Build with observed data

Call the API with cURL and the Python standard library: timeouts, retries on 503, bounded pagination and clear error handling.

Call the API from Node.js with built-in fetch: timeouts, retries, async pagination and a server route that keeps your key private.

What the Free plan covers: 500 requests per month, 20 per minute, the same captured datasets as paid plans, and where its limits appear.

Every response field for sportsbook, Pick6, DFS, statistics and context rows, with types, null behavior and how to read each one.

Three pagination styles with working loops: offset lists, the Pick6 next_after sequence and statistics cursors, plus their limits.

What each status means, what counts against your plan, when limits reset, and how to write retry logic that does not waste quota.

How to read observed_at, source_updated_at, age_seconds and stale, what worker targets mean, and how to label data honestly.

Signed Pick6 change webhooks on Production and a resumable stream on Developer: signature checks, retries, replay and limits.

Use native format=csv on Pick6 endpoints, and convert sportsbook, DFS and statistics JSON to CSV on your side with a working script.

Follow recorded Pick6 changes with a resumable cursor, read sportsbook history by sequence, and compute moves without overclaiming.

Post recorded Pick6 changes to a Discord channel with an incoming webhook: polling, cursors, quota math and message wording.

Load observed rows into Google Sheets with Apps Script and into Excel with Power Query, with the key kept out of cells and quota in check.

Three DraftKings numbers with three different meanings: American prices, standings multipliers and DFS salaries, and what each can support.

Which leagues each dataset configures, why the sport filter differs between sportsbook, Pick6, DFS and stats, and how to measure current coverage.

What running your own DraftKings scraper costs, what a stored API replaces and what it does not, and an adapter pattern for moving without a rewrite.

DFS contests and salaries are not the same data as pick'em lines. How the terms differ, what to test in a provider and what this API supplies.

What history each DraftKings dataset really holds, when recording began, what is not available, and how to start your own archive today.

Which inputs a prop model can read from this API, which it cannot, and how far player identity joins can be trusted across datasets.

Why there is no ready-made training set here, where point-in-time leakage comes from, and how to record your own as-of dataset from now on.

Build a chronological NBA points baseline from recorded box scores, score it on later games and compare with Pick6 targets you recorded yourself.

A walkthrough of a standard-library Python baseline: cursor pagination, numeric stat parsing, a SQLite cache and MAE on a later held-out window.

Query recorded NBA Pick6 rows, check capture age first, read More and Less selections and handle live, paused and alternative targets.

Filter recorded NFL Pick6 rows by exact stat label, encode spaces and plus signs, and tell first-quarter and first-half markets apart.

Pair NBA Points rows from Pick6 with ESPN player points from the statistics API, and see where the join and the history stop.

Read NFL Receiving Yards targets from Pick6 and receivingYards from ESPN box scores, with targets, receptions and part-of-game labels kept apart.

Read NHL Shots on Goal rows from Pick6 and shotsTotal from ESPN box scores, without letting the SOG label mislead the mapping.

Build an NBA player game log from retained ESPN box scores: season years, stat keys, cursor pagination and why you must sort rows yourself.

Build an NFL player game log from retained ESPN box scores: category rows, starting-year seasons, receiving and rushing keys and cursor pages.

For developers who want a response now: a keyless sample, then a free key, then an authenticated call. Not for production hardening or sportsbook player props.

Download Python and Node examples, a notebook, Postman templates, a daily archive workflow and a comparison feed. Bounded requests and no embedded credentials.

Connect Claude Code to DraftKings Pick6, sportsbook and DFS observations over MCP with an environment-backed key, and see what Claude web chat can reach.

Connect Gemini CLI to observed DraftKings data over MCP with httpUrl and an environment-backed header, and see how the Gemini app differs from the developer CLI.

Connect ChatGPT to DraftKings API documentation over MCP, understand why connectors cannot send an API key, and bring live observations in with REST or an Action.

Build a Lovable app on observed DraftKings data: the chat connector for build context, a server function with a project secret for runtime, and no key in the browser.

Sourced, dated comparisons of OpticOdds, ParlayAPI and DFS pick'em providers, plus a scraper migration path.