---
tags:
- pytorch
- xG
- football
- soccer
- expected-goals
- mlp
- binary-classification
- player-kpis
library_name: pytorch
---

# MLP xG Prediction Model (with Player KPIs)

This is a Multi-Layer Perceptron (MLP) model trained to predict Expected Goals (xG) in football/soccer. This version includes **player-level KPIs** (`shots_per_90` and `goals_per_90`) as additional features to capture individual player shooting ability.

## Model Description

- **Architecture**: Multi-Layer Perceptron with 3 hidden layers
- **Hidden Dimensions**: 128 → 64 → 32
- **Input Features**: 25 features (including player KPIs)
- **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

## Key Enhancement

This model extends the baseline MLP by incorporating **player-specific KPIs**:
- `shots_per_90`: Average shots per 90 minutes played by the player
- `goals_per_90`: Average goals per 90 minutes played by the player
- `goals_percentage`: Percentage of shots that result in goals

These features help the model capture individual player shooting quality and finishing ability beyond just the shot characteristics.

## Performance Metrics

- **Accuracy**: 0.8839
- **Precision**: 0.6456
- **Recall**: 0.1085
- **F1 Score**: 0.1858
- **ROC AUC**: 0.7978
- **Log Loss**: 0.3023

## Features

The model uses the following 25 features:

### Shot Context 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
- goals_percentage

### Player KPI Features:
- shots_per_90
- goals_per_90

## Usage

```python
import torch
import joblib
import pandas as pd
from huggingface_hub import hf_hub_download

# Download files
model_path = hf_hub_download(repo_id="rokati/mlp_with_kpis_xg", filename="best_mlp_model.pth")
architecture_path = hf_hub_download(repo_id="rokati/mlp_with_kpis_xg", filename="model_architecture.py")
scaler_path = hf_hub_download(repo_id="rokati/mlp_with_kpis_xg", filename="scaler.pkl")
config_path = hf_hub_download(repo_id="rokati/mlp_with_kpis_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=25, 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 with all 25 features including player KPIs
X_scaled = scaler.transform(X_new)
X_tensor = torch.FloatTensor(X_scaled)
with torch.no_grad():
    xg_prediction = model(X_tensor).numpy()
```

## Data Requirements

To use this model, you need:
1. **Shot event data** with geometric and contextual features
2. **Player KPI data** calculated from historical player performance (shots_per_90, goals_per_90)
3. The data should be merged on `player_id` before making predictions

## Training

The model was trained on football shot event data merged with player KPIs:
- Binary Cross Entropy loss
- Adam optimizer (lr=0.001, weight_decay=1e-5)
- ReduceLROnPlateau scheduler
- Batch size: 256
- Epochs: 50

## License

MIT