alex-rimerman

alex-rimerman / statcast-mcp

Public

This repository is public, allowing users to pull down the Developing Baseball's Statcast MCP.

22
0
100% credibility
Found Mar 14, 2026 at 22 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository provides a server that enables AI assistants to access and query detailed MLB Statcast baseball data, such as player stats, pitch-level details, and leaderboards, using natural language.

How It Works

1
🔍 Discover baseball stats magic

You find a handy tool that lets your AI chat buddy fetch real MLB player stats, game details, and leaderboards just by chatting in plain English.

2
📥 Pick up the stats helper

Grab and set up this simple baseball stats companion on your computer in moments.

3
🔗 Hook it to your AI friend

Whisper to your AI app like Claude to team up with the stats helper through a quick settings tweak.

4
💬 Chat about your favorite players

Ask natural questions like 'How did Aaron Judge hit last month?' or 'Who runs the fastest?' right in your AI conversation.

5
📊 See stats tables appear

Instantly get neat tables with pitch breakdowns, leaderboards, exit speeds, and more popping up in the chat.

🏆 Unlock pro-level baseball smarts

Now dive deep into games, players, and trends effortlessly, sharing cool insights with fellow fans.

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Star Growth

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AI-Generated Review

What is statcast-mcp?

This Python MCP server turns MLB Statcast data into a natural language playground for AI assistants, letting you query pitch-by-pitch stats, player arsenals, exit velocities, and leaderboards with plain English prompts like "Aaron Judge's exit velo in 2024." Built on pybaseball and sources like Baseball Savant and FanGraphs, it plugs into tools like Claude Desktop, Cursor, or VS Code Copilot—no coding needed. As a public repository for data on GitHub, it allows easy pulls for baseball's deepest metrics from 2008 onward.

Why is it gaining traction?

It stands out by bridging AI chat interfaces with granular Statcast tools, auto-resolving player names and handling rate limits, unlike raw APIs requiring GitHub tokens or custom scrapers. Developers love the quick PyPI install (`pip install statcast-mcp`) and config snippets for repository GitHub VSCode or Cursor setups, skipping boilerplate for instant leaderboards or game recaps. The hook: real-time sabermetrics in your IDE, perfect for repository public GitHub workflows without switching to private repos.

Who should use this?

Sabermetrics hobbyists querying undervalued hitters via xBA diffs, fantasy baseball managers scouting sprint speed leaders, or sports data journalists pulling Yankees-Red Sox pitch data. It's for Python devs in AI tools needing fast MLB insights, or analysts tired of manual pybaseball scripts for team standings and arsenal breakdowns.

Verdict

Grab it if you're into baseball analytics—solid docs and 16+ tools make it usable now, despite 21 stars and 1.0% credibility signaling early maturity. Watch for caching adds; fork this public repository GitHub project before going public to private.

(198 words)

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