AI-Powered Motorsport Analytics & Strategic Performance Engine.Simulating real-world Formula 1 race engineering decision workflows using structured data analytics.
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F1 Race Intelligence Platform is a modular, production-oriented motorsport analytics system that transforms raw Formula 1 race session data into actionable performance intelligence. Built on top of the FastF1 API and engineered using scalable data pipelines, this platform bridges sports analytics, machine learning foundations, and interactive decision-support visualization.
Modern Formula 1 strategy is driven by data — lap times, tire degradation, stint dynamics, pace evolution, and race momentum.
This platform replicates that analytical framework by:
• Ingesting official F1 race session data
• Cleaning and validating lap-level telemetry
• Performing structured performance modeling
• Generating interactive strategic insights
It is not just a visualization tool. It is a race intelligence system.
1️⃣ Driver Delta Intelligence Engine
• Lap-by-lap time delta modeling
• Cumulative advantage tracking
• Zero-baseline race momentum visualization
• Identification of strategic turning points
Enables micro-level pace analysis across the race lifecycle.
2️⃣ Tire Degradation Modeling System
• Quantile-based outlier filtering (1%–99%)
• LapTime normalization to seconds
• Linear regression slope extraction
• Side-by-side degradation comparison
Quantifies tire performance decay and driver consistency under load.
3️⃣ Defensive Data Pipeline Architecture
• FastF1 cache optimization
• Null-safe session loading
• Minimum lap threshold enforcement
• Defensive driver input validation
• Streamlit-level caching (data + resources)
Ensures analytical integrity and production-level robustness.
User Interface (Streamlit)
↓
Session Loader (FastF1 + Cache Layer)
↓
Data Validation & Cleaning Engine
↓
Feature Engineering Pipeline
↓
Analytics Core (Delta + Degradation Modeling)
↓
Interactive Plotly Intelligence DashboardThe architecture follows clean separation of concerns:
• Data Layer
• Analytics Layer
• Visualization Layer
• Interface Layer
This makes the system extensible for advanced modeling modules.
•Python 3.10+
•FastF1 (Official F1 Data API)
•Pandas / NumPy (Data Engineering)
•Scikit-learn (Statistical Modeling)
•Plotly (Interactive Visualization)
•Streamlit (Analytics Interface Layer)
• Cached session loading to reduce API overhead
• Vectorized data operations
• Minimal recomputation via Streamlit cache decorators
• Structured modular imports for scalability
• Clean codebase separation under src/
This enables low-latency analytics even on full-race datasets (1000+ laps).
F1-Race-Intelligence
│
├── app.py # Streamlit Controller (UI + orchestration)
├── src/
│ ├── data_loader.py # FastF1 session ingestion & caching
│ ├── metrics.py # Cleaning, modeling, analytics engine
│ ├── visualization.py # Plotly visualization layer
│
├── config.py # Thresholds & model parameters
├── requirements.txt
└── README.mdExperience the platform in action:
🔗 Streamlit Deployment:
https://f1-race-intelligence-qmzz4nuvmzql4hac6ajbbq.streamlit.app/
Fully functional production deployment with cached session loading and defensive input validation.
Lap Cleaning Strategy
• Convert timedelta → seconds
• Drop missing lap times
• Remove extreme outliers via quantile filtering
• Enforce minimum lap threshold
Degradation Metric
• Fit linear regression on LapNumber vs LapTimeSec
• Extract slope coefficient
• Interpret slope as tire decay rate
Delta Computation
• Merge lap times by lap number
• Compute lap delta
• Compute cumulative delta
• Plot race momentum curve
git clone https://github.com/akashcodes23/F1-Race-Intelligence.git
cd F1-Race-Intelligence
pip install -r requirements.txt
streamlit run app.py
This project demonstrates:
• Applied sports analytics
• Structured data engineering
• Statistical modeling foundations
• Interactive data storytelling
• Clean modular architecture
• Production-aware defensive programming
• Live cloud deployment (Streamlit Cloud)
Planned extensions:
• Pit Strategy Simulation Engine
• Lap Time Prediction Model (ML)
• Driver Consistency Index
• Stint-level segmentation modeling
• Strategy recommendation system
• AI-generated race summary module
• Multi-race comparative intelligence dashboard
The architecture already supports these extensions.
We welcome contributions in:
• Advanced modeling (time-series ML)
• Feature engineering optimization
• Visualization enhancements
• Strategy simulation frameworks
• Telemetry-level integration
Open an issue or submit a pull request.
Licensed under the MIT License, allowing full flexibility to reuse, modify, distribute, and integrate this project into personal or commercial applications. Attribution is required.
If you have questions, suggestions, or collaboration ideas, feel free to reach out at: 📩 akashgpatil23.05@gmail.com