F1 Data Analysis : Insights into Race Simulation & Telemetry

This course addresses a wide range of audience , this includes but is not limited to students new to this field and professionals who already have some experience in the wider automotive field and would like to know how the same concepts are applied in motorsports / F1.

This course is primarily taught by Ian Wright whose worked in motorsports for more than 20 years and previously was the Head of Engineering at Mercedes F1 Team.

** Upon completing this course and publishing your certificate on LinkedIn you will receive access to a REAL Data-set from one of the Motorsports team that you can use to practice and build your own race engineering tools**

What's this course all about ? Let's begin !!

Section 1: Data is the Gold Mine in F1, but WHY?

In this opening section, you’ll discover how data forms the bedrock of modern Formula One operations. You’ll learn the fundamentals of data gathering—from transponder technology to in-race sampling—and see how every fraction of a second counts on track. This section also explores the critical role that data plays in ensuring both performance and safety, culminating in a discussion on the real-world challenges of working with vast, fast-paced information streams.

Section 2: Know Your Sensors!!

Sensors are the eyes and ears of an F1 car, capturing everything from aero forces and temperatures to high-pressure impacts and chassis loads. Here, we demystify sensor technology: how each sensor type works, what data it outputs, and how engineers process this information to fine-tune car setups. You’ll also analyze real-world case studies—like the “Vegas” example—to see how teams leverage sensor data for optimal performance and rapid troubleshooting.

Section 3: All Those Squiggly Lines Mean Something!!

Telemetry traces may look like random squiggles, but they contain invaluable insights. In this section, you’ll learn how F1 teams use specialized software (e.g., ATLAS and RaceWatch) to visualize and interpret these streams of information in real time. You’ll tackle time-based vs. distance-based data analysis, pit-loss assessments, and see how this immediate feedback loop empowers teams to make winning decisions. Discussions center on interpreting data for race strategists—and how these same skills translate to motorsport data engineering roles.

Section 4: How Do You Simulate a Car Around a Track?

Before a wheel even touches the track, teams rely on advanced simulation to predict performance. This section introduces you to lap time simulation, from quasi-static to fully dynamic models, explaining the math and programming techniques that drive them. You’ll learn how tyre specs, track profiles, and environmental variables feed into these simulations—and how the results guide vital engineering choices. A fireside chat provides expert perspectives on global collaboration, model validation, and how you can start building or refining simulations of your own.

Section 5: ChassisSim – The Race Engineering Tool That Was Developed Before You Were Born

ChassisSim is a cornerstone software tool used by race engineers for decades. Here, you’ll get hands-on exposure to its WatchLog feature, learning to interpret simulation outputs and assess car behavior under different setups. By analyzing real data logs, you’ll see how changes to aero, tyres, and suspension directly impact lap times. Lively group discussions bring the theory to life, showing you how professionals tweak the virtual model to align with on-track outcomes.

Section 6: Plan A, B, C, D, E, F-Ferrari

When it comes to race day, strategy is everything—sometimes leading all the way to Plan F (Ferrari jokes included!). Building on your data skills, you’ll learn to filter and process vast amounts of real-time information, from logging rates to advanced track modeling. Delve into tyre performance and energy usage, then see how Monte Carlo simulations help predict race scenarios and inform pit strategies. This section also covers inertial measurement units (IMUs) and the deeper insights they provide on car dynamics throughout the race.

Section 7: How Am I Being Beaten on Track?

In the final section, you’ll look at competitor analysis to understand where your rivals have the edge. A deep dive into Red Bull Racing vs. McLaren case studies shows how teams monitor each other’s progress, refine strategies, and drive development decisions. A closing fireside chat synthesizes all you’ve learned across the course, offering future trends, career guidance, and practical takeaways to ensure that you’re always on the cutting edge of F1 data analysis.

Section 8 : Additional Data Set for those who complete the course
Complete the course and update your certificate on LinkedIn to gain access to a validated racing data set that can be used to develop your own telemetry analysis tools in python or MATLAB.