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Sports API

Odds data

How do you get historical odds data?

Historical odds data can support research, editorial analysis and testing, but archives differ significantly. A completed match result is not an archive of bookmaker prices. Before paying for history, define the competitions, markets, bookmakers and observation times your project requires, then test whether the available records answer that question.

Updated

Published · Updated · Sources checked

Past odds snapshots at different observation times link to a historical research dataset
A snapshot records what the feed observed at a time; it is not necessarily every individual price change.

Short answer

Use a provider with a documented historical endpoint or licensed archive. Confirm coverage dates, snapshot frequency, bookmaker and market availability, query costs and storage rights. Fetch representative samples and preserve their observation timestamps. Do not assume a live odds subscription includes complete past prices or every price movement.

Define the historical question precisely

Opening prices, closing prices and prices at a chosen point before kickoff are different datasets. Decide whether you need prematch or in-play records and how postponed events should be handled. List the exact markets and bookmakers. That brief makes a supplier's archive description useful instead of letting a broad claim of historical access hide missing periods or fields.

Sources: [1] The Odds API: Historical odds snapshots

Understand snapshots and their limitations

The Odds API describes historical snapshots rather than a complete tick-by-tick ledger. Its documentation states different archive start dates for featured and additional markets, with changes in snapshot frequency over time. Check the current page for the relevant period and product. A snapshot can miss short-lived movements between observations, which matters when testing strategies or quoting historical market conditions.

Sources: [1] The Odds API: Historical odds snapshots

Inspect sample responses before importing everything

Check event identifiers, scheduled times, bookmaker keys, market lines and returned snapshot timestamps. Determine how the service selects the available observation relative to the time requested. Missing records can reflect coverage rather than an error. Sportmonks also distinguishes older football history from odds retention, illustrating why a general historical-data add-on should not automatically be treated as an odds archive.

Sources: [2] The Odds API: API v4 documentation and quota costs, [4] Sportmonks: API FAQ, coverage, billing and troubleshooting

Avoid using information from the future

A research dataset should contain only information that would have been available at the decision time being tested. Joining final results or later corrections to earlier observations without tracking their availability can create look-ahead bias. Keep source timestamps alongside ingestion times. Our prediction guide explains why a retrospective result does not demonstrate an achievable future return.

Sources: [1] The Odds API: Historical odds snapshots

Budget for queries, storage and rights

Historical requests may have different access rules and credit costs from live endpoints. The Odds API documents historical access on paid plans; confirm current details before importing a large range. Check permitted retention and whether you may publish raw records. Read our commercial licensing guide rather than assuming research access grants redistribution rights.

Sources: [1] The Odds API: Historical odds snapshots, [3] The Odds API: Terms and conditions

Keep the archive reproducible

Save the request parameters, returned observation time and relevant product version with your permitted records. Explain gaps instead of silently filling missing odds with prices from another bookmaker. Keep original and normalised values distinguishable, and document any mapping decisions. The accuracy checklist offers further checks before a historical dataset is used in a published analysis.

For a small pilot, select a single competition and a short historical period. Record which requested observations exist, which bookmaker markets are missing and how many credits the import consumes. Compare the returned timestamps with the research question before scaling up. This pilot can expose an unsuitable archive much earlier than a large download, and it gives the supplier a precise example when clarification is needed. Check storage permissions separately.

Sources: [2] The Odds API: API v4 documentation and quota costs

Frequently asked questions

Does historical data include every price change?

Not necessarily. Snapshot archives sample the market at intervals. Ask explicitly if your project requires an event-by-event or tick-level history.

Are historical scores the same as historical odds?

No. Results record sporting outcomes; historical odds record market prices observed at particular times. You may need separate products and reliable event mapping.

Can I store historical data indefinitely?

Only if the applicable terms allow it. Retention permission and permission to resell raw records are different contractual questions.

Should I treat a missing snapshot as zero odds?

No. Zero is not a valid substitute for absent data. Preserve the gap and investigate coverage or query behaviour.

Sources

Checked . Documentation and terms change, so confirm details with the provider before you build.

  1. The Odds API: Historical odds snapshotshttps://the-odds-api.com/historical-odds-data/
  2. The Odds API: API v4 documentation and quota costshttps://the-odds-api.com/liveapi/guides/v4/
  3. The Odds API: Terms and conditionshttps://the-odds-api.com/terms-and-conditions.html
  4. Sportmonks: API FAQ, coverage, billing and troubleshootinghttps://docs.sportmonks.com/v3/api-faq

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