🏎️ F1 Analytics

Formula 1 telemetry analysis, performance comparison, and race-pace prediction — powered by FastF1 and the custom f1analytics library.

Project Structure

F1/
├── src/
│   ├── f1analytics/          # Core analysis library (pip installable)
│   └── pipeline/             # Real-time telemetry capture & processing
├── 2024/                     # Season analysis notebooks
├── 2025/                     # Season analysis notebooks
├── 2026/                     # Pre-season testing notebooks
├── models/                   # Trained ML models (race pace prediction)
└── pyproject.toml            # Package config & dependencies

f1analytics Library

A numpy-style analysis toolkit — import f1analytics as f1a and access everything directly.

import fastf1
import f1analytics as f1a

session = fastf1.get_session(2025, "Monza", "Q")
session.load()

# Telemetry overlay with delta-time
fig, ax = f1a.Telemetry("Italian GP", 2025, "Q", session=session)\
    .compare_laps(drivers={"LEC": "fastest", "NOR": "fastest"})

# Track map colored by fastest driver per mini-sector
fig, ax = f1a.MinisectorComparator("Italian GP", 2025, "Q",
    session=session, drivers={"LEC": "fastest", "NOR": "fastest"})\
    .plot_track_map()

Available Classes

Class	Analysis
`Telemetry`	Full-lap telemetry overlay (Speed, Throttle, Brake, Gear, RPM, DRS)
`CornerAnalysis`	Zoomed corner telemetry with speed, throttle, brake subplots
`CornerTimeComparator`	Per-corner elapsed-time deltas
`CornerSpeedComparator`	Peak/min speed comparison across corners
`CornerMetricComparator`	Generalized per‑corner metric comparison
`MinisectorComparator`	Track map colored by fastest driver per segment
`DualThrottleComparisonVisualizer`	Parallel throttle heatmap traces
`RacePaceBoxplot`	Race-pace lap-time boxplots
`SectorDeltaPlotter`	Fastest sector times & deltas
`RacePaceAnalyzer`	ML-based race‑pace classification (Random Forest)

Cross-Session Comparison

Compare the same driver across different sessions:

fig, ax = f1a.Telemetry("Pre-Season", 2026, "Testing").compare_laps(
    laps=[
        (session_day5, "LEC", "fastest", "Day 5"),
        (session_day6, "LEC", "fastest", "Day 6"),
    ]
)

📖 Full API docs: src/f1analytics/README.md

Pipeline

Real-time UDP telemetry capture from the F1 game, with decoding and DataFrame construction.

pipeline/
├── capture/          # UDP packet capture (monitor_recording.py)
├── decode/           # Binary → structured data (decode_telemetry.py)
├── build_df/         # Structured data → analysis-ready DataFrames
├── load_session.py   # FastF1 session loader with caching
├── run_pipeline.py   # Orchestrates capture → decode → build
└── tests/            # Pipeline unit tests

📖 Full pipeline docs: src/pipeline/README.md

Setup

# Clone
git clone https://github.com/QuantumGhost98/F1.git
cd F1

# Install f1analytics + dependencies
pip install -e .

Requirements

Python ≥ 3.9
FastF1 ≥ 3.4
matplotlib, pandas, numpy, scipy, scikit-learn

Season Notebooks

Folder	Content
`2024/`	Race weekend analyses (telemetry, strategy, pace)
`2025/`	Full season analysis notebooks
`2026/`	Pre-season testing comparisons (cross-session, 2022 vs 2026)

License

Personal project by @QuantumGhost98.

QuantumGhost98/F1