MSARSE [Micro-Sector Adaptive Racing Strategy Engine]

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  Track Map

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  Top Time-Loss Sectors

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  Telemetry Traces(Speed)

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  Telemetry Traces(Throttle)

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  Telemetry Traces(Brakes)

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  Telemetry Traces with 5 laps

Inspiration

"You don't lose a race in a sector. You lose it in the 30 meters before the apex." We were inspired by a fundamental problem in racing analytics: traditional tools tell you you're slow in "Sector 2," but they can't tell you WHERE in Sector 2. Is it the corner entry? The apex? The exit? The braking zone? Professional race teams spend hours manually reviewing telemetry to find these answers. Amateur racers and sim racers have no tools at all. We realized that by combining GPS telemetry with track geometry, we could automatically generate "micro-sectors" - 30-meter segments that reveal exactly where drivers lose time. The inspiration came from working with real COTA (Circuit of the Americas) telemetry data and seeing the gap between what current tools provide (3 sectors) and what drivers actually need (185 micro-sectors). We wanted to democratize precision racing analytics and make it open source.

What it does

MSARSE (Micro-Sector Adaptive Racing Strategy Engine) is the first open-source tool that automatically generates micro-sectors from GPS telemetry to reveal exactly where drivers lose time.

Key Features: Dynamic Micro-Sector Generation: Automatically divides any race track into ~30-meter segments using GPS traces - no manual track mapping needed GPS + Track Geometry Fusion: Combines GPS coordinates with track geometry to understand corner locations, racing lines, and track layout Granular Performance Analysis: Breaks down a 5.5km track into 185 micro-sectors (vs traditional 3 sectors), providing 66x better resolution Actionable Intelligence: Instead of "you're slow in Sector 2," MSARSE tells you: "You're losing 0.15s in micro-sector 47 (Turn 12 apex) - you're carrying 8 km/h less speed than your fastest lap"

Multiple Interfaces: Interactive Streamlit dashboard with color-coded track maps Command-line interface for batch processing Python API for custom analytics Real-World Impact: Analyzes a 10-lap session in under 2 seconds, with GPS accuracy to ±0.5 meters, handling 100,000+ telemetry points.

How we built it

Technology Stack: Python 3.10+ for core development pandas & numpy for high-performance data processing geopy for GPS distance calculations (Haversine formula) streamlit, plotly, folium for interactive visualizations pytest & hypothesis for comprehensive testing (>85% coverage)

Architecture (Modular Pipeline): TelemetryLoader: Validates and loads CSV telemetry data (GPS, speed, throttle, brake) DistanceCalculator: Converts GPS lat/lon to track distance using Haversine formula, handles GPS drift and normalization MicroSectorEngine: The innovation core - dynamically segments tracks into 30m micro-sectors and maps telemetry points to sectors LapComparator: Identifies fastest lap and calculates time deltas per micro-sector VisualizationEngine: Generates color-coded track maps (green=fast, red=slow) and telemetry overlays MSARSEPipeline: Orchestrates the complete workflow

Development Process: Started with COTA telemetry data analysis Built modular components with clear separation of concerns Implemented comprehensive testing (unit tests + property-based tests) Created multiple interfaces (dashboard, CLI, API) Developed data conversion scripts for real racing data Built complete documentation ecosystem

Challenges we ran into

GPS Accuracy & Drift: GPS has ±5-10m accuracy and drifts over time. We solved this with Haversine distance calculation + track length normalization to scale distances to known track length. Track Geometry Understanding: Without manual track mapping, how do we understand corners vs straights? Solution: GPS traces naturally reveal track geometry - tight curves show up as rapid direction changes. Temporal Alignment: Telemetry is sampled at different rates (GPS at 10Hz, speed at 100Hz). We used timestamp-based interpolation to align all data streams. Performance at Scale: Processing 100,000+ telemetry points needed optimization. We used vectorized pandas operations and efficient distance calculations. Data Format Conversion: Real COTA data was in long format (one row per parameter). We built conversion scripts with synthetic GPS generation for data without coordinates. Visualization Complexity: Creating interactive, color-coded track maps that sync with telemetry charts required careful coordination between folium, plotly, and streamlit. Making it Accessible: Racing analytics is complex - we needed to make it usable for everyone from professional engineers to sim racers. Solution: multiple interfaces and comprehensive documentation.

Accomplishments that we're proud of

The Core Innovation: We're the first to dynamically generate micro-sectors from GPS telemetry in the open-source space. This is genuinely novel.

66x Better Resolution: Traditional tools give 3 sectors (~33% resolution). MSARSE gives 185 micro-sectors (~0.5% resolution). That's a 66x improvement in analytical precision.

Production-Ready Quality: 85% test coverage with unit + property-based tests Comprehensive error handling Professional documentation ecosystem Multiple interfaces (dashboard, CLI, API) Real Data Processing: Successfully processed actual COTA race telemetry with 100,000+ data points, proving it works with real-world data.

Performance: Analyzes a 10-lap session in <2 seconds with ±0.5m GPS accuracy.

Complete Package: Not just code - we built documentation, examples, conversion scripts, sample data, contribution guidelines, and MIT licensing.

Accessibility: Made precision racing analytics free and open source - previously only available in $10,000+/year proprietary systems.

What we learned

GPS Mathematics: Deep dive into Haversine formula, coordinate systems, and handling GPS drift/errors in real-world conditions.

Racing Analytics Domain: Understanding how race engineers actually analyze data, what feedback drivers need, and the gap in current tools.

Data Pipeline Architecture: Building modular, testable components that work together seamlessly while remaining independently useful.

Property-Based Testing: Using Hypothesis to test universal properties (monotonic distances, complete sector coverage) rather than just specific examples.

Visualization Complexity: Synchronizing multiple visualization libraries (folium for maps, plotly for charts, streamlit for UI) is non-trivial.

Documentation Matters: A revolutionary tool is useless if people can't understand it. We learned to tell a compelling story about the problem and solution.

Real-World Data is Messy: Handling missing GPS coordinates, out-of-range values, format inconsistencies - production data is never clean.

Open Source Best Practices: Proper licensing (MIT), contribution guidelines, code of conduct, and welcoming documentation for community growth.

What's next for MSARSE [Micro-Sector Adaptive Racing Strategy Engine]

Short-term (v0.2.0) Real-time Telemetry Streaming: WebSocket-based live race analysis Multi-Track Support: Pre-configured profiles for Silverstone, Spa, Monza, Laguna Seca Advanced Visualizations: 3D track maps, heatmaps, racing line overlays Driver Comparison Mode: Compare two drivers at micro-sector granularity Historical Trend Analysis: Track improvement over time

Medium-term (v0.3.0) Machine Learning: Optimal racing line prediction based on telemetry patterns Weather Integration: Adjust analysis for track conditions (wet/dry, temperature) Tire Degradation Modeling: Factor in tire wear over stint Live Timing Integration: Connect to race timing systems Enhanced Analytics: Fuel load compensation, brake temperature analysis

Long-term (v1.0.0) Mobile App: Trackside analysis on tablets/phones Cloud Processing: Handle massive datasets in the cloud Team Collaboration: Multi-user features for race teams Video Overlay Export: Generate video overlays for coaching Professional Features: Advanced setup comparison, driver coaching AI

Community Growth Track Profile Library: Community-contributed track configurations Plugin System: Allow custom analysis modules Integration APIs: Connect with sim racing platforms (iRacing, ACC) Educational Content: Tutorials, case studies, racing analytics courses Partnerships: Work with racing teams, sim racing leagues, educational institutions Vision: Make MSARSE the standard for open-source racing analytics, democratizing precision performance analysis for everyone from professional teams to weekend warriors.

Built With

• csv
• folium
• geopy
• git
• github
• haversine-algorithm
• hypothesis
• numpy
• pandas
• plotly
• pytest
• python-3.10+
• streamlit