parmacalcio1913

parmacalcio1913 / event-data-chatbot

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A CLI chatbot powered by StatsBomb open data that can be used to query event data using natural language.

16
2
85% credibility
Found May 22, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A friendly chatbot that lets anyone ask questions about football match data without needing to write code or understand databases. You simply type questions like 'Who scored the most goals in La Liga 2015/16?' and the AI searches through thousands of match records to find the answer. The tool uses real football data from StatsBomb and explains results in plain English, so anyone can explore statistics, compare teams, or generate match reports just by chatting.

How It Works

1
🔍 You discover a new way to explore football data

You've always wondered things like 'Who had the best shots in the 2015 Premier League?' but never had an easy way to find out.

2
🎒 You set up your assistant

You connect your AI assistant (Claude) to this tool so it can understand your questions about football matches.

3
📦 Your football data is downloaded and ready

With one simple command, all the match data from StatsBomb loads into your computer as a local library.

4
💬 You ask your first question

You type something like 'Show me all goals scored by Messi in 2015/2016 La Liga' and press Enter.

5
Your AI assistant thinks and finds answers
🏟️
Basic question

A simple question gets answered directly with a table or list of results

📋
Match summary

You can type '/summary 3877313' to get a full written match report with lineups and key events

🎉 You get your answer instantly

The AI explains the results in plain language—no code, no technical terms. You can keep asking follow-up questions to explore deeper.

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AI-Generated Review

What is event-data-chatbot?

This is a CLI chatbot that lets you ask Claude analytical questions about football event data using plain English. You can type queries like "Who scored the most goals in the 2015/2016 La Liga?" or "Compare Barcelona's home and away xG" and get back structured answers. Under the hood, an MCP server exposes a local StatsBomb open-data snapshot as a read-only SQL tool, and Claude writes the queries against DuckDB for you. The project is written in Python, requires an Anthropic API key, and ships with tab completion and a built-in match summary command.

Why is it gaining traction?

The main hook is that you get the analytical power of a language model without writing a single line of SQL. The architecture is clean: a CLI chat loop, an MCP server that bakes the full StatsBomb schema into the tool description so Claude never wastes tokens inspecting the catalog, and a download script that fetches competitions, matches, lineups, and events into a local DuckDB file. The `--query` flag exposes the SQL Claude generates, which is useful if you want to learn the schema. The `--usage` flag tracks token consumption per turn.

Who should use this?

Football analysts, data journalists, and developers building quick prototypes on StatsBomb data will get the most value. If you want to explore passing networks, xG trends, or player contributions without spinning up a notebook or writing SQL, this is a fast path. It is less useful if you need real-time data, broader league coverage (only five leagues for 2015/2016 are included), or a web interface.

Verdict

This is a well-structured proof of concept that demonstrates how MCP can bridge a local database to a language model for domain-specific queries. The credibility score of 0.85% reflects a very small community and limited production hardening, but the code is readable, CI runs linting and tests across Python 3.10-3.12, and the README is thorough. Worth trying if you want to experiment with natural language data exploration on football event data.

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