This server provides a comprehensive local NFL analytics platform backed by a DuckDB warehouse of play-by-play data since 1999 (1.28M plays). You can:
Query the Warehouse: Run ad-hoc SQL queries via query_warehouse and inspect schemas with describe_warehouse, including data-dictionary notes and join conventions.
Analyze Teams & Players: Get team form (record, EPA splits, rest/travel profiles), player bios and stats, coach profiles (career records, tendencies, ATS performance), and referee stats (penalty biases, home/over rates).
Generate Predictions & Ratings: Obtain model-based game predictions (win probability, margin), view team power ratings in EPA/play, and review model backtest reports.
Monitor Betting Markets: Explore Kalshi markets (game, spread, total, win totals, Super Bowl), get market details with bid/ask, implied probabilities, and fees, track price history, and use a betting board to compare Vegas lines vs. Kalshi prices highlighting dislocations.
Access News & Injuries: Search news by topic or player, get recent player news and injury info, and browse latest league headlines.
Manage Data: Check data freshness and refresh logs via data_status, and trigger a manual refresh of the latest nflverse data with refresh_data.
Provides tools for querying and analyzing a local DuckDB warehouse containing NFL play-by-play data, player/team stats, rosters, injuries, officials, draft history, and betting lines, enabling AI agents to answer football questions via SQL.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@NFL Analytics MCP which QBs perform best traveling east?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A personal NFL analytics platform that runs entirely on your machine: a DuckDB warehouse of every NFL play since 1999, a dark-glass web app ("Jarvis"), auto- updating data and news, a prediction-market tracker, and an AI analyst wired in through MCP. Questions get answered by running SQL against real data — computed answers, not vibes.
No API keys. No subscriptions for the core experience. One ~2 GB download.
Piece | What it does |
Warehouse ( | 1.28M plays 1999–2025, schedules with odds through 2026, player/team stats across the v1/v2 nflverse eras, advanced stats, NGS, snap counts, depth charts, personnel/participation, FTN charting, combine, ESPN QBR — plus curated venue coordinates powering true travel distances |
Jarvis web app ( | FastAPI + React SPA: team HUDs, matchup cards (travel/rest/refs/coach-H2H/weather/market for any game 1999→upcoming), all-time H2H explorer, coaches with scheme fingerprints, referee intel, betting board (Vegas-vs-Kalshi dislocations), news, and a 15-chapter football Knowledge book |
Derived views | 20+ SQL views: team-game workhorse with haversine travel miles, H2H series with relocations merged, referee tendencies 1999+, coach PROE/4th-down aggression, one unified weather answer per game |
News engine ( | ESPN + team feeds polled 6-hourly, categorized, player-tagged by gsis_id, full-text searchable |
Kalshi tracker ( | Market snapshots 6-hourly + Vegas line history per game |
Prediction model | Trained and validated, deliberately paused — betting surfaces are market-vs-market only |
MCP server | 20 tools ( |
Legacy dashboard ( | The original Streamlit UI, kept for friends — frozen |
Related MCP server: Feyod MCP Server
nflverse releases ──► scripts/refresh_data.py ──► data/*.csv
│
Open-Meteo ──► fetch_weather.py
│
build_warehouse.py ──► nfl.duckdb ◄── build_views.py (views, venues, macros)
│
┌───────────────────────────┼──────────────────────────┐
│ │ │
MCP server (20 tools) FastAPI (web/api) pytest invariants (tests/)
│ │
Claude Code React SPA (web/ui) ── Playwright e2e
│
kalshi.duckdb ── news.duckdb (sidecars, 6h pollers)Five Windows Task Scheduler jobs keep it fresh: weekly data refresh, 6-hourly news + Kalshi pollers, a daily smoke test, and a nightly headless Claude health check that audits the data and runs the test suite.
See SETUP.md. Short version:
git clone https://github.com/parthakker/nfl-analytics && cd nfl-analytics
uv sync --extra dev # or: pip install -e ".[dev]"
python -m nfl_analytics.cli refresh --bootstrap # ~2 GB nflverse download
cd web/ui && npm ci && npm run build && cd ../..
python web/run_web.py # Jarvis on http://localhost:8000Three pytest tiers: tests/unit (no database), tests/warehouse (invariants
against the real DB — travel distances, H2H symmetry, venue resolution, era
continuity), tests/api (contract tests for every endpoint). CI runs the unit
tier plus warehouse/api against committed ~24 MB fixture databases
(nfl fixture rebuilds them). Playwright e2e (web/ui/e2e/) runs locally
against the real server: npm run e2e.
Compute, don't retrieve — every answer is SQL with its filters cited.
Hand-curated where sources are wrong — data/stadiums.json fixes venues
nflverse mislabels (all seven 2025 international games); curated files are
version-controlled and never overwritten by refreshes.
Verification built in — row floors and sanity checks in the build, invariant tests in CI, a smoke test and an AI health loop on the scheduler.
Keep it simple — one machine, no cloud dependencies, boring tools.
nflverse (play-by-play, stats, rosters), nfldata (schedules + odds), Open-Meteo (weather), ESPN (news, QBR), Kalshi (market data). MIT licensed; the data belongs to its sources.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Query PostgreSQL databases in plain English — LLM-generated, safety-validated SQL.
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