on the right binary contract could outrank every consistent performer. This tension between composite quality scores and simple transparent metrics is unsolved across prediction market design.
Polymarket and Kalshi use leaderboards sorted by PnL percentage (similar to S4). Metaculus uses calibration scoring (similar to S1–S3 accuracy component). No major prediction market currently uses all four components. The Forecastathon composite design remains a reference point in academic market microstructure literature on decentralised forecasting incentives.