🏎️ F1 Analytics

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

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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

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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

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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

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Setup

# Clone
git clone https://github.com/ppm98dev/F1.git
cd F1

# Install f1analytics + dependencies
pip install -e .

Requirements

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

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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)

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License

Personal project by @ppm98dev.