A sports betting API is a programmatic way to read the prices that sportsbooks, exchanges and pick'em apps are offering, along with the games those prices belong to. You send an HTTP request, you get JSON back, and your code decides what to do with it: show it, compare it, store it or alert on it.
That's the short answer. The useful one is about what's inside the response and what you can build with it. Some APIs stop at raw prices. Others also return the arithmetic people usually build on top, such as fair prices, +EV rows and arbitrage pairs. Knowing which you need saves you weeks.
What a sports betting API returns
Most of what you'll read falls into a handful of groups. Here's how they map onto the endpoints in ZippyOddsAPI, which is a useful reference shape even if you end up elsewhere.
| Group | What it is | Example endpoint |
|---|---|---|
| Catalog | Leagues, market keys, books, teams, players | /v1/leagues, /v1/markets, /v1/sportsbooks |
| Events | One row per real game, joined across sources | /v1/events |
| Prices | Each book's markets and outcomes for those games | /v1/odds, /v1/props, /v1/futures |
| Scores | Status and score for games in progress | /v1/scores |
| History | A market's prices over time | /v1/history |
| Computed | Rows worked out from the prices | /v1/ev, /v1/arbitrage, /v1/middles, /v1/holds |
The first four are raw data. The last group is where a betting api starts doing work for you. A computed row is only as good as the method behind it, so read how it's worked out before you trust it. Our docs publish the full method, including which books set the fair price and when a price counts as too old.
A first request
Every call carries the key in a header. Here's a request for the main markets in the NBA, in decimal odds:
curl -s "https://zippyoddsapi.com/v1/odds?league=nba&format=decimal" \
-H "Authorization: Bearer $ZIPPYODDS_KEY"Each row is an event with a bookmakers list. Each bookmaker has markets, and each market has outcomes with a side, a point where there's a line, and a price. Every success comes back as { data, meta }, and meta.credits_used tells you what the call cost. The sportsbook API integration guide covers the engineering around a call like this: retries, rate limits and mapping teams.
Things people build on a sports betting API
The same data supports very different products. These are the common ones, roughly in order of how much they ask of the data.
An odds screen
One table, every book's price for each side, best price highlighted. It looks simple, and the hard part is freshness: a best price that's 20 minutes old is a trap. Your screen has to know how old each price is, which is why our guide to the live odds API spends most of its time on timestamps.
A +EV alert
Compare each price with a fair price built from sharp books with their margin removed, then alert when a price beats it by some threshold. You can build the fair price yourself, or read it. In ZippyOddsAPI, /v1/ev?league=nba&min_ev=2 returns rows at least 2% above fair value, each with fair_probability, ev_percent and the references that set it.
An arbitrage scanner
Two books, opposite sides, prices that together come to less than 100% implied. Say one book has the home side at -3.5 for +105 (decimal 2.05) and another has the away side at +3.5 for +100 (decimal 2.00):
- Implied chances: 1 / 2.05 = 48.78%, and 1 / 2.00 = 50.00%. Together, 98.78%.
- Margin: 1 / 0.987805 - 1 = 1.23% on the total stake.
- Stake split: 48.78 / 98.78 = 49.4% on the home leg, 50.6% on the away leg.
That 1.23% only exists if both legs fill at those prices. Arbitrage has execution risk: a price can move or a stake can be cut between the first bet and the second.
A model and its backtest
If you're building a model, you need a market price to compare it with. A betting data api gives you that price at the time it was observed, and price history lets you check whether your picks beat the closing line. Store what you read with its timestamps. A backtest built on prices without their real age flatters itself.
A tracker that measures closing line value
Record the price you took, then read the last price before the start. Whether you consistently beat that closing price tells you more about a method than a month of results.
Raw prices or computed rows
You can get by with raw prices and do the maths yourself. Removing the margin from a two-way market takes a few lines of code. Doing it well across many books takes more care: which books count as sharp, how to treat books that share one odds platform, what to do when a reference is stale, and when an edge is too large to believe.
If you'd rather not own that, use a sports betting data api that returns computed rows and documents how it gets them. If you would, take raw prices and treat the computed endpoints as a check on your own numbers. Either way, a row that says 5% EV is an estimate at the moment the prices were read, not a promise of return.
What to check before you build
Before you write much code against any provider, check:
- Where the prices come from. Read from each operator's own public data, or bought from a supplier? ZippyOddsAPI reads prices from operators' own public data. Some books are also seen through public publishers' snapshots; those copies are labelled raw-only and never scored.
- Whether every price carries the time it was observed. If it doesn't, you can't tell a live price from a stale one.
- Which books are feeding now, as opposed to which are on a list. Our coverage page shows the current state, and a book on the list isn't a promise of prices.
- How requests are charged. In ZippyOddsAPI a request costs a fixed 0 to 3 credits whatever the size of the answer, and refused requests cost nothing.
- What happens when you hit a limit. A clear 429 with a
Retry-Afterheader is easy to handle. Silent truncation isn't.
For a wider look at the price data itself, the sports odds API guide compares formats and markets, and what is a sportsbook API covers the difference between a book's own interface and an aggregator. The docs list every endpoint, field and error, and explain how to get a key.