# theOehrly/Fast-F1

FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry

Repository: https://github.com/theOehrly/Fast-F1
Canonical: https://ross.abutalabs.com/products/fast-f1
Homepage: https://docs.fastf1.dev
Language: Python
License: MIT
License Family: permissive
Topics: formula1, datascience, motorsport
Last push: 2026-08-20T14:36:42+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 81, longevity 100
- inputs: {"age_days": 2328, "days_push": 13, "days_rel": 126, "gap_med": 14, "n_releases_24m": 16}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5331, forks 487 (observed 2026-08-28T04:09:15.682661+00:00)

## What it is
FastF1 is a Python package for accessing and analyzing Formula 1 data, including lap timing, car telemetry, tyre and weather data, event schedules, and session results. It wraps the Jolpica-F1 (Ergast-compatible) API and live timing data into extended Pandas DataFrames with Matplotlib plotting integration and request caching.

## Use cases
- analyze F1 lap times and sector times in pandas
- get Formula 1 car telemetry data in Python
- plot F1 race data with matplotlib
- fetch historical F1 race results and schedules
- analyze tyre and pit stop strategy data
- build an F1 strategy dashboard
- download F1 timing data from 2018 onwards

## When to choose
- you want F1 timing, telemetry, or results data as Pandas DataFrames
- you are doing motorsport data analysis or visualization in Python
- you need both current and historical F1 data via the Jolpica-F1/Ergast API
- you want caching to avoid re-downloading API data

## When to avoid
- you need real-time sub-second live telemetry streaming during a race
- you need timing/telemetry data before the 2018 season
- you need a non-Python environment (though an R wrapper, f1dataR, exists)
- you need officially licensed Formula 1 data

## Facets
- artifact type: library
- maturity: active
- function: data-science, data-visualization, caching, http-client, analytics
- domain: data-science, sports, analytics
- platform: python, cross-platform
- tags: formula1, f1, motorsport, telemetry, pandas, matplotlib, ergast, jolpica-f1, lap-timing, racing-data, python

## Member repositories
- theOehrly/Fast-F1 (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:15.682661+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:58:52.601026+00:00, confidence not recorded.
  - readme: https://github.com/theOehrly/Fast-F1 (fetched 2026-08-28T04:09:15.682661+00:00, sha a32fecc4445f)
  - homepage: https://docs.fastf1.dev (fetched 2026-08-29T08:53:26.781116+00:00, sha de6a460afdba)
  - site_page: https://docs.fastf1.dev/changelog/index.html (fetched 2026-08-29T08:53:26.790461+00:00, sha 45a3806edf23)
  - site_page: https://docs.fastf1.dev/api_reference/index.html (fetched 2026-08-29T08:53:26.792444+00:00, sha e653746b634e)
  - site_page: https://docs.fastf1.dev/data_reference/index.html (fetched 2026-08-29T08:53:26.794341+00:00, sha 806f01664cc2)
- Data as of 2026-08-30T08:39:29.467469+00:00.