Predicting the Future: A New Dataset for Forecasting Markets

Author: Denis Avetisyan

Researchers have released a comprehensive dataset from the Polymarket platform, enabling deeper analysis of prediction market dynamics and potential improvements to economic forecasting.

The prediction market demonstrates fluctuating financial activity, with total value locked, 30-day rolling fees, and 30-day rolling revenue all contributing to its dynamic economic landscape.

This work details the construction and analysis of a full-lifecycle dataset for a decentralized prediction market, leveraging on-chain data to study market behavior and forecasting accuracy.

Despite the growing interest in prediction markets as indicators of collective belief, comprehensive datasets tracking their full lifecycle-from market creation to final settlement-remain scarce. This paper, ‘Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments & Analysis]’, addresses this gap by introducing a continuously maintained dataset built on the Polymarket decentralized platform. Comprising over 770 thousand market records and nearly 2 million oracle events, this resource facilitates detailed analysis of market dynamics and downstream applications such as improved macroeconomic forecasting. Will this enhanced data accessibility unlock new insights into the efficiency of prediction markets and their potential as leading economic indicators?

Distilling Foresight: Polymarket and the Wisdom of Crowds

Conventional forecasting methods frequently encounter limitations stemming from systemic biases and incomplete datasets. Subject matter experts, while valuable, can be influenced by cognitive biases – confirmation bias, anchoring bias, and optimism bias, among others – leading to skewed predictions. Furthermore, reliance on historical data, while common, often proves inadequate when faced with novel events or ‘black swan’ occurrences – unpredictable events with extreme impact. The scarcity of comprehensive, real-time data across diverse fields exacerbates these issues, hindering the ability to accurately assess probabilities and anticipate future outcomes. Consequently, traditional approaches often struggle to provide reliable foresight, creating a need for alternative forecasting mechanisms that can overcome these inherent limitations.

Polymarket operates on the principle that aggregating the diverse perspectives of many individuals yields surprisingly accurate predictions about future events. This “wisdom of the crowd” is harnessed through a prediction market, where users speculate on the probability of outcomes by trading contracts linked to real-world occurrences – from political elections to scientific discoveries. Unlike traditional forecasting which often relies on expert opinion or static models, Polymarket’s market prices dynamically reflect the collective intelligence of its participants. Crucially, the platform incentivizes accurate predictions; traders who correctly anticipate outcomes profit from their insights, while those who misjudge events incur losses. This incentive structure fosters a constant refinement of probabilities, creating a responsive and often remarkably prescient forecast mechanism that transcends the limitations of individual analysis.

The efficacy of Polymarket as a forecasting tool is fundamentally reliant on a sophisticated data infrastructure. Capturing the continuous flow of trades, order book dynamics, and resulting probabilities requires high-throughput, low-latency systems capable of handling a significant volume of real-time information. Beyond simple data collection, effective analysis demands robust data pipelines for cleaning, transforming, and storing market signals. This allows researchers and analysts to identify predictive patterns, assess market sentiment, and ultimately, extract meaningful forecasts about real-world events. Furthermore, the platform’s data needs to be readily accessible through APIs and analytical tools, fostering innovation and enabling the development of increasingly accurate predictive models. Without this foundational data capability, Polymarket’s potential to harness collective intelligence remains unrealized.

Prediction markets differ from traditional financial markets by focusing on the valuation of future event outcomes rather than existing assets, resulting in dynamically updated prices based on ongoing trades.

Constructing the Data Foundation: From Markets to Insight

The Polymarket Data Pipeline functions as the core infrastructure for ingesting, transforming, and persisting all data generated by platform activity. This includes, but is not limited to, trade executions, order book updates, market state transitions, and user interactions. Data is collected from various sources, processed to ensure accuracy and consistency, and then stored in a structured format for subsequent analysis and retrieval. The pipeline is designed for high throughput and low latency to accommodate real-time market demands, supporting both historical data access and live data feeds for applications such as market monitoring, reporting, and algorithmic trading strategies. Scalability and fault tolerance are key design considerations, ensuring the pipeline can handle increasing data volumes and maintain data integrity even in the event of system failures.

The Polymarket data pipeline utilizes a relational database to manage the complex data generated by market activities. This database employs a structured schema to organize information including, but not limited to, market creation events, trade executions, and outcome resolutions. Data integrity is maintained through the enforcement of constraints and validation rules at the database level, preventing inconsistent or inaccurate data from being stored. Accessibility is ensured via standardized query interfaces – specifically SQL – enabling efficient retrieval of data for reporting, analysis, and the operation of the Polymarket platform. The relational structure facilitates complex joins and aggregations, allowing for the derivation of key performance indicators and market insights.

Synchronization Metadata within the Polymarket data pipeline serves as a comprehensive tracking system for data lineage and state. This metadata includes timestamps for each data transformation, unique identifiers for data sources and versions, and checksums to verify data integrity at each stage of processing. Specifically, it records the status of each record – whether it has been successfully ingested, processed, and stored – enabling the pipeline to identify and resolve inconsistencies or failures. The consistent application of this metadata allows for accurate data reconciliation, prevents data duplication, and facilitates auditing of the entire data lifecycle, ultimately guaranteeing the reliability of all downstream analyses and insights.

The Polymarket data system integrates off-chain and on-chain information throughout a market's lifecycle-from creation to settlement-to provide a structured database for analysis, empirical research, and application development. — The Polymarket data system integrates off-chain and on-chain information throughout a market’s lifecycle-from creation to settlement-to provide a structured database for analysis, empirical research, and application development.

Mapping the Full Lifecycle: Capturing Market Dynamics

The Full-Lifecycle Dataset provides a comprehensive record of Polymarket market activity from initiation to conclusion. This includes data pertaining to market creation, which details the initial parameters and question posed; all subsequent trading events, encompassing every order filled within the market’s lifespan; and ultimately, the resolution event, triggered by an oracle confirming the outcome of the market question. Capturing these three core phases – creation, trading, and resolution – ensures a complete and auditable history of each market, allowing for detailed analysis of market dynamics and participant behavior.

The Full-Lifecycle Dataset consolidates data from OrderFilled Events and Oracle Resolution events to provide a comprehensive and verifiable record of all activity within Polymarket markets. Currently, the dataset comprises records from over 770,880 distinct markets, capturing the complete transaction history from initial trading through final outcome determination. This integration allows for detailed analysis of market behavior, including price discovery, trade patterns, and the accuracy of oracle reporting, offering a full audit trail for each market’s lifecycle.

Market Metadata is a critical component of the Full-Lifecycle Dataset, providing necessary context for interpreting trading activity and oracle outcomes. This metadata details the specific parameters of each market question, including the precise wording of the question, the possible outcomes, and the associated market creator. The dataset currently supports this contextual information with a record of 943,548,464 trades and 1,988,150 oracle events, enabling comprehensive analysis of market dynamics and the factors influencing prediction market resolution. This linkage between metadata, trading data, and oracle reports facilitates a complete audit trail and allows for granular investigation of market behavior.

Analysis of Polymarket markets reveals that the most frequently discussed primary topics, as visualized by keyword frequency and trading volume <span class="katex-eq" data-katex-display="false"> (\log scale) </span>, consistently drive market activity. — Analysis of Polymarket markets reveals that the most frequently discussed primary topics, as visualized by keyword frequency and trading volume $(\log scale)$ , consistently drive market activity.

Anchoring Resolution in Trust: UMA and Blockchain Security

Polymarket achieves reliable outcome determination through its integration with the UMA Optimistic Oracle, a system designed to resolve real-world events on-chain without relying on centralized authorities. This oracle functions on a challenge-based mechanism: a proposed outcome is initially accepted as true, but anyone disputing it can stake tokens as a challenge. If the challenge is valid, the staker receives a reward, and the incorrect outcome is corrected; conversely, frivolous challenges result in the challenger losing their stake. This incentivizes accurate reporting and discourages manipulation, creating a decentralized and trustworthy process for resolving market outcomes. The UMA Optimistic Oracle, therefore, enables Polymarket to offer markets on a wide range of events with a high degree of confidence in the veracity of the results, fostering a more robust and transparent prediction market ecosystem.

The foundation of Polymarket’s reliable outcome resolution rests upon blockchain technology, offering a system built on inherent trust and security. Each market resolution event, and the data supporting it, is permanently recorded on a distributed ledger, creating an immutable audit trail accessible to anyone. This transparency eliminates concerns of hidden manipulation or retroactive alterations, fostering confidence in the process. Furthermore, blockchain enables the use of self-executing smart contracts, which automate the settlement of winning positions without the need for intermediaries. These contracts enforce pre-defined rules with absolute certainty, minimizing counterparty risk and ensuring that payouts are distributed fairly and efficiently according to the resolved outcome – a system drastically reducing the potential for disputes or fraudulent activity.

The architecture of Polymarket relies heavily on smart contracts to fundamentally reshape how predictions are managed and settled. These self-executing agreements automate critical functions – from securely holding user funds in custody to accurately representing positions within a given market – thereby eliminating the need for traditional intermediaries. Post-trade settlement, often a complex and delayed process, is streamlined and executed instantly upon outcome resolution. This automation drastically minimizes counterparty risk, as the terms of the agreement are immutable and enforced by the blockchain itself, ensuring that all parties adhere to the pre-defined conditions without reliance on trust or manual intervention. The result is a more efficient, transparent, and secure prediction market experience.

Daily activity on Polymarket, as indicated by transaction volume, active wallets, and market diversity, is normalized in this analysis.

Validating the Signal: Insights from NBA and CPI Data

Polymarket, a decentralized prediction market, exhibits a remarkable capacity for forecasting event outcomes, as demonstrated through its analysis of NBA game predictions. The platform aggregates and distills collective intelligence, effectively leveraging the wisdom of the crowd to generate accurate forecasts. By analyzing thousands of individual predictions on game results, Polymarket consistently outperforms traditional forecasting methods. This success isn’t merely correlational; the market’s ability to synthesize diverse perspectives into a single, predictive probability highlights a novel approach to event forecasting, suggesting its potential application extends far beyond the realm of sports and into areas requiring robust predictive analysis.

Polymarket, a platform leveraging prediction markets, demonstrates a surprising correlation with established economic indicators. A comparative analysis reveals its potential to serve as an alternative, and potentially leading, gauge of economic activity, specifically when benchmarked against the Cleveland Fed’s Nowcast – a real-time estimate of US GDP growth – and the Bureau of Labor Statistics’ (BLS) Consumer Price Index (CPI). This isn’t merely a coincidental alignment; the market’s collective predictions, formed through incentivized forecasting, appear to distill information and anticipate trends with notable accuracy. The implications suggest that aggregating predictions from informed individuals can generate valuable signals, offering a dynamic and responsive complement to traditional, survey-based economic reporting methods and potentially offering earlier insights into shifting economic conditions.

A rigorous calibration of market-implied probabilities, achieved through the application of Isotonic Regression to data spanning October 2020 to March 2026, reveals a remarkably high degree of accuracy in forecasting event outcomes. This statistical technique effectively adjusts the raw market probabilities, resulting in a Brier Score of 0.20339 – a metric used to assess the accuracy of probabilistic predictions. This score indicates strong calibration, meaning the market’s predicted probabilities align closely with observed frequencies, and suggests the platform generates reliable probabilistic forecasts. The methodology demonstrates a capacity to translate collective predictions into a well-calibrated probabilistic signal, furthering the potential of prediction markets as valuable tools for forecasting and decision-making.

Analysis of NBA market probabilities reveals broad calibration, as demonstrated by the close alignment between predicted probabilities and observed win rates with the <span class="katex-eq" data-katex-display="false">\\frac{45}{100}</span> benchmark. — Analysis of NBA market probabilities reveals broad calibration, as demonstrated by the close alignment between predicted probabilities and observed win rates with the $\\frac{45}{100}$ benchmark.

The construction of a robust dataset, as detailed within this study concerning Polymarket, necessitates a commitment to minimizing superfluous information. Tim Berners-Lee articulated this principle succinctly: “Data is just stuff. Structure is what gives it meaning.” The presented dataset isn’t merely a collection of on-chain transactions; it’s a carefully curated structure designed to reveal patterns within the prediction market lifecycle. This focus on essential data – streamlining the data pipeline for macroeconomic forecasting – reflects a dedication to clarity. Unnecessary complexity would obscure the signal amidst the noise, violating the core tenet that density of meaning is paramount. The utility of the dataset stems directly from this deliberate reduction, permitting focused analysis of market dynamics.

Where Do We Go From Here?

The construction of this Polymarket dataset-a complete accounting, as it were-reveals less a triumph of engineering than a necessary surrender to brute fact. They called it a data pipeline; it was, more accurately, a confession that prediction markets generate information at a rate exceeding most analysts’ capacity for comfortable abstraction. The immediate utility lies in offering a grounded substrate for macroeconomic modeling, yet the more interesting questions linger at the periphery.

Specifically, the challenge now isn’t more data, but a parsimony of interpretation. The temptation will be to layer complexity upon complexity, to seek predictive signals in ever-finer-grained market fluctuations. A more mature approach will involve identifying the minimal sufficient statistics-the core mechanisms-driving accurate forecasting. The blockchain offers a complete record; it does not, however, offer inherent wisdom.

Future work should prioritize not just predictive power, but also the limits of prediction. A complete accounting of forecast failures-the instances where collective intelligence falters-may prove more valuable than any string of successful calls. Ultimately, the dataset offers a mirror. It reflects not just the market, but the hubris of those who attempt to master it.

Original article: https://arxiv.org/pdf/2604.20421.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/