How do you use an odds API?

Authenticate, fetch and cache the structure of available sports and markets, then request the markets you need on a polling schedule matched to how fast they move. Store every response as an observation with the time you received it rather

How often should you poll?

As a function of time to event rather than a fixed interval. Prices days out move slowly; prices in the final hour move constantly. A uniform interval either wastes requests early or misses movement late, and rate limits usually bind before

What should you do when a market is suspended?

Record it as a state rather than treating it as an error and retrying through it. Suspension, removal, re-listing, line changes, voids and empty prices are all normal states, and the fact that a market suspended at a particular time is itse

What is the hardest part of an odds integration?

Identifier reconciliation. Every source names teams, players, competitions and markets differently and none of it is stable. You need canonical entities with source-specific aliases and a review path for unmatched items, because fuzzy match

How to Use an Odds API

Authenticate, request the markets you need on a schedule matched to how quickly they move, and store every response with the time you received it. The difficulty is not fetching. It is handling markets that suspend, prices that go stale, identifiers that change, and a rate limit that binds before your coverage does.

The shape of the integration

Authenticate and discover. Most interfaces expose the available sports, competitions and market types before you request prices. Fetch that structure and cache it; it changes slowly and requesting it repeatedly wastes the budget you need elsewhere.

Request the markets you actually need. Requesting everything is the instinct and it exhausts a rate limit quickly while producing data nobody uses. Decide what you cover before you write the loop.

Poll on a schedule matched to movement. Prices for an event days away move slowly. Prices in the hour before it starts move constantly. A uniform interval either wastes requests early or misses movement late, so the interval should be a function of time to event.

Store every response. This is the decision that determines whether you have a dataset or a cache. A system that overwrites current prices can answer what the price is now; a system that appends observations can answer what it was at any point, which is what every subsequent analysis requires.

Record receipt time, not just any timestamp in the payload. The provider's timestamp tells you when they say the price applied. Your receipt time tells you when you could have acted on it. Those differ, sometimes substantially, and evaluation work needs the second.

The states that are not errors

A market's life is not open then settled, and treating anything else as a failure produces gaps in the data at exactly the interesting moments.

Suspended. Trading paused, usually because something is happening. Prices may be absent or stale. This is a state to record, not an error to retry through, and the fact that a market suspended at a particular time is itself information.

Removed and re-listed. Markets disappear and come back, sometimes with a different identifier. A pipeline that keys purely on identifier will treat the return as a new market and lose continuity.

Changed line. The market you were tracking becomes a different market when a handicap or total moves. Whether that is the same market with a new value or a new market entirely is a modelling decision you must make explicitly, and it changes what any historical series means.

Void and settled. Different outcomes with different implications for anything computed from them.

Empty. No price available for a listed outcome, which is distinct from a price of zero and must not be stored as one.

Each of these should be a recorded state in your schema. Collapsing them into missing data loses the information that the state itself carried.

Rate limits shape your coverage

You will almost always hit a request budget before you run out of markets you would like to watch. That makes sampling a design decision rather than an implementation detail: which markets get frequent polling, which get occasional, and what triggers a temporary increase. Deciding this deliberately produces defensible coverage; discovering it through throttling produces gaps that correlate with exactly the busy periods you cared about.

The part that actually costs time

Identifier reconciliation. Every source names teams, players, competitions and markets differently, and none of the naming is stable.

The same club appears with and without a suffix. A player is a full name in one feed and an abbreviation in another. Competitions get renamed between seasons. Market types that look equivalent have different settlement rules underneath.

So any system reading more than one source needs a mapping layer, and that layer is where the engineering effort concentrates. The practical approach is a canonical entity per team, player and competition, with source-specific aliases attached, plus an explicit review path for anything unmatched. Fuzzy matching alone silently mismaps, and a mismap is worse than a gap because it produces plausible wrong data rather than obviously missing data.

Budget for this properly. Teams consistently estimate the integration as the API work and discover that the API work took a week and reconciliation took the rest of the quarter.

And version the mapping. When an alias is corrected, historical data joined under the old mapping is now inconsistent, so knowing which mapping version produced a given join is what makes historical analysis reproducible.

Frequently asked questions

How do you use an odds API?: Authenticate, fetch and cache the structure of available sports and markets, then request the markets you need on a polling schedule matched to how fast they move. Store every response as an observation with the time you received it rather than overwriting current state.
How often should you poll?: As a function of time to event rather than a fixed interval. Prices days out move slowly; prices in the final hour move constantly. A uniform interval either wastes requests early or misses movement late, and rate limits usually bind before your desired coverage does.
What should you do when a market is suspended?: Record it as a state rather than treating it as an error and retrying through it. Suspension, removal, re-listing, line changes, voids and empty prices are all normal states, and the fact that a market suspended at a particular time is itself information worth keeping.
What is the hardest part of an odds integration?: Identifier reconciliation. Every source names teams, players, competitions and markets differently and none of it is stable. You need canonical entities with source-specific aliases and a review path for unmatched items, because fuzzy matching alone produces plausible wrong data.