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Welcome to tif1

tif1 is a high-performance Python library for Formula 1 data analysis. It provides access to timing, telemetry, and weather data from TracingInsights (2018-current). tif1 is a faster alternative to fastf1 with the same API bindings. The speed increase comes from asynchronous data loading, HTTP/2 multiplexing, and optimized data backends.

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Why tif1?

Fast

Parallel loading is more than 3x faster than sequential fetching (benchmark-validated). It uses HTTP/2 multiplexing.

API Compatible

The bindings match fastf1. Migration needs few code changes.

Flexible Backends

Choose Pandas for compatibility, or Polars for about 2x faster processing.

Multi-tier Caching

Two cache tiers: an in-memory LRU cache and a SQLite cache for persistent storage.

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Why tif1 over fastf1?

fastf1 is an established library at the center of the F1 data community. tif1 started as a personal project with a different approach in a few areas:
  1. Fetch only the necessary data. Do not use session.load() and do not download a full weekend. Pull one lap of telemetry for one driver in seconds. This costs less time, disk, and bandwidth.
  2. No API rate limits. The upstream API behind fastf1 (jolpica-f1) caps unauthenticated use at 500 requests/hour (4 req/s burst). The tif1 CDN serves static files with no quotas, no keys, and no throttling.
  3. Charts included. 22 optional one-call plot_*() functions turn raw data into finished charts: tif1.plot_top_speeds(...), tif1.plot_driver_laptimes(...), and more.
  4. Works from any IP address. The tif1 CDN works from home, cloud, CI, and notebook environments. It needs no VPN and no residential IP address.
  5. More data. Mini-sector data, per-lap weather, and derived telemetry channels beyond the current fastf1 scope.

Full comparison

Read the “Why tif1 instead of fastf1?” page. It includes the verified 500 requests/hour rate-limit sources.

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Key Features

  • Fast: Multi-CDN access (jsDelivr, Hugging Face buckets, and StaticDelivr) with automatic fallback and SQLite caching
  • Complete: Lap times, sectors, telemetry, tire compounds, weather, and more
  • Historical: Data from 2018-current
  • Reliable: Automatic retry logic with circuit breaker and multi-CDN fallback
  • Async: Parallel data fetching for better performance
  • HTTP/2: Uses niquests for multiplexed, connection-efficient network requests
  • Flexible: Supports both pandas and polars backends
  • Type-Safe: Comprehensive type hints for IDE support
  • Jupyter-Ready: Rich HTML display in notebooks
  • Native Charts: 22 one-call chart functions in tif1.charts for track maps, telemetry comparisons, and performance analysis

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Performance Comparison

Benchmark-suite-validated results (2026-09-02, uv run pytest tests/benchmarks/ -m benchmark): parallel fetch is more than 3x faster than sequential fetch. The cache-hit read path is about 20x faster than the legacy path (109 ms to 5.3 ms median). The fastest-laps cold path is about 2.4x faster than the legacy path. Numbers vary by machine and network. Run the benchmark suite to reproduce them.

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Core Architecture

tif1 uses a modern networking stack with multi-CDN support and a multi-tier cache. This design decreases the time spent waiting for data.

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Data Available

  • Lap times and sectors (S1, S2, S3) with LapTime as Timedelta and LapTimeSeconds as numeric helper
  • Tire compounds and stint information
  • Telemetry: speed, throttle, brake, RPM, gear, DRS
  • Position data (X, Y, Z coordinates)
  • Acceleration data (X, Y, Z axes)
  • Weather data automatically included in laps
  • Driver metadata with team colors and headshots
  • Race control messages

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Next Steps

Why tif1?

Why choose tif1 instead of fastf1?

Installation

Install tif1 and verify the installation.

Quickstart

Load a first session in less than 30 seconds.

Examples

Common patterns and code examples.

Charts

Visualize F1 data with 22 one-call chart functions.

Data Reference

Every field in the raw TracingInsights data files, with explanations.

Migration Guide

Coming from fastf1? See the differences.

API Reference

Explore the complete tif1 API.

Tutorials

Learn with detailed analysis examples.

Installation

Install tif1

Why tif1?

Why tif1 instead of fastf1?

Quickstart

Load a first session

API Reference

Explore the API

Charts API

Native chart functions

Tutorials

Learn with examples
Last modified on September 10, 2026