RaceMind

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  Tire & Grip Alerts

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  Top-Down GR86 Tire Heat Map (psi / temp / health)

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  Track Sector Stress Map

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  Pit Stop Loss & Risk Tool

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  RaceMind — Race HUD (Esports Mode)

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  Race Map — Live & Replay

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  Telemetry & Driver Behaviour

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  Track & Weather Impact

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  Strategy & Pit Window

RaceMind was inspired by professional motorsport engineering tools and the challenge of turning raw GR Cup telemetry into something any esports driver can use. I built it using Python and Streamlit, combining real lap reconstruction, tire-wear modeling, fuel-stint simulation, safety-car logic, and a fully custom SVG track-map engine. Along the way, I learned how teams analyze tire degradation, pace loss, undercut/overcut strategy, and driver aggression using data. The hardest parts were repairing corrupted lap timestamps, designing a believable but lightweight wear model, and making smooth real-time animations inside Streamlit. Despite challenges with inconsistent telemetry, rendering issues, and synchronizing live vs playback modes, RaceMind now delivers a complete engineering dashboard—replaying laps, predicting pit windows, visualizing sector stress, and providing AI-powered pit advice all in one streamlined interface.

Built With

• analytics
• and
• and-data-cleanup-?-svg-+-html/css-?-custom-rendered-track-maps
• and-hud-visuals-?-streamlit-components-?-embedded-dynamic-html-for-live-car-animation-?-matplotlib/charts-(streamlit-built-ins)-?-sector-stress-and-telemetry-plots-?-randomized-simulation-models-?-tire-wear
• and-telemetry-processing-?-streamlit-?-interactive-ui-framework-for-building-the-entire-dashboard-?-numpy-&-pandas-?-statistical-modeling
• csv
• cup
• fuel-stints
• g-force-behavior
• gr
• lap
• lap-reconstruction
• local
• probability
• racemind-was-developed-using-a-focused-stack-of-modern-and-lightweight-technologies:-?-python-?-core-logic
• sc
• simulations
• telemetry
• tire-heat-rings