Polymarket Guide for Data Scientists: Building Probability Calibration Models from Historical Resolutions

A data scientist working with prediction market data faces a practical workflow problem: how to extract, validate, and backtest calibration models against thousands of historical market outcomes. Polymarket, as one of the world’s largest decentralized prediction markets, publishes resolved binary outcomes spanning geopolitics, sports, finance, and entertainment. The platform’s structure—where shares trade from $0 to $1 and resolve to either $1 (outcome occurred) or $0 (outcome did not occur)—creates a natural laboratory for testing whether market prices reflect true probabilities and whether historical resolution data can improve forecast accuracy.

The engineering challenge is not merely downloading a CSV file. It requires understanding market mechanics, resolution methodology, data quality constraints, and the regulatory landscape that determines which markets are accessible in different jurisdictions. A model trained on Polymarket historical data must account for how outcomes are actually determined, what information was available at prediction time, and whether the market price at close is a valid proxy for the true probability at settlement. These details matter because the difference between a 0.65 market price and a 0.70 market price can reflect genuine uncertainty, data delays, or illiquidity rather than disagreement about ground truth.

Understanding Polymarket’s market structure and data availability

Polymarket operates two distinct platforms: Polymarket US, which is CFTC-regulated and requires Know Your Customer (KYC) verification for US residents, and an international version restricted in 40+ jurisdictions including the United Kingdom, France, Germany, Italy, Netherlands, and Australia. This geographic fragmentation affects data availability and research scope. US users can access all markets on Polymarket US; international users operate under different liquidity and market availability constraints depending on their location.

Each market on Polymarket is a binary outcome contract: users buy YES or NO shares at prices between $0.00 and $1.00. A YES share pays $1.00 if the outcome occurs, $0.00 if it does not. A NO share behaves inversely. The market price of YES shares therefore represents the collective probability estimate of the crowd. If YES shares trade at $0.72, the implicit crowd probability is 72%. The fee structure—taker fees ranging from $1.00 to $1.75 per 100 shares depending on category, while makers pay zero—creates a spread between the true market-clearing price and the executed price seen by retail traders. This spread is a data quality issue: the closing price used for model training may not be the marginal probability-setter for large positions.

The platform uses pUSD, a Polygon-based stablecoin backed 1:1 by USDC, as its settlement currency. Understanding the pUSD-to-USD conversion and network fees is important for researchers working with transaction-level data. USDC and stablecoins more broadly maintain a 1:1 backing to fiat reserves, but the conversion between pUSD and other assets occurs at the network level and may incur slippage during periods of heavy trading or network congestion. For model builders, this means the final settlement value is pUSD, not USD, and any analysis comparing market prices to external data sources must account for conversion rates and timing.

Data scientists can access historical market data through several channels: the polymarket platform itself provides an API and historical order book, third-party data providers package cleaned event data, and community repositories sometimes publish parsed resolutions. The quality and completeness of these sources varies. Some repositories lag behind live markets; others may include corrected prices after disputes are resolved. For rigorous backtesting, researchers should verify their data against primary sources and understand the latency between market closure and final resolution.

Market resolution methodology and the UMA Optimistic Oracle

A prediction market is only as useful as its resolution process. Polymarket uses written rules for each market and the UMA Optimistic Oracle, which resolves outcomes through a token-holder dispute mechanism. Under this system, a resolution proposal is submitted, and if no one disputes it within a specified window, it is deemed correct. If a dispute occurs, UMA token holders vote on the correct resolution. This design differs fundamentally from centralized prediction markets where a platform operator determines outcomes.

For data scientists, the practical implication is that some resolutions are not instantaneous. A market may close, the initial resolution proposal may be submitted, and then a dispute may be raised. The time between initial proposal and final determination can span days or weeks. During this period, the market price may shift as traders reassess the probability of the dispute being upheld or the true outcome becoming clear. A dataset that only records the initial resolution without tracking disputes and corrections will miss important information about market behavior under uncertainty.

The written rules for each market specify what information determines the outcome. A geopolitical market might depend on an official government announcement, a news source, or a third-party data provider. A sports market might use official league statistics. The exactness of these rules matters: a market asking “Will the US unemployment rate fall below 3.8% in Q4 2024?” must specify which data source is authoritative (Bureau of Labor Statistics), which release is used (the monthly report or a revision), and which date is the cutoff (the last trading day before the release or the release date itself). Careless rule interpretation is a common source of disputes and a potential source of systematic bias in historical data.

When building a training dataset, researchers should audit the resolution rules for a representative sample of markets. Document cases where disputes occurred, when they were resolved in favor of the proposal versus the disputers, and how often rule ambiguity played a role. These audit results inform how much confidence to place in the historical outcomes. Markets with clear, externally verifiable outcomes (e.g., election results) are generally more reliable than markets with subjective or delayed data sources (e.g., CEO statements or political events subject to interpretation).

Data extraction, cleaning, and feature engineering for calibration models

Extracting historical Polymarket data begins with identifying the relevant API endpoint or data provider. The Polymarket API returns order book snapshots, historical fills, and market metadata including opening time, closing time, status, and resolution. For a backtesting study, researchers typically want the market creation date, closing price, final resolution, and time series of the mid-price (average of bid and ask) leading up to closure. The order book often contains sparse or illiquid pricing in early market life and periods of low trading activity, so data quality varies by market age and category.

A core cleaning step is handling gaps and outliers. Markets sometimes experience flash crashes, where the order book temporarily becomes unbalanced due to a large market order, network latency, or bot behavior. A price of $0.01 on YES shares followed by $0.50 one block later may reflect execution rather than true market belief. Researchers often apply a moving-average filter, remove trades executed at extreme prices if they represent tiny volumes, or exclude data from times of known platform issues. The specific rules depend on the research question: a study of market efficiency may retain all data to identify anomalies, while a study of crowd probability may filter aggressively to focus on deep liquidity.

Feature engineering for a probability calibration model typically includes market age, trading volume, number of unique traders, bid-ask spread, market category, historical accuracy of similar markets, and external features such as news sentiment or expert surveys. Each feature serves a different purpose. Market age can capture how refined crowd estimates become over time. Volume and trader count measure liquidity depth, which correlates with information efficiency. Bid-ask spread reflects uncertainty: wider spreads often signal lower confidence or higher disagreement. Market category (e.g., crypto, US politics, sports) may have systematic prediction accuracy differences.

A preprocessing step specific to Polymarket involves adjusting for the fee structure. The taker fee is embedded in the market price but not always explicitly shown to researchers. If a user buys YES shares at $0.72, they may actually pay $0.72 plus a per-share fee, reducing their effective exposure. For backtesting, researchers should either adjust all prices by the expected fee or create a separate fee vector. Ignoring fees creates a subtle bias: the historical market price is not the “true” crowd probability, but rather the crowd probability net of transaction costs.

Building and validating calibration models against Polymarket resolutions

A calibration model takes a market price (or a set of features derived from market state) and estimates the true probability that the outcome will occur. Ideally, among all markets where the model predicts 0.65 probability, approximately 65% should resolve YES and 35% should resolve NO. This is the central calibration constraint. A poorly calibrated model might systematically overestimate probabilities (predicting 0.75 when the true frequency is 0.60) or underestimate them (predicting 0.50 when the true frequency is 0.55).

Common model architectures include logistic regression (the baseline), random forests, gradient boosting, and neural networks. Logistic regression is interpretable and often performs surprisingly well: the coefficients show which features move the predicted probability in which direction. Random forests and boosting models can capture non-linear relationships, such as the possibility that prediction accuracy improves for markets with both moderate volume and recent trading but not for purely young or purely old markets. Neural networks are powerful but require more data to avoid overfitting and can be harder to debug if accuracy is poor.

The training process divides historical data into training and test sets. A common split is 70% training, 30% test; for time-series data, researchers often use a chronological split where earlier markets train the model and later markets test it. A chronological split is more realistic for a live trading system, which cannot use future data. The model is trained on the training set, hyperparameters are tuned, and then the model is evaluated on the held-out test set using calibration-specific metrics.

Calibration metrics include Brier score (mean squared difference between predicted probability and actual outcome), log loss (negative log-likelihood), and calibration plots (binning predictions into deciles and plotting predicted versus observed frequency). A Brier score near 0 is excellent; 0.25 is random guessing. Log loss penalizes high-confidence wrong predictions severely, so a model that confidently predicts YES at 0.90 when the market resolves NO incurs a large penalty. Calibration plots visually show whether the model is well-calibrated: a well-calibrated model’s line should be close to the 45-degree diagonal.

Accounting for market efficiency and information leakage

A critical pitfall in backtesting against Polymarket historical data is information leakage: using information at model evaluation time that would not have been available at prediction time. If a model is trained on market prices up to the close but tested on prices after disputes are resolved, the model has access to information (the disputed resolution outcome) that traders did not have. This produces inflated accuracy estimates.

More subtly, if a model uses external data such as election polls or economic forecasts, the researcher must ensure that the version of the data available at prediction time is used, not the final revised version. Election polls are released on specific dates; using the latest polls (released days or weeks after market close) would overstate how predictable the market outcome was. Economic data is often revised; using the latest GDP figure instead of the initial release inflates accuracy. Researchers should document exactly which version of external data is incorporated and when it became available.

Another consideration is selection bias in the markets analyzed. Polymarket has thousands of markets, but not all are equally liquid or have complete historical data. Markets that resolved quickly and cleanly (no disputes) are easier to study but may not represent the full market. Markets in obscure categories with few traders may have prices driven more by noise than by information. Researchers should report the criteria used to filter markets and acknowledge how this selection might bias results. A model trained exclusively on clean, liquid, major-category markets may not generalize to sparse or disputed markets.

Market efficiency also plays a role. If Polymarket prices are efficient—reflecting all available information—then no model should be able to consistently outpredict the market. If the model does, it may be overfitting, benefiting from data leakage, or exploiting genuine inefficiencies that have since disappeared. A model backtested on 2023 data may have found predictable patterns that were corrected by the time the model is deployed in 2024. Researchers should report the test set time period and acknowledge the risk that future market efficiency may be higher.

Handling perpetual futures and leverage in extended datasets

Polymarket also offers perpetual futures with up to 20x leverage, in addition to binary outcome markets. While perpetuals are useful for traders seeking exposure to ongoing price movements, they are less suitable for probability calibration research. Perpetuals do not have binary resolutions; their price reflects continuous supply and demand without a ground-truth outcome. Including perpetual prices in a dataset designed to validate calibration models introduces a category mismatch: the model expects a binary YES or NO resolution, but the perpetual market closes without one.

If a researcher includes perpetuals in an analysis, they should be treated as a separate category or explicitly excluded from the primary dataset. Documentation should specify which markets are binary outcomes and which are perpetuals. Some analyses may be interested in whether binary market prices and perpetual prices converge on similar probability estimates as resolution approaches, but this is a different research question and requires different methodology.

Leverage also affects the data interpretation. A trader using 10x leverage on a $0.60 probability outcome is making a directional bet with amplified exposure. This can shift market prices in ways that do not reflect crowd probability but rather reflect leveraged trader behavior, margin calls, or funding rates. For a study focused on the crowd’s true probability estimate, researchers may want to adjust for estimated leverage-driven trading or filter out periods of high leverage activity. The challenge is that leverage usage is often not directly observable in public market data; researchers must infer it from price movement patterns or trading volume anomalies.

Regulatory and data access constraints by jurisdiction

Access to Polymarket data depends on geography. The CFTC regulates Polymarket US as a derivatives exchange, requiring KYC for US users, which means US-based researchers can access the full US market dataset after completing identity verification. International researchers in unrestricted countries can access Polymarket International, but data from restricted jurisdictions may not be available to users in the UK, France, Germany, Italy, Netherlands, or Australia.

The UK’s Financial Conduct Authority (FCA) restricts prediction markets, and the European Securities and Markets Authority (ESMA) has positioned prediction markets as crypto-asset derivatives requiring strict authorization. Individual countries add their own restrictions: Germany’s BaFin and Italy’s CONSOB have both issued guidance restricting or prohibiting prediction market trading. The Dutch AFM similarly restricts cryptocurrency prediction markets. These regulatory boundaries affect which markets exist and which historical datasets are publicly available without geographic restrictions.

For researchers in restricted jurisdictions, accessing Polymarket data may require workarounds such as VPNs or using third-party data aggregators that have already collected and republished the data. However, this introduces legal and ethical complexity. A researcher should consult with their institution’s legal or compliance team before implementing workarounds. Published academic datasets and historical datasets from established crypto data providers may offer a more straightforward path.

Practical workflow: From raw data to validated model

A concrete workflow begins with defining the research scope: which market categories to include, which time period, which prediction targets, and which external data sources. For example, a researcher might focus on US politics markets from 2023 through mid-2024, predict whether the market price at 30 days before close is predictive of final resolution, and incorporate polling data from published surveys.

Step two is data collection. Download the historical order book, fills, and market metadata from Polymarket’s API or a data provider. Record the mid-price (or closing price, depending on the research goal) at regular intervals (hourly, daily, at market close) and the final resolution. Document any gaps, disputed resolutions, or corrected outcomes. Step three is cleaning: remove duplicates, handle missing values, outlier-filter extreme prices, and align external data timestamps to market times.

Step four is feature engineering. For each market, compute features such as market age at evaluation, trading volume in the last 7 days, bid-ask spread, historical accuracy of the same market category, and any relevant external features (polling average, economic data release, expert forecast). Step five is model training: split data chronologically, train a calibration model on the training set, tune hyperparameters using cross-validation or a validation set, and evaluate on the test set using Brier score, log loss, and calibration plots.

Step six is error analysis. Which markets did the model mispredict most badly? Were they in any particular category, time period, or liquidity regime? Did disputes affect resolution uncertainty? Are there systematic biases (e.g., overconfidence on close elections)? Step seven is documentation and reproducibility: save the code, data processing scripts, train/test splits (by market ID and date), model hyperparameters, and results metrics. This allows future researchers to replicate the study, extend it, or compare different approaches.