RLStatistics-ML

RLStatistics-ML is a Python toolkit designed to process Rocket League replay data, extract player and match statistics, and train machine learning models—such as Random Forest classifiers—to predict match outcomes and enhance an in-game plugin’s analytics capabilities.

Introduction

RLStatistics-ML provides a streamlined pipeline to:

1. Parse and clean raw replay JSON files.
2. Compute aggregated statistics per player and per match.
3. Train and evaluate a Random Forest model for outcome prediction.

Features

• Replay Processing: Load and normalize Rocket League replay data into pandas DataFrames for further analysis.
• Statistical Extraction: Compute key metrics (goals, saves, shots, assists) per player and match.
• Machine Learning Pipeline: Train a Random Forest classifier end-to-end using scikit-learn’s RandomForestClassifier for match outcome prediction.
• Configurable Workflows: Toggle data processing and model training directly in main.py via commenting/uncommenting.
• CSV & JSON Outputs: Export processed data and model predictions in both CSV and JSON formats for integration with external tools.

Installation

1. Clone the repository

   git clone https://github.com/BenjaminDanker/RLStatistics-ML.git
   cd RLStatistics-ML

2. Create a virtual environment (recommended)

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Usage

All operations are controlled in main.py. By commenting or uncommenting the corresponding lines, you can switch between replay processing and model training:

if __name__ == "__main__":
    # To process replay files:
    # from replay_process import process_replays
    # process_replays(input_path="replays_all.json", output_path="replays_processed.csv")

    # To train & evaluate the model:
    # from train_model import train_and_evaluate
    # train_and_evaluate(train_csv="replays_processed.csv", output_json="replays_stats.json")

1. Enable replay processing by uncommenting the process_replays lines.
2. Enable model training by uncommenting the train_and_evaluate lines.
3. Run:

   python main.py

Dependencies

• Python 3.8+
• pandas: data manipulation and analysis library
• NumPy: numerical computing library
• scikit-learn: machine learning toolkit, used here for Random Forests

Contributing

1. Fork the repo.
2. Create a feature branch (git checkout -b feature/my-feature).
3. Commit your changes (git commit -m "Add new feature").
4. Push to the branch (git push origin feature/my-feature).
5. Open a Pull Request.

License

This project is licensed under the MIT License.