How Our AI Model Works

Every prediction on Tahminbaz is generated by an ensemble of multiple machine learning models. Our process is fully transparent.

Data Collection

We collect match data, team statistics, player performance metrics, and betting odds from over 20 leagues worldwide. Thousands of data points are updated daily.

Feature Engineering

We transform raw data into features that models can understand:

Elo Ratings: Dynamic power rankings for every team

Form Index: Performance trend over the last 5-10 matches

Fatigue Score: Impact of match congestion and rest days

Head-to-Head: Historical performance between teams

Home/Away Splits: Home and away advantage analysis

Model Layer

We use three different model architectures:

Poisson Regression: A classical statistical approach for modelling goal expectancy

Gradient Boosting: A tree-based model that captures complex patterns

Neural Network: A PyTorch-based deep learning model

Ensemble and Calibration

Rather than relying on a single model, we combine all three with adaptive weights. The weights automatically adjust based on recent performance.

A calibration layer continuously verifies that the probabilities our model produces align with real outcomes. When we say "70% probability," we aim for approximately 70 out of 100 such matches to end that way.

Our Transparency Commitment

You can track our prediction performance live on the /transparency page. We hide nothing -- losses included, everything is public.