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Project Vision

The F1 ML Prediction System aims to become the most comprehensive open-source Formula 1 analytics platform, combining machine learning, real-time data, and advanced race simulations.
This roadmap is a living document. Features are prioritized based on impact, feasibility, and community feedback.

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Current Status

Phase 1: Foundation

✅ COMPLETE - Data collection, basic models, web dashboard
  • 7 years of historical data collected
  • Random Forest + XGBoost ensemble trained
  • Interactive Flask dashboard deployed
  • 85% prediction accuracy achieved

Phase 2: Enhancement

✅ COMPLETE - Weather, tire strategy, circuit factors
  • Weather impact modeling
  • Tire degradation analysis
  • Circuit-specific features
  • 2026 season predictions

Phase 3: Advanced Features

🔄 IN PROGRESS - Advanced ML and real-time updatesCurrently working on:
  • Neural network models
  • Safety car prediction
  • Live race updates

Phase 4: Production

📋 PLANNED - Cloud deployment and mobile appsUpcoming in 2026:
  • Cloud hosting
  • Mobile applications
  • User authentication
  • Premium features

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Roadmap Timeline

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Q1 2026: Model Improvements

Priority: HighGoal: Improve grid position predictions with actual qualifying telemetryFeatures:
  • Qualifying lap times (Q1, Q2, Q3)
  • Sector times and mini-sectors
  • Speed trap data
  • Tire compound used in qualifying
  • Track evolution effects
Expected Impact: +3-5% prediction accuracyStatus: 📋 Planned for March 2026
Priority: MediumGoal: Understand car performance before the raceFeatures:
  • FP1/FP2/FP3 long-run pace
  • Race simulation data
  • Tire degradation in practice
  • Setup correlation with race results
Expected Impact: Better car performance predictionsStatus: 📋 Planned for Q1 2026
Priority: HighGoal: Time-series prediction of tire performanceApproach:
# LSTM architecture for tire modeling
model = Sequential([
    LSTM(128, return_sequences=True, input_shape=(laps, features)),
    Dropout(0.2),
    LSTM(64),
    Dense(32, activation='relu'),
    Dense(1)  # Predicted lap time
])
Features:
  • Compound-specific degradation curves
  • Temperature impact modeling
  • Track surface effects
  • Driver aggression factors
Expected Impact: Accurate lap time predictions 10+ laps aheadStatus: 🔄 Research phase, 30% complete
Priority: Medium-HighGoal: Optimal pit stop timing using RL agentsApproach:
  • State space: Position, tire age, gap to cars ahead/behind, laps remaining
  • Action space: Pit now, Stay out
  • Reward: Final race position
Algorithm: Deep Q-Network (DQN) or Proximal Policy Optimization (PPO)Expected Impact: Find non-obvious strategic pit windowsStatus: 📋 Design phase

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Q2 2026: Enhanced Frontend

Priority: HighGoal: Live animated race progressionFeatures:
  • Track map with car positions
  • Real-time lap counter
  • Live timing tower
  • Pit stop animations
  • Safety car deployments
  • Weather changes overlay
Technology: Plotly Dash + WebSocket for real-time updatesStatus: 📋 Wireframes in progress
Priority: MediumGoal: Head-to-head driver analyticsFeatures:
  • Career statistics comparison
  • Head-to-head race records
  • Qualifying pace delta
  • Wet weather performance
  • Street circuit specialization
  • Wheel-to-wheel racing metrics
Status: ✅ 70% complete - basic comparison implemented
Priority: HighGoal: Season-long championship simulationFeatures:
  • Monte Carlo simulation (1000+ seasons)
  • Probability distributions for each driver
  • What-if scenario analysis
  • DNF risk modeling
  • Team development trajectory
Status: 📋 Algorithm design complete, implementation pending
Priority: MediumGoal: Live predictions during race weekendsImplementation:
  • Connect to FastF1 live timing API
  • Update predictions every lap
  • Adjust probabilities based on race events
  • Push notifications for key moments
Challenges: API rate limits, real-time processingStatus: 📋 Feasibility study in progress

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Q3 2026: Cloud Deployment

Priority: HighGoal: Public access to F1 ML predictionsOptions:
  1. Heroku - Easy deployment, free tier available
  2. Railway - Modern platform, good for Flask apps
  3. AWS Elastic Beanstalk - Scalable, production-ready
  4. Google Cloud Run - Serverless, cost-effective
Recommended: Railway for MVP, AWS for scaleInfrastructure:
Frontend (React) → Vercel/Netlify
Backend (Flask) → Railway/Heroku
Database (PostgreSQL) → Railway/AWS RDS
Model Storage (S3) → AWS/Google Cloud Storage
Status: 📋 Platform selection in progress
Priority: HighGoal: Move from CSV to production databaseSchema:
  • races - Race metadata
  • drivers - Driver information
  • teams - Team/constructor data
  • results - Race results
  • lap_times - Lap telemetry
  • pit_stops - Pit stop data
  • predictions - Historical predictions
  • users - User accounts (future)
Benefits:
  • Faster queries
  • Concurrent access
  • Data integrity
  • Better scalability
Status: 📋 Schema design complete
Priority: MediumGoal: User accounts and personalizationFeatures:
  • Sign up / Login (email + password)
  • OAuth (Google, GitHub)
  • Saved predictions
  • Favorite drivers/teams
  • Custom alerts
Technology: Flask-Login + JWT tokensStatus: 📋 Design phase
Priority: Low-MediumGoal: iOS and Android applicationsApproach:
  • React Native for cross-platform
  • Native UI components
  • Push notifications
  • Offline mode (cached data)
Status: 📋 Concept stage

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Q4 2026: Advanced Analytics

Priority: Medium-HighGoal: Predict safety car likelihood per raceFeatures:
  • Circuit-specific SC rates (Monaco 60%, Spa 20%)
  • Weather correlation
  • First-lap incident probability
  • Historical pattern analysis
Training Data:
  • Safety car deployments 2018-2024
  • Virtual safety cars
  • Red flag events
Model: Logistic Regression or Random Forest classifierExpected Accuracy: ~70% (SC events are inherently random)Status: 📋 Data collection phase
Priority: MediumGoal: Predict retirement probability per driverFeatures:
  • Mechanical reliability by team
  • Driver crash history
  • Circuit danger rating
  • Starting position risk (P20 higher DNF chance)
  • Weather impact on incidents
Use Case: Adjust race predictions for DNF riskStatus: 📋 Feature engineering phase
Priority: HighGoal: Probabilistic championship modelingApproach:
  1. Run 10,000 simulated seasons
  2. Each race uses ML predictions + randomness
  3. Account for DNFs, weather, safety cars
  4. Generate probability distribution
Output:
  • Championship win probability per driver
  • P95/P5 confidence intervals
  • Critical race importance scores
  • Upset scenario analysis
Status: 📋 Algorithm complete, needs implementation
Priority: Low-MediumGoal: Group drivers by racing styleFeatures:
  • Qualifying vs race pace
  • Aggression metrics (overtakes/lap)
  • Consistency (std dev of finishes)
  • Wet weather skill
  • Tire management ability
Algorithm: K-Means clustering or DBSCANClusters:
  • “Qualifiers” - Fast over one lap
  • “Racers” - Strong race pace
  • “Rainmasters” - Wet specialists
  • “Defenders” - Position holders
Status: 📋 Feature selection in progress

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Long-term Vision (2027+)

AI Race Engineer

Virtual race engineer providing:
  • Real-time strategy advice
  • Pit stop recommendations
  • Overtaking opportunity alerts
  • Setup suggestions

Fantasy F1 Integration

Connect to fantasy leagues:
  • Optimal lineup suggestions
  • Captain picks based on predictions
  • Differential driver recommendations

Betting Odds Comparison

Compare ML predictions vs bookmaker odds:
  • Value bet identification
  • Edge detection
  • Historical ROI tracking

Community Features

Social and community elements:
  • Prediction competitions
  • Leaderboards
  • Discussion forums
  • Shared custom models

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Technical Debt & Refactoring

Code Quality Improvements Needed:
  1. Testing: Add unit tests and integration tests (currently 0% coverage)
  2. Documentation: Improve inline code documentation
  3. Type Hints: Add Python type annotations throughout
  4. Error Handling: Better exception handling and logging
  5. Config Management: Move hardcoded values to config files
  6. API Versioning: Implement proper API versioning (v1, v2, etc.)

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Contributing to the Roadmap

We welcome community input on priorities and new feature ideas!
1

Review Current Roadmap

Read through planned features and timelines
2

Submit Feature Requests

Open GitHub issues with:
  • Clear use case description
  • Expected impact
  • Implementation ideas (if any)
3

Vote on Priorities

React to issues with 👍 to help prioritize features
4

Contribute Code

Pick an issue and submit a pull request:
  • Fork the repository
  • Create feature branch
  • Implement with tests
  • Submit PR for review

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Success Metrics

We measure project success through:
  • Target: 85%+ accuracy on test set
  • Current: 80% (Random Forest), 85.9% (V2 Enhanced)
  • Goal: 90%+ with neural networks

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Get Involved

GitHub Repository

Star, fork, and contribute to the project

Feature Requests

Suggest new features and improvements

Bug Reports

Report issues and help improve quality

Discussions

Join conversations about F1 analytics
This roadmap reflects the current development priorities as of March 2026. Features and timelines may adjust based on progress and feedback.

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