As an analyst and motorsport enthusiast looking to develop my analytical skills, the idea of getting my hands on real-world telemetry and historic races results is a dream come true. So, as I began to explore the world of data around me, I couldn’t wait to get stuck into some motorsport data. With that in mind, I set out on a journey through the internet to find the data sources I needed to help me scratch that itch.

I set my sights of Formula 1, the pinnacle of motorsport, and something that I have followed closely since I was a young child. Even back then, live timing and race positions were shown on screen, giving me some key data without having to look away from the action. Since then, the telemetry available to the viewer has increased, with new visuals and more data available in real-time. That’s wonderful for a data-centric viewer, but it’s only there for a few moments. I wanted to be able to dig deeper into this data, unearthing new and exciting insights!

My journey through the internet led me to two incredible resources, the Ergast database and developer API, and the FastF1 python package. Both resources have excellent documentation, meaning that even those who are relatively new to APIs or have minimal coding experience could get to grips with the base functions with just a little time and effort. Once you’re set up, these two resources provide a wonderful opportunity for motorsport fans to dive headfirst into telemetry and race data.



The Ergast Developer API is a web service which provides a historical record of the Formula One the series.

While in doesn’t include telemetry data, it has all the results data your heart could desire! It provides data from the series’ inception in 1950 to date, and it is updated shortly after each new race is completed.

The database can be queried via multiple methods, with each method having detailed documentation. It has popular formats, such as json and xml outputs, as well as SQL database images available for download. For non-programmers, there is a manual interface or the database can be download in a csv format.


FastF1 (Python package)

The use of FastF1 does require a little Python knowledge, but if you’re keen to learn, it’s a great place to develop your skills! I had minimal knowledge prior to writing and running my first script with using FastF1, and you can get a lot of telemetry data with only a few lines of code! The documentation is excellent, with some great examples to get you up and running.

FastF1 uses two sources:

  • The Ergast API (mentioned above)
  • The Official F1 data stream

The package allows you to access car telemetry, lap timing, positional data, weather data, session results, and much more. It’s a real goldmine for F1 data, and we’ll be making great use of it in future blogs!


Other Blogs in This Series

Data in Motorsport: Acquiring Formula 1 Telemetry Data using Python and FastF1

Data in Motorsport: Creating a Race Track Visualization in Tableau

Jonathan Carter
Author: Jonathan Carter