Behind the Scenes: How I Build Prediction Models
A look into the methodology, data sources, and processes that power my sports predictions.
Behind the Scenes: How I Build Prediction Models
Transparency is one of my core values. Today, I'm pulling back the curtain on how the predictions actually get made.
Data Collection
Every model starts with data. I aggregate from multiple sources:
- Official league statistics
- Advanced metrics providers
- Historical line movement data
- Weather and environmental factors
- Injury reports and roster changes
The Modeling Process
Step 1: Feature Engineering
Raw stats don't tell the whole story. I transform data into predictive features:
- Rolling averages (10-game, 30-game windows)
- Home/away splits
- Rest adjustments
- Opponent-adjusted metrics
Step 2: Model Training
I use ensemble methods that combine multiple algorithms:
- Gradient boosting for point spreads
- Logistic regression for moneylines
- Neural networks for player props
Each model is backtested against 5+ years of historical data.
Step 3: Line Comparison
The model output is compared against available betting lines. Only when there's significant edge (3%+ expected value) do I make a pick.
Continuous Improvement
Models aren't set-and-forget. I review performance weekly, identify weaknesses, and iterate. The sports landscape changes – the models must adapt.
The Human Element
Despite all the automation, every pick gets a final human review. Context matters. Injuries, suspensions, and situational factors can override model recommendations.
This combination of data science and sports knowledge is what gives us the edge.