## What a Production Model Actually Looks Like
A production sports prediction model is not a single script or spreadsheet. It is an integrated system of components that collect data, process it, generate predictions, compare to markets, and track performance.
## The Component Architecture
**Data Layer:**
- Automated data collection scripts (match data, xG, lineups, weather, odds)
- Database (PostgreSQL): tables for matches, teams, players, predictions, bets
- Data quality checks: automated alerts for missing data or outlier values
**Processing Layer:**
- Feature calculation: rolling averages, strength adjustments, recency weights
- Rating updates: team ratings recalculated after each match
- Prediction generation: Poisson regression + Dixon-Coles → probability matrix
**Market Comparison Layer:**
- Live odds collection (Pinnacle API or scraping)
- Expected Value calculation: (Model probability − Implied probability) × odds
- Value threshold filter: only matches above CLV threshold enter bet candidates
**Execution Layer:**
- Bet candidate list generated automatically
- Stake calculation: Kelly fraction applied to model probability and best available price
- Manual bet placement (or API integration for automated placement)
- Bet log update: automatic recording with all metadata
**Reporting Layer:**
- Real-time dashboard: current bankroll, daily P&L, weekly CLV
- Post-match CLV calculation: predicted vs closing Pinnacle
- Monthly automated report generation
## Technology Stack
A solo bettor can build this with:
- Python (data collection, processing, prediction)
- SQLite or PostgreSQL (database)
- Jupyter Notebook or VSCode (development)
- Google Sheets or Tableau (dashboard)
- GitHub (version control)
A team can extend to cloud infrastructure (AWS/GCP), real-time data streams, and web-based dashboards.
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