Dashboard Week day 5 – A glimpse into the Japanese Grand Prix

Coming to the end of Dashboard Week, on day 5 we worked with data on Formula One racing. Off we went back into the world of Tableau!

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See other posts in my Dashboard Week series:

• Dashboard Week day 1 – The Ascent of Starlink
• Dashboard Week day 2 – From the best to the not so good superannuation funds in Australia
• Dashboard Week day 3 – Can I park on the street near where I want to eat out? (City of Melbourne)
• Dashboard Week day 4 – The more you know, the happier you are?
• Dashboard Week day 5 – A glimpse into the Japanese Grand Prix

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I am not a fan of sports. Facing the Grand Prix data, I had feelings similar to when I worked on my viz for The Data School’s final interview in December. The data we had back then was on Big Bash League cricket games. Out of the two weeks I had to work on my viz, I spent the entire first week researching everything I could find about the BBL. If you’re curious, take a look at how that one turned out over on my Tableau Public profile.

Data exploration

On top of my professed unfamiliarity with Formula One, I was already both physically and mentally exhausted from earlier in the week. At this point I had worked on one viz each day for four days straight. Come Friday morning, I struggled immensely trying to pick apart the 14 tables in this Formula One dataset.

At the start, I planned to go through the fields within each of the tables and take note of which of these fields I may want to use to put together a story. However, with the sheer number of fields across all 14 tables and the limited time I had, it proved to be akin to boiling the ocean (as Coach David likes to say). While I did process and load all of the tables into Power BI (yes, that’s right) in an attempt to sort out some sort of data model, even this proved infeasible. As is evident from the spider web you see above – where only the primary and foreign keys are shown and many more fields have been hidden.

Deciding on the focus of my viz

All that is to say, for timeboxing purposes I decided to pick one particular Grand Prix to look into – the Japanese Grand Prix. Because I’ve always been fascinated by Japan (would love to go there someday!). In particular, I was intrigued by one of the three circuits in Japan, namely the Suzuka Circuit, since it’s known for being one of the more difficult circuits in Formula One racing.

Long story short, there wasn’t much of the story that I could find in the data within a span of a few hours on Friday, before presentation time hit at 3 pm. If I had more time I’d like to gather more data and compare the relative difficulties of the different circuits in Formula One racing, in terms of altitude, number of turns, etc.

And, naturally, it wouldn’t be me if I didn’t throw a spanner in the works by submitting myself to some sort of technical challenge in the course of things (no pun intended). This time I stumbled upon the spatial data for circuits and a tutorial on using the Google Maps API to retrieve elevation data, so I did just that for the Suzuka Circuit for a nice map at the bottom of my viz.

Alteryx workflow

Here’s my Alteryx workflow:

Tableau viz

Have a look at my viz on Tableau Public: