AI Insight
Researchers have created the first comprehensive dataset tracking the complete lifecycle of decentralized prediction markets, integrating data from October 2020 to present across market creation, trading, and settlement phases. The dataset contains over 3.29 million market records, 1.90 billion order executions, and 21 million oracle events from platforms like Polymarket, which has processed over $50 billion in trading volume. The unified system resolves fragmentation issues by linking on-chain smart contract data with off-chain sources through a three-layer relational database structure.
Why it matters
This dataset enables systematic analysis of how prediction markets function as forecasting tools, with demonstrated applications in sports betting calibration, economic indicator tracking, and blockchain security research. The public availability of continuously updated data and analysis tools could improve understanding of collective forecasting accuracy and market manipulation risks.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Prediction markets are markets for trading claims on universal future events (e.g., presidential elections). Fueled by a meteoric surge with over $50 billion trading volume, they have emerged as a promising forecasting mechanism, where their prices provide continuously updated signals of collective beliefs. In decentralized platforms (e.g., Polymarket), the prediction market lifecycle include six stages: market creation, token registration, trading, oracle interaction, dispute, and final settlement. However, comprehensively tracking this complete pipeline remains a major challenge, as the underlying data are severely fragmented across heterogeneous on-chain smart contracts and off-chain sources. To fill this critical gap, we present the first continuously synchronized dataset suite for the full-lifecycle of decentralized prediction markets. To achieve large-scale cross-source integration, incomplete linkage, and continuous synchronization, we build a unified relational data system that integrates three canonical layers: i) market metadata, ii) fill-level trading records, iii) oracle-resolution events, through identifier resolution, on-chain recovery, and incremental updates. The resulting dataset spans from October 2020 to update-to-date and comprise more than 3.29 million market records, over 1.90 billion order execution records, and nearly 21 million oracle events. We describe the data model, collection pipeline, and consistency mechanisms that make the datasets reproducible and extensible. We further demonstrate its utility for multiple communities through NBA outcome calibration for sport traders, CPI expectation reconstruction for economists, and oracle-risk analysis for blockchain researchers. A public website with dataset access, interactive visualizations, and lightweight LLM-assisted exploration tools are publicly available at https://www.polymonitor.club.