Model Analytics
Track model accuracy, compare algorithms, and understand feature importance.
Logistic Regression
68.2%
Accuracy
P: 65.4%
R: 70.1%
F1: 67.7%
Random Forest
70.5%
Accuracy
P: 68.9%
R: 71.3%
F1: 70.1%
XGBoost
72.1%
Accuracy
P: 70.3%
R: 73.2%
F1: 71.7%
LightGBM
71.8%
Accuracy
P: 69.7%
R: 72.9%
F1: 71.3%
Ensemble
73.4%
Accuracy
P: 71.2%
R: 74.1%
F1: 72.6%
Accuracy Over Time
Calibration Curve
Feature Importance
Confusion Matrix (Ensemble Model)
Predicted →
Home
Draw
Away
Home
145
32
18
Draw
28
89
25
Away
15
22
126
↑ Actual
Model Comparison
| Model | Accuracy | Precision | Recall | F1 Score | Status |
|---|---|---|---|---|---|
| Logistic Regression | 68.2% | 65.4% | 70.1% | 67.7% | Available |
| Random Forest | 70.5% | 68.9% | 71.3% | 70.1% | Available |
| XGBoost | 72.1% | 70.3% | 73.2% | 71.7% | Available |
| LightGBM | 71.8% | 69.7% | 72.9% | 71.3% | Available |
| Ensemble | 73.4% | 71.2% | 74.1% | 72.6% | Primary |