This page will take you through the journey behind the Formula 1 project. It was the first big piece of work I've undertaken and I thought it was fitting to base it on Formula 1, a sport that I follow closely and have worked with in the past. My undergraduate dissertation modelled the relationship between driver and car and was eventually published in the Journal of Quantitative Analysis in Sport, read here.

Each component of the final viz deals with different aspects of the history & components that make Formula  1 one of the world’s most followed sports!

Screen Shot 2017-09-09 at 14.36.54.png
sankey 2.PNG
Wheel Dots.png
track map.png
Screen Shot 2017-09-09 at 14.32.44.png
Screen Shot 2017-09-09 at 14.33.14.png

A big thank you to the Ergast Developer API through which I was able to scrape all of the data I needed for the viz. I wrote a script in R to automate the process and performed some initial data cleansing and restructuring using the jsonlite package and several loops to export a data frame into a csv format, ready for Tableau to digest. I performed some further cleansing in Tableau to generate the ‘normalised points’ scoring system and get the data in the right format for some of the graphs. The normalised points system was required to provide a fairer approach to different era’s in which the number of points given for a race win has varied significantly over time. It was only partly successful in this, however, as the F1 calendar has also developed over time meaning modern drivers have the opportunity to score points in far more races than their predecessors. This will ultimately skew the points ranking to the modern era of Formula 1, though it was the best way of normalising the points system in my mind without over complicating it. I was not the first to try and compare drivers from different era’s and I certainly won’t be the last!

©2020 by sportschord