NFL Game Prediction and Analysis Pipeline

Build an NFL Handicapping Pipeline (Python)

Goal: A Python system that ingests free/low-cost data, projects games/props, finds edges vs books, and outputs a clean “board” (JSON/Markdown) for posting in Whop/Discord.

Stack & Data

Python 3.10+, pandas, requests, scikit-learn, APScheduler, mysql-connector-python.

DB: MySQL.

Sources (free where possible):

Games/teams/players: nfl_data_py.

Odds & movement: The Odds API (free tier OK).

Public consensus % (proxy for bets/money): VegasInsider/Covers (scrape).

Weather (hourly, wind at stadium): National Weather Service API.

What to Build (modules)

Slate ingest

Pull weekly schedule (season/week, home/away, kickoff time).

Compute flags: divisional, rest days, travel/time-zone.

Odds snapshots

Every 10–15 min: store book lines (spread/total/moneyline; later props).

Track openers vs current per game/book; compute movement deltas.

Consensus scrape

Pull % bets/% money proxies for sides/totals; timestamp and store.

Weather

Stadium lat/lon → NWS hourly forecast around kickoff (wind/gust/precip).

Team & player features

From nfl_data_py: rolling EPA (off/def), success rate, neutral pace.

Player usage baselines (target share, rush share, red-zone).

Projections & edges

Convert spread/total → implied team totals; blend with EPA/pace + weather.

Simple ridge/elastic-net models for core props (QB pass yds, WR rec, RB rush att).

Compare projection vs line → edge %, fair price, Kelly fraction, confidence (0–100).

Parlay helper (rule-based)

Generate 1–2 correlated legs per game script (e.g., “home leads early” → QB over + WR rec over + opp QB att under).