🧠 Premier League Player Performance Prediction

This project aims to predict English Premier League (EPL) players' performance using machine learning techniques. It was developed as a graduation thesis to demonstrate applied knowledge in data science, predictive modeling, and sports analytics.

🎯 Objective

The goal of this project is to build a predictive model that can forecast key performance indicators (KPIs) of EPL players — such as goal involvement, match rating, or form — based on historical match and player data.

📚 Background

Accurately predicting football player performance has significant applications in sports analytics, fantasy leagues, and club decision-making. Traditional scouting methods are often limited in scope; this project leverages data-driven methods to enhance prediction accuracy and uncover hidden player insights.

⚙️ Methodology

1. Data Collection & Preprocessing

   • Cleaned missing and inconsistent values
   • Normalized numerical features
   • Encoded categorical variables (position, team, etc.)

2. Feature Engineering

   • Derived metrics like goal contribution rate, defensive efficiency
   • Analyzed player form over recent matches

3. Model Development

   • Trained and compared multiple classifiers:
     • Logistic Regression
     • Random Forest
     • Support Vector Machines (SVM)
     • XGBoost

4. Model Evaluation

   • Metrics used: Accuracy, F1-score, Precision, Recall, ROC-AUC
   • Cross-validation for generalizability

📈 Results

🥇 Best Model: XGBoost achieved the highest performance with an F1-score of XX% and ROC-AUC of XX% (fill with actual values)

Visualization examples:

• Feature importance
• Confusion matrix
• Model performance comparison

🗂️ Project Structure

PL-Player-Prediction/
├── data/                # Raw and cleaned datasets
├── notebooks/           # Jupyter notebooks for EDA and modeling
├── models/              # Trained models (optional)
├── plots/               # Performance visualizations
├── pl_predictor.py      # Main ML script
├── requirements.txt     # Project dependencies
└── README.md            # Documentation

🧪 Technologies Used

• Python 3.10
• pandas, numpy
• scikit-learn
• XGBoost
• seaborn, matplotlib
• Jupyter Notebook

🔍 Sample Predictions

(Include a sample table or image showing predicted vs. actual performance.)

💡 Future Work

• Expand dataset to multiple seasons or leagues
• Integrate time-series modeling (e.g., LSTM)
• Create a web-based dashboard using Streamlit
• Add model explainability (SHAP / LIME)
• Deploy model with a REST API (FastAPI)

📜 Citation

If you use this work or base your project on it, please cite:

@project{isamel2025plprediction,
  author    = {Mostafa Isamel},
  title     = {Premier League Player Performance Prediction using Machine Learning},
  year      = {2025},
  note      = {Graduation Project, [Your University Name]}
}

👨‍💻 Author

Mostafa Isamel
Graduating Data Science Student
GitHub: @mostafaisamel

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🚀 Getting Started

1. Clone Repository

git clone https://github.com/mostafaisamel/PL-Player-Prediction.git
cd PL-Player-Prediction

2. Install Requirements

pip install -r requirements.txt

3. Run Jupyter Notebooks

jupyter notebook

Explore the notebooks in /notebooks to view EDA, model training, and results.

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🤝 Contributions

Feel free to open an issue or submit a pull request for improvements, suggestions, or questions.