🏁 F1 Performance & Data Analytics Ecosystem

Developer: Thanasis Charalambous | Role: Data Analysis Engineer

A comprehensive suite of MATLAB-based engineering tools designed to extract competitive intelligence from Formula 1 telemetry and historical datasets. This repository tracks the evolution from historical database management to advanced, automated pre-season testing pipelines.

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📂 Project Portfolio

01. Formula 1 Race Data Analysis (Historical)

Focus: Database Merging & Strategy Visualization
This project analyzes and visualizes historical lap times and pit stop strategies using comprehensive CSV datasets.

• Features: Automated data loading; Year/Race filtering; Merging of driver, constructor, and lap data.
• Visuals: Lap time evolution plots with automated pit-stop markers for top finishers.
• Data Source: F1 World Championship Dataset (1950-Present) via Vopani/Kaggle.

02. 🏎️ Race Pace Audit: 2025 São Paulo GP (NOR)

Focus: Statistical Filtering & Fuel Normalization
A large-scale statistical analysis of Lando Norris's performance, applying 2-sigma filtering to isolate true racing pace.

• Fuel Correction Model: $T_{Corrected} = T_{Raw} - (L_{Remaining} \times 0.041575)$.
• 2-Sigma ($\sigma$) Filtering: Statistical outlier rejection ($\mu + 2\sigma$) to remove traffic and driver errors.
• Degradation Calculation: Linear regression on fuel-corrected times to find the tire wear coefficient in ms/lap.
• Results: Identified a negative degradation (-9.9 ms/lap) on the final Medium stint, indicating pace improvement as fuel decreased.

03. 📊 Practice Pace Analysis: 2025 Abu Dhabi GP

Focus: Multi-Driver Benchmarking & Title Decider Simulation
Comparative analysis of the three title contenders (NOR, VER, PIA) across Practice 1, 2, and 3 to predict the final qualifying and race hierarchy.

Pace Scenario	Compound	NOR	VER (Gap)	PIA (Gap)
QUALI SIMS	SOFT	1:23.083	+0.363	+0.510
RACE SIMS	MEDIUM	1:29.270	+0.407	+0.471

• Outputs: Generated stint-based scatter plots and Excel pace comparisons organized by tire life and compound.

04. 🏎️ 2026 Pre-Season Testing Analysis (Bahrain)

Focus: Automated Data Engineering Pipeline
A dual-stage pipeline that automates the processing of 144 hours of raw 2026 testing telemetry into executive-level insights.

• Dynamic Metadata Extraction: Scans CSV headers to automatically detect driver lineups, preventing manual mapping errors.
• Unified Aggregator: Synthesizes 6 days of testing into a "Global Source of Truth" report.
• 2026 Testing Pecking Order:
  • Peak Performance: Charles Leclerc (1:31.992)
  • Reliability: George Russell (349 Laps)
  • Race Pace Efficiency: Max Verstappen (0.073s/lap)

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🛠️ Technical Skillset & Methodology

Technique	Application	Formula / Model
Fuel Normalization	Performance isolation	$T_{Raw} - (Fuel \times Factor)$
Degradation Rate	Tire wear analysis	Linear Regression Slope
Outlier Rejection	Traffic/Error removal	2-Sigma ($\sigma$) Statistical Filter
Pace Smoothing	Trend visualization	4th-Degree Polynomial Regression

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🚀 How to Run

1. Race Analysis (01): Set dataFolder to your CSV path and choose a Race ID in MATLAB.
2. Post-Race Audit (02): Execute F1_Post_Race_Analysis.m to generate fuel-corrected stint reports.
3. Practice Analysis (03): Run F1_Pace_Analysis_Multiple_Drivers_FINAL.m for Abu Dhabi benchmarking.
4. Testing Pipeline (04): * Update DIRECTORY SETUP (Day X) in F1_Pace_Analysis_Preseason_2026.m.
   • Execute daily scripts, then run post_analysis_v4.m for global aggregation.

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Author: Thanasis Charalambous | Role: Data Analysis Engineer
Data Sources: Tracing Insights & Kaggle (Vopani).
Disclaimer: This repository is for educational and non-commercial purposes only.