Analysis in sports is changing. Advanced statistics like Wins Above Replacement (WAR) or Expected Goals (xG) are making their way into TV punditry and conversations in bars. But the people who need the information the most, ex-professionals and coaches without a background in statistics, often shun it.
Not because they don’t see the value, but because the language is impenetrable, the underlying data is overwhelming, and the insights are difficult to translate.
Generative AI provides an opportunity to bridge the gap.
In this talk, I’ll share how I used Model Context Protocol (MCP) to turn Anthropic’s Claude Desktop into a football opposition analyst by providing access to team and player performance event data, and in turn lower the barriers so anyone can turn a sea of numbers into actions.
This talk will cover:
- How MCP enables AI to access and interpret domain-specific knowledge
- Real examples of AI-generated football insights in action
Adam Cowley
Adam Cowley is Manager of Developer Education at Neo4j. He leads the team behind GraphAcademy, Neo4j’s developer learning platform. His 20+ years of experience spans software engineering, data analysis, and product ownership. He is currently focused on applying Generative AI to create more personalised developer education.
Outline
Citation
@online{bochman2025,
author = {Bochman, Oren},
title = {Using {MCP} to Turn {Claude} into a {Football} {Opposition}
{Analyst}},
date = {2025-12-10},
url = {https://orenbochman.github.io/posts/2025/2025-12-10-pydata-using-mcp-to-turn-claude-into-football-analyst/},
langid = {en}
}