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tags:
- pytorch
- xG
- football
- soccer
- expected-goals
- mlp
- binary-classification
library_name: pytorch
---
# MLP xG Prediction Model
This is a Multi-Layer Perceptron (MLP) model trained to predict Expected Goals (xG) in football/soccer.
## Model Description
- **Architecture**: Multi-Layer Perceptron with 3 hidden layers
- **Hidden Dimensions**: 128 → 64 → 32
- **Input Features**: 22 features
- **Output**: Binary probability (goal vs no goal)
- **Framework**: PyTorch
- **Dropout Rate**: 0.3
- **Activation**: ReLU (hidden layers), Sigmoid (output)
- **Normalization**: Batch Normalization after each hidden layer
## Performance Metrics
- **Accuracy**: 0.8797
- **Precision**: 0.6452
- **Recall**: 0.1225
- **F1 Score**: 0.2060
- **ROC AUC**: 0.7866
- **Log Loss**: 0.3169
## Features
The model uses the following 22 features:
- angle_to_gk
- angle_to_goal
- ball_closer_than_gk
- body_part_name_Left Foot
- body_part_name_Other
- body_part_name_Right Foot
- dist_to_gk
- distance_to_goal
- goal_dist_to_gk
- minute
- nearest_opponent_dist
- nearest_teammate_dist
- opponents_within_5m
- play_pattern_name_From Counter
- play_pattern_name_From Free Kick
- play_pattern_name_From Goal Kick
- play_pattern_name_From Keeper
- play_pattern_name_From Kick Off
- play_pattern_name_From Throw In
- play_pattern_name_Other
- play_pattern_name_Regular Play
- teammates_within_5m
## Usage
```python
import torch
import joblib
from huggingface_hub import hf_hub_download
# Download files
model_path = hf_hub_download(repo_id="rokati/mlp_xg", filename="best_mlp_model.pth")
architecture_path = hf_hub_download(repo_id="rokati/mlp_xg", filename="model_architecture.py")
scaler_path = hf_hub_download(repo_id="rokati/mlp_xg", filename="scaler.pkl")
config_path = hf_hub_download(repo_id="rokati/mlp_xg", filename="config.json")
# Load architecture
import importlib.util
spec = importlib.util.spec_from_file_location("model_architecture", architecture_path)
model_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(model_module)
# Load model
model = model_module.MLP(input_dim=22, hidden_dims=[128, 64, 32], dropout_rate=0.3)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()
# Load scaler
scaler = joblib.load(scaler_path)
# Make prediction
# X_new should be a pandas DataFrame or numpy array with the correct features
X_scaled = scaler.transform(X_new)
X_tensor = torch.FloatTensor(X_scaled)
with torch.no_grad():
xg_prediction = model(X_tensor).numpy()
```
## Training
The model was trained on football shot event data with:
- Binary Cross Entropy loss
- Adam optimizer (lr=0.001, weight_decay=1e-5)
- ReduceLROnPlateau scheduler
- Batch size: 256
- Epochs: 50
## License
MIT
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