Shipp API

Shipp.ai is the definitive source on real-time data.

Create an API Key

Reference docs.

Implement as many tests as possible as soon as possible. Verify data and how the app uses data. Be flexible in schema — event rows are schema-flexible JSON and fields vary by sport, feed, and event.

Base prefix:

• All routes are under: https://api.shipp.ai/api/v1

Documentation & References

All detailed examples, request/response schemas, and walkthroughs live in the official docs. Always consult these before building:

Authentication

All endpoints require an API key. The API supports several ways to provide it:

Pick whichever method works best for your client.

Endpoints Overview

Below is a summary of the available endpoints. For full request/response examples, schemas, and field descriptions see the API reference.

POST /api/v1/connections/create

Create a new raw-data connection by providing natural-language filter_instructions that describe what games, teams, sports, or events you want to track.

Returns a connection_id (ULID) you’ll reuse for all subsequent runs.

→ Full docs & examples

POST /api/v1/connections/{connectionId}

Run a connection and receive raw event data.

Supports optional body fields for time-based filtering (since), cursor-based pagination (since_event_id), and result limiting (limit).

→ Full docs & examples

GET /api/v1/connections

List all connections in the current org scope.

→ Full docs & examples

GET /api/v1/sports/{sport}/schedule

Retrieve upcoming and recent games for a given sport (past 24 hours through next 7 days).

Supported sport values: nba, nfl, mlb, ncaafb, soccer (case-insensitive).

→ Full docs & examples

Data Shape

Event rows returned in data[] are schema-flexible JSON objects. Fields vary by sport, feed, and event. Common field categories include:

• IDs: game_id, home_id, away_id, attribution_id, posession_id
• Text / enums: sport, home_name, away_name, game_clock, desc, type, category
• Numeric: home_points, away_points, game_period, down, yards_first_down, location_yard_line
• Time: wall_clock_start, wall_clock_end

Not every row has every field. Agents and clients should be defensive and handle missing keys.

For the complete field reference see docs.shipp.ai.

Error Format

Errors are returned as JSON with an error message, HTTP status code, and a hint:

Response Compression

Include an Accept-Encoding header to receive compressed responses (zstd, gzip, or deflate). Compression is applied automatically when the response body exceeds 1 KB.

Usage Tips

• Keep filter_instructions short, explicit, and testable. Mention the sport/league and scope.
• Store and reuse connection_id — don’t create a new connection per run. Creating connections has time and credit overhead.
• Use since_event_id for efficient polling (cursor-based pagination). Send the lexicographically largest event ID you’ve received.
• Use the schedule endpoint to discover game_ids and team names before creating connections.
• Surface error hint messages directly to users when limits are hit.
• Treat every field in data[] as optional — use existence checks and graceful fallbacks.
• Log unknown data shapes in development so you can refine filter_instructions.
• Polling interval: 5–30 seconds depending on domain and cost sensitivity.
• Deduplicate events by keeping a rolling set of seen event IDs.
• Consult the how-to guides for end-to-end integration walkthroughs.

Integration Pattern: “Real-Time Contract”

When building a feature powered by Shipp, define a contract for each screen or agent loop:

1. User promise — what must stay accurate while the user (or agent) is watching?
2. Triggering events — what changes should cause UI updates or agent actions?
3. Data shape needed — what fields do you need to render or decide?
4. Freshness window — how old can the last update be before you warn the user?
5. Failure mode — what do you show/do if updates stop arriving?

Then translate the contract into filter_instructions when creating the connection.

Example Project: Alph Bot

Alph Bot is an open-source automated trading bot that uses Shipp for real-time sports data and trades on prediction markets. It demonstrates a production-quality integration of the Shipp API.

How Alph Bot Uses Shipp

1. Game discovery — Uses GET /api/v1/sports/{sport}/schedule to list available games:

2. Connection creation — Creates a Shipp connection for a specific game, describing what events to track via filter_instructions.

3. Live polling — Runs the connection on an interval, using since_event_id for cursor-based pagination to receive only new events since the last poll.

4. AI analysis — Feeds the schema-flexible data[] events directly to Claude AI, which analyzes game state and estimates outcome probabilities.

5. Trading decisions — Compares AI-estimated probabilities against Kalshi market prices to find value bets.

Key Takeaways from Alph Bot

• Reuse connections — Alph Bot creates a connection once and polls it repeatedly, avoiding unnecessary creation overhead.
• Cursor pagination — Uses since_event_id so each poll only returns new events, keeping responses fast and costs low.
• Defensive data handling — Treats all fields as optional since the data shape varies by sport, game phase, and event type.
• Schedule-first workflow — Discovers game IDs via the schedule endpoint before creating targeted connections.

See the Alph Bot README for full setup instructions.

Versioning

This API is versioned under /api/v1/. New versions will be introduced under a new prefix when breaking changes are required.