The Model Building Pipeline

## From Data to Probability Building a sports prediction model follows a reproducible pipeline. Each step must be completed before the next begins — shortcuts at any stage compromise the entire output. ## The Six-Stage Pipeline **Stage 1 — Define the question:** What exactly are you predicting? Match winner? Goals scored? Player performance metric? The prediction target must be precisely defined before data collection begins. **Stage 2 — Collect and clean data:** Identify data sources, collect historical data, clean for errors and missing values, structure for analysis. This stage takes the most time. **Stage 3 — Feature engineering:** Create the input variables (features) from raw data. Examples: rolling average xG, home/away performance split, head-to-head record, days since last match. **Stage 4 — Model specification:** Choose the model structure: Poisson regression, logistic regression, Elo rating, or machine learning approach. Simple models should be tried before complex ones. **Stage 5 — Estimation:** Fit the model to historical data. Estimate parameters. **Stage 6 — Evaluation:** Test the model on held-out data. Measure probability accuracy. Compare to the market. ## The Minimum Viable Model A first model does not need to be sophisticated. A minimum viable model: - Uses 3–5 input features (home xG rate, away xG rate, home advantage, rest days difference, relative position) - Uses Poisson regression with these features as predictors - Produces match-level goal probabilities from which all market probabilities can be derived Build the MVP first. Evaluate it. Improve iteratively.