Football Intelligence & Optimisation Server

An MCP (Model Context Protocol) server that gives Claude live Fantasy Premier League data, expected-goals analytics, and a genuine constrained-optimisation engine — so you can ask Claude to build your FPL squad, compare players, or get transfer advice, backed by real data rather than a hard-coded response.

Why this exists

Built as a hands-on demonstration spanning three things I actually work with: data engineering (two independent live/scraped data sources, reconciled and cached), operations research (a real linear program, not a heuristic, deciding your squad), and shipping something that's actually deployed and connectable — not a notebook that only runs on my machine.

Related MCP server: FPL Intelligence

What it can do

Tools:

Prompts (ready-made structured asks an MCP client can surface directly):

Example prompts to try

Once connected, just ask Claude things like:

• "Build me the best FPL squad under £100m using a 4-4-2 formation"

• "Compare Haaland and Watkins — who's the better pick right now?"

• "Find me the best value defenders under £5m"

• "What are Arsenal's next 5 fixtures like?"

• "Who should I captain this week?"

How it's built

FPL API (live)  ──┐
                   ├──> player_pool.py ──> points_prediction.py ──┐
Understat (xG) ────┘         (data layer)    (scoring proxy)      │
                                                                    ▼
                                              optimize_squad.py (PuLP linear program)
                                                                    │
                                                                    ▼
                                          server.py (FastMCP: 5 tools + 2 prompts)

Two independent, unofficial data sources feed this: the FPL API (free, undocumented but stable) and Understat (scraped, less stable — handled with retries, caching, and graceful fallback to last season's data when the current season is too new to have stats yet). Points predictions use an empirical-Bayes shrinkage estimator so a one-match fluke doesn't skew the optimiser. The optimiser itself is a genuine mixed-integer linear program (via PuLP/CBC) — not a greedy heuristic — jointly solving squad selection, starting XI, and captaincy in one pass under FPL's real constraints (budget, position quotas, max 3 players per club).

Tech stack

Python · FastMCP · pandas · PuLP (CBC solver) · httpx · understatapi · uv · Docker

Running it locally

uv venv
uv pip install -r requirements.txt
uv run fastmcp dev inspector server.py

That opens the MCP Inspector in your browser, where you can call any tool directly and see the raw response.

Connecting to Claude Desktop

uv run fastmcp install claude-desktop server.py --name "Football Optimiser"

Restart Claude Desktop and it'll show up as a connected tool — from there you can just talk to it in plain English.

Deployment

Runs as a standard containerized HTTP service (see Dockerfile) — set MCP_TRANSPORT=http and it serves over Streamable HTTP instead of stdio. A shared API key (FOOTBALL_MCP_API_KEY env var) and a per-IP rate limit (middleware.py) gate the public endpoint.

Live server: (https://football-mcp-server-production.up.railway.app/)

A few honest notes

• This project was built and tested right as the 2026/27 Premier League season kicked off — several of the design decisions (graceful fallback when a data source has no data yet, empirical-Bayes shrinkage instead of raw season stats) exist specifically because early-season data is sparse, not because they're theoretically elegant.

• The xG integration matches players between two sources with no shared ID, by name — exact match first, fuzzy match as a fallback for accents/name variants, with anything it's not confident about explicitly flagged rather than silently guessed.

• Built iteratively over several weeks, story by story, against a tracked project plan with effort estimates — the commit history reflects that rather than one large dump.

License

MIT