How to Judge Prediction Market Data Quality
Prediction market datasets can look orderly while hiding mismatched contracts, stale observations, missing markets, or ambiguous resolution states. A polished table is not evidence that the underlying records describe the same thing at the same time.
This guide explains a platform-neutral process for defining, collecting, testing, and documenting prediction market data. If you are still mapping the underlying market concepts, start with How Does Polymarket Work? before designing a dataset schema.
Why Prediction Market Dataset Quality Matters
Meaning errors survive clean formatting. A row may contain valid values but still refer to the wrong outcome, contract, market version, or resolution condition. Semantic checks must come before statistical analysis.
Missingness can create a false edge. If a collection process skips inactive markets, failed requests, thin books, or difficult-to-map records, a backtest may only see the easiest cases. Coverage needs to be measured against an expected universe.
Freshness changes interpretation. Prices, order books, status fields, and event metadata describe a source at particular moments. Without source and collection timestamps, comparisons can mix states that never existed together.
Reproducibility separates evidence from a screenshot. Raw inputs, transformation versions, and validation logs let another researcher reconstruct a result and locate where assumptions entered the pipeline.
A Six-Step Dataset Quality Workflow
1. Define the Research Question and Unit of Observation
State the decision the dataset must support, then define one row precisely. A row might represent a market snapshot, an outcome quote, an order-book level, a trade, or a final resolution; combining these units without explicit keys creates ambiguous analysis.
Best for. Write a short data contract that names required fields, allowed states, time semantics, and exclusions before collecting at scale. The contract gives later quality tests a concrete target.
2. Preserve Raw Records and Provenance
Save the source platform, retrieval method, source URL or official interface, source identifiers, retrieval time, and the unmodified response where permitted. Record collection failures and empty responses as events instead of silently omitting them.
Reality check. A normalized table cannot answer every future question. Retaining provenance and raw inputs is what allows a parser change, source correction, or disputed mapping to be investigated later.
3. Normalize Identifiers, Outcomes, and Time
Keep original identifiers as strings and create separate internal keys for markets, events, contracts, and outcomes. Normalize timestamps to UTC while preserving the original value and its meaning, and document how probabilities, prices, quantities, and missing values are represented.
What to look for. Test reused display names, reordered outcomes, multi-outcome markets, revised close times, duplicate identifiers across namespaces, and daylight-saving boundaries. The current prediction market API guide can help frame platform-specific mapping questions, but implementation details should still be checked against official documentation.
4. Measure Completeness, Uniqueness, and Freshness
Compare collected records with an expected market universe or a documented sequence of pages and checkpoints. Track missing intervals, duplicate keys, delayed updates, unmapped records, failed requests, and the age of each observation when it becomes available for analysis.
Limitation. A source catalog may itself change or omit records, so completeness is always relative to a stated reference and time. Publish the denominator, collection window, and unresolved gaps with any coverage score.
5. Validate Market State and Resolution
Separate open, closed, suspended, cancelled, disputed, and resolved states according to the source's current definitions. Preserve the market question, resolution criteria, referenced source, final outcome, and every observed correction rather than overwriting history.
Reality check. A final label is not enough for trustworthy outcome analysis. Researchers need to know which rule version and source supported that label, and whether the record was later corrected.
6. Version Transformations and Run Repeatable Tests
Assign versions to schemas, parsers, mapping tables, and quality rules. Build tests from saved examples that include normal records, missing fields, unexpected states, duplicates, revisions, and malformed values, then produce a machine-readable validation report for every dataset release.
Best for. Treat a dataset as publishable only when the same raw inputs and code version reproduce the same outputs, known exceptions are documented, and failed quality thresholds stop the release.
How to Evaluate a Prediction Market Dataset
Semantic accuracy. Sample records across market types and compare the question, outcomes, identifiers, status, timestamps, and resolution wording with the original source. Include changed and resolved markets, not only active examples.
Coverage. Report expected records, collected records, failures, exclusions, duplicates, and unmapped items by time period and source. A single overall percentage can hide serious gaps in one category.
Timeliness. Measure source-to-collection delay and collection-to-availability delay using distributions, not only averages. Match acceptable delay to the research question rather than claiming that one cadence is universally real time.
Reproducibility. Confirm that another run can locate the raw inputs, apply the recorded schema and transformation versions, and recreate the published outputs and quality report. Compare available workflows in the Polymarket analytics tools guide without assuming that a tool's interface proves the quality of its underlying data.
Limits and Risks to Understand
Source risk. Interfaces, schemas, historical coverage, identifiers, and status definitions can change. Monitor documented changes, fail visibly on unknown values, and preserve the source version or retrieval context when possible.
Selection and survivorship risk. Datasets that retain only available, active, popular, or successfully resolved markets can misrepresent past conditions. Record removals, failures, exclusions, and market-state transitions.
Timestamp and look-ahead risk. Using a corrected value or final label earlier than it was actually available can invalidate a backtest. Preserve observation times and model information availability explicitly.
Resolution risk. Contract wording, source revisions, disputes, and cancellations can make outcome labels more complex than yes or no. Keep the resolution evidence and uncertainty beside the label.
Decision risk. High data quality reduces avoidable errors but does not guarantee a sound forecast, favorable execution, sufficient liquidity, or profit. Research conclusions still depend on assumptions, methods, and risk controls.
Getting Started
- Choose one narrow research question and define exactly what one dataset row represents.
- Collect a small sample while preserving raw records, source identifiers, URLs, and UTC timestamps.
- Write a versioned schema and explicit mapping rules for markets, contracts, outcomes, states, and missing values.
- Create fixtures for duplicates, gaps, revisions, unknown states, reordered outcomes, and malformed records.
- Measure coverage and freshness against a documented source universe and collection window.
- Manually audit a stratified sample, including closed, changed, and difficult-to-map markets.
- Release the dataset only with its validation report, known limitations, transformation version, and reproducibility instructions.
FAQ
What makes a prediction market dataset high quality?
It accurately preserves market meaning, has measurable coverage and freshness, records provenance, handles state and resolution changes, and can be reproduced from saved inputs and versioned transformations. Fitness still depends on the question the dataset is meant to answer.
How do I detect missing prediction market data?
Compare collected identifiers and time intervals with a documented source universe, pagination sequence, or checkpoint history. Keep failed requests and unmapped records in the quality report so missingness is visible rather than silently discarded.
Can two prediction market datasets be merged by title?
Not safely. Similar titles can hide different outcomes, deadlines, rules, or resolution sources. Match records through explicit platform identifiers and documented semantic checks, and retain uncertain mappings as unresolved.
Does clean historical data make a backtest reliable?
No. Data quality is necessary but does not remove selection bias, look-ahead bias, model error, execution assumptions, fees, or liquidity constraints. A backtest should state both data limitations and methodological assumptions.