Agent builders
How to evaluate a sports betting data or intelligence API before you build an agent on it
A checklist you can run in one afternoon against any provider, including ours: what it returns, what you are allowed to do with it, how fresh it is, whether it can be audited, and how to measure it without trusting a headline number.
By DeepChamp research team · Updated
Short answer
Judge a sports betting data or intelligence API on eight checks before you build an agent on it: what it actually returns, whether its licence lets you use the output, how fresh each field is, whether every signal is logged with a timestamp and a version, how you will measure it against the closing line, whether the schema is stable and agent-readable, what a useful call costs, and what the provider refuses to claim.
The eight checks
1. Scope: raw data or derived intelligence
Raw feeds return prices, scores and statistics, and your agent must rank, dedupe and reason over them. Intelligence layers return derived context such as ranked news, briefs and scores, which saves that work but makes you depend on their method. Decide which you need before you compare anything else.
2. Licence: what you may do with the output
Read the terms for displaying, storing, redistributing and feeding the output to a model. Many odds and statistics feeds restrict resale and redisplay. If you plan to build on derived analysis, ask the provider in writing whether that is allowed, and keep the answer.
3. Freshness: a timestamp on every response
An agent can only refuse stale input if the response says when it was current. Look for an as-of time and a freshness figure in every response, not just in the documentation, and test it during a busy injury-report window.
4. Audit trail: stable ids, hashes and versions
A signal you cannot identify later cannot be graded honestly. Look for a stable id, a content hash, an issue time and a model version on every signal, so you can prove what was published, when, by which version.
5. Measurement: closing line value, not a headline
Do not rely on a win-rate headline. Log each signal, read the closing market from your own licensed feed, and compute closing line value over a sample. A provider whose output cannot be measured this way gives you nothing to improve against.
6. Agent-readability: schema, docs and discovery
Prefer typed enums, an OpenAPI file, an llms.txt, an MCP server with described tools and error codes that say whether to retry. The real test: can a fresh agent complete a task from the documentation alone, with no human help?
7. Cost per useful call
Count the calls an agent needs for one task, not the price of one call. Check for a free tier you can prototype on, idempotent retries, whether failed calls are billed, and whether an agent can pay without a human creating an account.
8. Honest limits
A trustworthy provider says what it does not know and does not claim. Guaranteed outcomes, “locks”, unverifiable win rates, no age gate and no problem-gambling information are reasons to walk away.
A one-afternoon test plan
- 1Read the terms and write down, in one sentence each, what you may store, display, redistribute and train on. If any answer is unclear, email the provider and wait for a written reply before you build.
- 2Call the same endpoint twice a minute apart and during a busy window. Check that the as-of time changes, that cached responses say they are cached, and that errors say whether to retry.
- 3Pull ten signals and check that each has a stable id and an issue time. Pull them again the next day and confirm the ones you already hold are byte-identical, or that the provider tells you which changed and why.
- 4Log every signal with the entry price from your own licensed feed. At the start time, record the closing price from the same feed. Compute closing line value for each signal.
- 5Point a fresh agent at the documentation, with no other help, and ask it to complete one task. Note where it gets stuck: those are the gaps in the docs.
- 6Run a day of realistic calls and total the cost. Include retries and the calls an agent makes while it decides which endpoint to use.
- 7Read the footer of every page and response for the age gate, the problem-gambling helpline and any claim about results.
Red flags
- A win rate or return figure with no sample size, no list of all graded picks and no definition of how a pick is graded.
- Picks that can be edited after the game with no record of the change.
- Language such as “lock”, “guaranteed” or “can’t lose”.
- No timestamp on any response, or a documentation page that says “real time” without a measured freshness figure.
- Output that repeats sportsbook prices you did not license, or a scraper dressed as an API.
- No age gate, no problem-gambling information, no statement that the output is not betting advice.
How DeepChamp for Agents answers the eight checks
DeepChamp for Agents is a sports-betting research intelligence layer: ranked, deduplicated news, matchup and prop briefs, edge scores and a research agent called Jev. This table is the vendor’s own answer, so test it rather than trusting it. The free daily slate and matchup context are live today; the lines marked “specified” are described in the published contract and are not yet on in production.
| Check | Answer | Status |
|---|---|---|
| Scope | Derived research intelligence. No response contains odds, prices, price comparisons, box scores or stat lines, and no field exists for them. Pair it with a licensed odds feed if your agent needs prices. | Live (slate); specified (other lines) |
| Licence | Outputs are our own derived work. Raw data from third parties is never returned. The DeepChamp Terms govern use; separate API terms will be published before any paid line goes live. | Live (Terms); API terms pending |
| Freshness | Every response carries an as-of time and a data-freshness figure in its meta block. | Live |
| Audit trail | Each signal carries a signal id, a content hash, an issue time and a model version. Issued signals are appended to a hash-chained ledger. | Specified |
| Measurement | Signals carry a market-close reference time, never a price, so your agent reads its own closing market and computes its own closing line value. | Specified |
| Agent-readability | OpenAPI at https://deepchampai.com/openapi.json, llms.txt, an MCP server, an A2A agent card and an installable skill. | Live |
| Cost | The slate and matchup context are free with no signup, rate limited per IP. Paid lines, when they go live, are priced per call and a failed call is not charged. | Free tier live; paid lines off |
| Honest limits | No win rate, record, return or results field exists anywhere in the API. Every response relays the 21+ notice and 1-800-MY-RESET. | Live |
Common questions
How do I evaluate a sports betting API for an AI agent?
Check eight things: what it returns, its licence terms, the freshness of each response, whether signals carry ids, hashes and versions, how you will measure it against the closing line, how readable it is for agents, what a useful call costs, and what claims it refuses to make. Then log its output and measure closing line value over a sample before you rely on it.
What is the difference between a sports data API and a sports intelligence API?
A data API returns raw facts such as prices, scores and statistics. An intelligence API returns derived analysis such as ranked news, written briefs, probability estimates and scores. A data API leaves the reasoning to your agent; an intelligence API supplies some of it and asks you to trust and verify its method.
How can I tell whether an AI betting signal is any good?
Do not use a win-rate headline. Record the signal and the price available when it was issued, read the closing price from your own feed, and compute closing line value over a large sample, reporting the sample size and an interval. Closing line value measures price quality rather than results, so treat it as a warning light, not a guarantee.
Does DeepChamp for Agents publish a win rate?
No. DeepChamp for Agents publishes no win rate, record, return or profit figure, and the API has no field for them. It returns research signals with a probability estimate and an edge score, which are estimates and not predictions of outcomes.
This page is published by DeepChamp AI, so read it as one vendor’s view. Examples use hypothetical numbers and are not live markets, picks or claims about performance.
21+ for U.S. sports betting. Follow the laws and age requirements where you are. DeepChamp AI provides independent sports betting research and analysis. It does not accept or place wagers, hold customer funds for betting, or operate a sportsbook. No wins or profits are guaranteed. If you or someone you know has a gambling problem, call or text 1-800-MY-RESET or visit the National Council on Problem Gambling.