Methodology

1Data Collection

We source our raw data from enterprise-grade sports APIs, ensuring we have up-to-the-minute information on everything from starting lineups to pitch conditions. We track over 1,000 distinct data points per match, establishing a robust foundation for our models.

2Feature Engineering

Raw data isn't enough. We engineer custom metrics to better capture team performance dynamics:

• Expected Goals (xG) Momentum: How a team is performing vs. expected over their last 5 fixtures.
• Fatigue Index: Calculated based on travel distance, rest days, and fixture congestion.
• Tactical Compatibility: How a team's typical formation performs historically against the opponent's preferred setup.

3Machine Learning Models

We employ an ensemble approach, utilizing several different machine learning architectures. Gradient Boosting (XGBoost) helps us find non-linear patterns in the data, while Poisson regression models simulate goal distributions to output exact probability percentages for Home, Draw, and Away outcomes.

4Human-in-the-Loop Analysis

While our baseline probabilities are untouched by human emotion, our generated articles and tactical write-ups use large language models (LLMs) to construct readable, insightful narratives explaining the statistical phenomena the model is reacting to.