XGBoost
Fast gradient boosting with strong tabular performance, missing-value handling, and SHAP-based feature explanations.
Machine Learning (ML) models
LightGBM
High-speed, memory-efficient boosting with native categorical support, leaf-wise growth, and fast hyperparameter sweeps.
Random Forest
Stable ensemble baseline with built-in out-of-bag scoring, permutation importance, and low variance on noisy data.
CatBoost
Handles categorical features natively with ordered boosting for overfitting resistance and built-in permutation importance.
Neural Networks / LSTM
Captures complex non-linear patterns in time-series data with embeddings, attention activations, and hybrid tree stacking.
Ensembles & Calibration
Stacks multiple models and calibrates probabilities (Platt scaling, isotonic) for more reliable edge detection.
Ridge Regression
Regularized linear model (L2) for spread and totals regression — stable under multicollinearity with interpretable coefficients.
Repo-specific Models
- Logistic Regression: value probability modeling, manager/referee effect analysis (premier-league).
- ELO + custom ratings: team/player form and matchup strength (all sports models).
- Poisson/Score-based regressions: player prop and goal prediction pipelines (hockey, soccer).
- Gradient Boosting variants: domain-specific feature-discrete handling (gridiron, tennis, golf).
- Time-series models: sequential injury trend and live tracking models (nba-predictions).