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Size an entire sports betting slate as one portfolio, using fractional Kelly and exposure limits.
About
Size an entire sports betting slate as one portfolio, using fractional Kelly and exposure limits.
Remote endpoints: streamable-http: https://api.kellyiq.com/mcp
Security Report
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
Endpoint verified · Requires authentication · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Found in Source Code
Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.
What You'll Need
Set these up before or after installing:
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"com-kellyiq-kellyiq": {
"url": "https://api.kellyiq.com/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
KellyIQ examples
Working examples for the KellyIQ API: size a whole sports betting slate as one portfolio from your own scripts, models, spreadsheets and AI assistants.
You send your bankroll and the bets you have chosen, with your view of each. KellyIQ removes the bookmaker's margin from each price, applies your view, and returns a dollar stake for every bet, sized across the slate together under per-bet and total-exposure limits.
Get a key
Create an API key in the KellyIQ app under Settings → API. Every plan includes API access, Free included, each with its own daily allowance. Then:
export KELLYIQ_API_KEY="kiqa_..."
Recipes
Each recipe is a complete script in Python (requests) and JavaScript (Node 18+, no packages).
| Recipe | What it shows |
|---|---|
first-optimize | Size a three-bet slate and read every part of the result |
slate-from-csv | Keep your slate in a spreadsheet: CSV in, CSV of stakes out (slate.csv) |
reproducible-results | Get exactly the same answer again with the returned seed |
handling-limits | Back off and retry on rate limits and a busy server |
cd recipes
python first-optimize.py # or: node first-optimize.mjs
Each recipe has a walkthrough in the docs: Recipes.
Python package
pip install kellyiq (kellyiq-python): the same API with
specific exceptions for each error and automatic retries when the server is busy.
| Example | What it shows |
|---|---|
python/size-a-slate.ipynb | A notebook: stakes, the probabilities behind them, and the risk at each Kelly fraction, as tables |
python/slate_from_csv.py | CSV in, CSV of stakes out, with each error handled (slate.csv) |
python/size_from_model.py | Your model's probabilities in, stakes merged back into the DataFrame (model_output.csv); needs pandas |
python/nba_home_teams.py | A real NBA slate (DraftKings prices, Oct 30, 2026) sized with every home team at the same assumed Moderate Edge (nba_2026-10-30.csv); needs pandas |
Guide: Python SDK.
Excel
spreadsheets/kellyiq-slate.xlsx: enter your bankroll and your
bets, press Ctrl+Alt+F5, and get a stake for every bet with a summary of the slate. No macros, and your API key stays in a
file on your computer, never in the workbook. Excel for Windows. Setup: spreadsheets/.
Benchmark
benchmarks/slate-sizing: three NBA slates sized by cap-then-scale, by
the exact constrained optimum and by KellyIQ, scored by exact expected log growth. The slates, the
scoring script and every stake are included so you can rerun it.
AI assistants (MCP)
KellyIQ runs a hosted MCP server at https://api.kellyiq.com/mcp, so your assistant can size a
slate as one of its tools. Claude connects by signing in; coding tools use an API key, with
ready-to-use configs in mcp/:
| Assistant | Setup |
|---|---|
| Claude (web, desktop, mobile) | Settings, Connectors, Add custom connector, paste the URL, then Connect and sign in |
| Claude Code | mcp/claude-code.md |
| Cursor | mcp/cursor.json |
| VS Code (GitHub Copilot) | mcp/vscode.json |
| Gemini CLI | mcp/gemini-cli.md |
Full guide: AI assistants (MCP).
Skills
skills/analyze-my-slate teaches the assistant to collect your
slate in one message, confirm before using an optimize, and explain the stakes and the risk.
Install steps for Claude and Claude Code: skills/.
Docs and support
- Quickstart, API reference, OpenAPI spec
- support@kellyiq.com (include the
requestIdfrom any error)
Keep your key out of code you commit or share; every example here reads it from the environment.
KellyIQ sizes the bets you choose; it does not choose bets or predict outcomes. Results are calculations, not betting advice. 21+, where legal.
The examples are MIT licensed. The KellyIQ optimizer itself runs as a hosted service.
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