F1-Prediction: An Open-Source Project for F1 Race Prediction Using Machine Learning

F1-Prediction is an open-source machine learning project focused on predicting Formula 1 race results. It uses historical data to train models, enabling multi-level predictions from podium finishes to full rankings. For F1 enthusiasts and data science learners, this project serves as an excellent practical case of combining sports data analysis with machine learning. The original author of the project is Francesco Lazzarotto, and it was published on GitHub at the link https://github.com/FrancescoLazzarotto/F1-Prediction on June 6, 2026.

F1 race result prediction faces complex challenges and is influenced by multi-dimensional factors:

• Driver Factors: Driving style, psychological quality, wet weather racing ability, historical performance on specific tracks
• Team Factors: Car performance, reliability, strategy team level, resource allocation, and tire management
• Track Characteristics: Differences in speed and handling requirements across tracks (e.g., Monza emphasizes straight-line speed, while Monaco tests low-speed handling)
• Environmental Factors: Weather conditions, safety car deployments, and the impact of temperature on tires
• Random Factors: Unpredictable events such as collisions, mechanical failures, and red flag interruptions These factors together form the unique appeal and challenges of F1 prediction.

Data Collection and Feature Engineering

Collect historical race results, driver career data, team performance metrics, track historical data, and real-time data (practice/qualifying lap times, tire wear), then convert them into numerical features (e.g., average finishing position on the current track, points trend over the last five races).

Model Selection and Training

• Classification Models: Random Forest, XGBoost, etc., for predicting podiums/points zones
• Ranking Models: LambdaMART to optimize full rankings
• Regression Models: Predicting finishing times or time gaps
• Ensemble Methods: Voting/weighted averaging to improve accuracy

Evaluation Metrics

Use metrics such as accuracy, average ranking error, Kendall Tau coefficient, and Top-N hit rate to evaluate model performance.

• Real Data Science Workflow: Need to handle scattered data sources, inconsistent formats, and missing values, which is close to real-world work scenarios
• Feature Engineering Space: Can build creative features (e.g., performance differences between dry and wet conditions, changes in team competitiveness over the season)
• Model Interpretability Practice: Need to explain prediction reasons using feature importance, SHAP values, etc.
• Continuous Iteration and Feedback: New season data can verify and improve models, fostering maintenance awareness This project provides data science learners with a practical platform to apply theory to complex problems.

• Data Processing Layer: Use Pandas to clean data, construct features, and merge multiple data sources
• Model Training Layer: Use scikit-learn and XGBoost for training and hyperparameter tuning
• Prediction Service Layer: Encapsulate models into reusable services to support predictions for new races
• Visualization Layer: Use Matplotlib/Plotly to display prediction results, historical trends, and model performance For specific implementation details, please refer to the project source code.

The open-source project welcomes community contributions. Potential improvement directions:

1. Data Expansion: Integrate tire data, fuel strategies, and DRS usage
2. Model Innovation: Try deep learning, graph neural network, and other architectures
3. Real-Time Prediction: Develop a prediction system that updates in real-time during races
4. Visualization Enhancement: Build interactive dashboards to display results and insights
5. Multi-Season Validation: Establish a cross-season model performance evaluation framework These directions can promote the continuous development of F1 prediction technology.

The F1-Prediction project demonstrates the practical and educational value of combining sports data analysis with machine learning: it serves as an entry point for data-driven decision-making for F1 enthusiasts, and a practical platform for data science learners to apply theory to complex problems. Its open-source nature allows the community to participate in improvements together, driving the progress of F1 prediction technology.