Research

F1 Data Analysis

Telemetry, lap data, and race analytics using the FastF1 Python library

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5 technologies
0 key decisions
3 results

Problem

Problem

F1 telemetry data is rich — lap times, sector times, tyre compounds, pit stop windows, car speed traces, throttle, brake, DRS — but it lives in FastF1 / Ergast API formats that require wrangling before any meaningful analysis. The project explores race performance patterns, driver comparisons, and strategy analysis across sessions.

Approach

Approach

FastF1 provides structured access to official F1 timing data, car telemetry, and weather. The analysis pipeline loads session data, cleans lap and telemetry frames, and produces visualizations: lap time evolution over a race, driver speed traces overlaid by sector, compound degradation curves, pit window timing analysis, and qualifying pace comparison across the field.

Architecture

Architecture

F1 Data Analysis — system diagram

FastF1 APISession / Lap / Te…Data Cleaning + Fe…Matplotlib Visuali…

Key Technical Decisions

Key Technical Decisions

Assembly Instructions — 0 Steps

Results

Results

  • Lap time evolution, sector analysis, and tyre compound degradation curves
  • Driver head-to-head telemetry overlays (speed, throttle, brake by sector)
  • Pit window strategy analysis and qualifying pace comparison across the field

Tech Stack

Tech Stack

PythonFastF1PandasMatplotlibJupyter

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