analytics

sports-betting: AI-driven insights for smarter betting analytics

Name: sports-betting: AI-driven insights for smarter betting analytics
Author: georgedouzas

A Python suite of machine‑learning tools for sports betting analytics, perfect for founders creating data‑powered betting products.

From the repo · “Collection of sports betting AI tools.”

822 stars+7★ this week152 forksPythonGuide quality 8/10 · reviewed Oct 12Updated 9/24/2026100% free · open source

What it does

This tool provides a collection of sports betting AI tools to analyze and predict sports outcomes using Python

When to use it

•You need to analyze historical sports data to identify trends and patterns
•You want to build a predictive model to forecast sports game outcomes
•You are looking for a customizable framework to integrate with your existing sports betting platform

Ready-to-paste prompt

python sports_betting.py --league NBA --team Lakers --opponent Celtics

Heads up: Make sure you have Python 3.8 or later installed, as the tool uses libraries that are not compatible with earlier versions

Saves to your device

Use with Claude

New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install

Claude Code

mkdir -p ~/.claude/skills/sports-betting && curl -fsSL https://workflowstacks.com/api/skills/sports-betting/claude-skill -o ~/.claude/skills/sports-betting/SKILL.md

Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)

claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How sports-betting: AI-driven insights for smarter betting analytics works

Codeflow

Free to inspect

Sports-betting: AI-driven insights for smarter betting analytics is a medium Python project (~11k lines across 78 code files, plus 45 test files). Expect some technical setup — comfortable with a terminal, or ask a developer. Last commit a month ago, MIT license, has a test suite.

Size

Medium codebase

~11k lines · 78 code files · ~2 h to skim

Setup

Some technical setup

Comfortable with a terminal? 20–40 min. Otherwise ask a dev.

Runs on

Python

No API keys detected

Python 91%Shell 9%

Where to start reading

1
README.md
Start here — what it does and how to install it
2
pyproject.toml
Dependencies and the commands it exposes
3
src/sportsbet/__init__.py
Inside src/ — the main logic begins here

What's in each folder

src/Core code — the actual logic58 files

docs/Documentation44 files

specs/Tests — proof it works79 files

tests/Tests — proof it works49 files

.specify/Tool / agent settings18 files

.claude/Editor / agent settings10 files

.github/CI / automation (GitHub Actions)10 files

READMEHas testsDocumentedCI checksMIT licenseUpdated this month