tif1

A fast, canonical Formula 1 data library fetched from TracingInsights data https://github.com/TracingInsights/2026 (2018-current).

Data is available ~30 minutes after the session ends. Data in fastf1 is available 20-25 minutes after the session ends. So there is a slightly longer delay of 25 minutes for data availability in tif1 compared to fastf1, but this is because tif1 has more data and does more processing to enrich the data before making it available.

Installation

pip install tif1

Optional Dependencies

# For development
pip install tif1[dev]

Quick Start

import tif1

# Get available events for a year
events = tif1.get_events(2025)
print(events)  # ['Australian Grand Prix', 'Chinese Grand Prix', ...]

# Get sessions for an event
sessions = tif1.get_sessions(2025, "Chinese Grand Prix")
print(sessions)  # ['Practice 1', 'Sprint Qualifying', 'Sprint', 'Qualifying', 'Race']

# Get a session
session = tif1.get_session(2021, "Belgian Grand Prix", "Race")

# Get all drivers as DataFrame
print(session.drivers_df)

# Get all laps
laps = session.laps
print(laps.head())

# Get specific driver
ver = session.get_driver("VER")
ver_laps = ver.laps

# Get telemetry for a specific lap
lap = ver.get_lap(19)
telemetry = lap.telemetry
print(telemetry[["Time", "Speed", "Throttle"]].head())

Features

• Fast: Multi-CDN access via StaticDelivr (primary) and jsDelivr (fallback) with automatic failover and SQLite caching
• No need to session.load() - only the required data is fetched when necessary. You can just get the telemetry data of any specific lap within seconds.
• Canonical: Focused tif1 API surface
• Complete: Lap times, sectors, telemetry, tire compounds, 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
• Optimized: SQLite cache with JSON storage
• Flexible: Supports both pandas and polars backends
• Validated: Optional data validation with Pydantic models
• Configurable: .tif1rc configuration file support
• Type-Safe: Comprehensive type hints for IDE support
• Jupyter-Ready: Rich HTML display in notebooks

Advanced Usage

Canonical API Notes

• tif1 exposes canonical data/session APIs directly (get_session, Session, Laps, Lap, Driver).
• Event/session schedules are loaded from packaged JSON assets and schema-validated at runtime.

Configuration File

import tif1

# Create ~/.tif1rc with your settings
# {
#   "max_retries": 5,
#   "validate_data": true,
#   "backend": "polars",
#   "cdns": [
#     "https://cdn.staticdelivr.com/gh/TracingInsights",
#     "https://cdn.jsdelivr.net/gh/TracingInsights"
#   ],
#   "cdn_use_minification": false
# }

# Get configuration
config = tif1.get_config()
print(f"Max retries: {config.get('max_retries')}")

# Set configuration
config.set("validate_data", True)
config.save()

Circuit Breaker & CDN Fallback

import tif1

# Check circuit breaker status
cb = tif1.get_circuit_breaker()
print(f"Circuit breaker: {cb.state}")

# Check CDN sources
cdn = tif1.get_cdn_manager()
sources = cdn.get_sources()
print(f"Available CDNs: {len(sources)}")

# Reset if needed
if cb.state == "open":
    tif1.reset_circuit_breaker()

Fastest Laps & Telemetry (Optimized for Speed)

import tif1

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")

# Get fastest lap per driver
fastest_by_driver = session.get_fastest_laps(by_driver=True)
# Pandas backend: LapTime is Timedelta, LapTimeSeconds is numeric seconds
print(fastest_by_driver[["Driver", "LapTime", "LapTimeSeconds"]].head())

# Get overall fastest lap
overall_fastest = session.get_fastest_laps(by_driver=False)

# Get driver's fastest lap
ver = session.get_driver("VER")
ver_fastest = ver.get_fastest_lap()

# Get telemetry from overall fastest lap
fastest_tel = session.get_fastest_lap_tel()  # ~1.3s

# Get telemetry from each driver's fastest lap (parallel fetching!)
fastest_tels = session.get_fastest_laps_tels(by_driver=True)  # ~0.4s for 19 drivers

# Get telemetry for specific drivers' fastest laps
top3_tels = session.get_fastest_laps_tels(by_driver=True, drivers=["VER", "HAM", "LEC"])  # ~0.13s

# Get specific driver's fastest lap telemetry
ver_fastest_tel = ver.get_fastest_lap_tel()  # ~0.08s

Logging

import tif1
import logging

# Enable debug logging
tif1.setup_logging(logging.DEBUG)

session = tif1.get_session(2021, "Belgian Grand Prix", "Race")

Cache Management

import tif1

# Get cache instance (SQLite)
cache = tif1.get_cache()
print(f"Cache location: {cache.cache_dir}")

# Clear cache
cache.clear()

# Disable caching for a session
session = tif1.get_session(2021, "Belgian Grand Prix", "Race", enable_cache=False)

Error Handling

import tif1

try:
    session = tif1.get_session(2025, "Invalid GP", "Practice 1")
    laps = session.laps
except tif1.DataNotFoundError:
    print("Data not available")
except tif1.NetworkError:
    print("Network error occurred")
except tif1.InvalidDataError:
    print("Data is corrupted")

Data Available

• Lap times and sectors (S1, S2, S3)
  • LapTime is canonicalized as Timedelta on pandas backends
  • LapTimeSeconds is provided as a numeric helper for sorting/comparisons
• Tire compounds and stint information
• Telemetry: speed, throttle, brake, RPM, gear, DRS
• Position data (X, Y, Z coordinates)
• Acceleration data (X, Y, Z axes)
• Distance and relative distance
• Unique data identifiers

Session Types

• Practice 1, Practice 2, Practice 3
• Qualifying
• Sprint, Sprint Qualifying, Sprint Shootout
• Race

Development

# Install with uv
uv sync --all-extras

# Install git hooks with prek (commit + push)
uv run prek install

# Run the full local quality gate (same checks as CI lint job)
uv run prek run --all-files

# Run example
uv run python examples/basic_usage.py

# Run tests (parallel by default via xdist)
uv run pytest tests/ -v

# Run linting
uv run ruff check src/ tests/

# Run type checking
uv run ty check src/tif1

# Run benchmarks (serial for stable timing)
uv run pytest -o addopts='' tests/test_benchmarks.py -v -m benchmark --benchmark-only --no-cov -n 0

Documentation

Full documentation available at: docs.tracinginsights.com

Contributing

Contributions are welcome! A few ideas what you can contribute:

Improve documentation.
Add more tests.
Improve performance.
Found a bug? Fix it!
Made an article about tif1? Great! Let's add it into the README.md.
Don't have time to code? No worries! Just tell your friends and subscribers about the project. More users -> more contributors -> more cool features.

License

MIT License - see LICENSE file for details.

What's available from fastf1

Verified against docs.fastf1.dev (FastF1 API reference) and current tif1 code/tests.

Verification basis:

• FastF1 docs: session/event API, core.Session, core.Laps, core.Lap, core.Telemetry

Status key:

• supported: implemented with compatible behavior
• partial: implemented with simplified/placeholder behavior
• missing: not implemented yet

Top-level and event/session API

core.Session

core.Laps, core.Lap, core.Telemetry

What's planned to be added in tif1 from fastf1

High-priority API gaps for parity with docs.fastf1.dev:

• get_testing_session
• get_testing_event
• get_events_remaining
• richer get_event_schedule parity
• deeper parity for Session metadata/classification fields

What's out of scope for tif1 from fastf1

• Ergast (fastf1.ergast)
• LiveTiming (fastf1.livetiming)

Full detailed matrix: docs/fastf1_compliance_matrix.md