Will Zandvoort make Formula 1 exciting again?

Category: Best visualization (small and large newsrooms)

Country/area: Netherlands

Organisation: NOS

Organisation size: Big

Publication date: 7 Dec 2019

Credit: Arnoud van der Struijk, Lars Boogaard, Ruben Sibon, Wiel Wijnen, Jurjen IJsseldijk, Emil van Oers, Ben Prins

Project description:

According to the race results, Formula 1 isn’t the most exciting sport to watch. Four out of five races in the last five years were won from the front row. We asked ourselves why overtaking in Formula 1 is so hard and if this might be influenced by the layout of the circuit. Because such circuit data isn’t available, we made our own dataset measuring every Formula-1 circuit by hand. We found out that Zandvoort, the new Dutch grand prix, will be one of the shortest and most tortuous races on the calendar with little opportunities to overtake.

Impact reached:

We have published the story on multiple platforms, with a visual longread and a Youtube video as main products. Besides that, we published several news articles, posted on Instagram and we made a podcast about it.

Our explainer video on Youtube reached nearly 800.000 views. The video appeared in Youtube’s ‘trending’ videos in The Netherlands (as #1), which means that the Youtube algorithm noticed the engagement, caused by our video. In the comment section, our viewers posted more than 1.300 comments and started a discussion about the topic. The majority of our viewers were under 35 years old, so we succeeded in reaching a young audience with our investigation.

The visual longread didn’t reach that much readers (almost 70.000), but the readers stayed on our page for more than 8 minutes. Comparing to the attention span of our young target audience, we think that’s a lot. The feedback we got on our investigation was mainly positive. We expected that autosport enthousiasts would like our story, but we were surprised with the positive feedback from people who say they aren’t interested in Formula 1 at all. Our visual approach made them better understand Formula 1, they wrote us.

Techniques/technologies used:

Because the data we needed didn’t exist, we created our own dataset. It required a lot of hand work: we needed to measure every circuit by hand. We started by measuring the circuit’s length and width using Google Earth. But to measure the turns, we had to go a bit more analog than we were used to: we measured every turn of every circuit using a set square.

We aggregated and analyzed the data with Excel and Google Spreadsheets, by which we created two datasets: one of every circuit (total length, number of turns, length of DRS-zones etc.) and one of every corner: length, width and angle.

We combined text, illustrations, infographics and data visualizations to make sure that every part of story would be told in the best possible way. We created the visualizations and illustrations with Adobe Illustrator and Adobe Photoshop. The visual longread was build with Vue.js, the GSAP Animation Suite and Scrollmagic.js. The video graphics were made with Adobe After Effects.

What was the hardest part of this project?

The hardest part of this project was the absence of data about the circuits. To prove our hypothesis (the circuit layout influences the possibility to overtake), we had to measure every circuit by hand. That took us a few days but resulted in a unique dataset. But when we combined all the data, we didn’t find the correlation we hoped for. A difficult moment in our proces, because we thought that all our measuring might be for nothing. On the other hand, the (ex-)racers we interviewed, told us that overtaking is harder on a tortuous circuit. It made us realize that measuring the circuit can be done in many ways. Besides that, a Formula 1 race can be influenced by a lot of circumstances. The weather, for instance.

We chose to add these other circumstances, like the weather and the safety car, to our research. Combined with the interviews with racers, we concluded that it was still possible to tell our story, but that datavisualization itself wasn’t enough. For some parts of the story, like the perfect race line and the DRS-effect, we chose to tell the story with explanatory infographics.

What can others learn from this project?

We think that the absence of data shouldn’t be a reason to kill an idea. In some instances, aggregating your own data is worth the investment. And sometimes the story requires methods that can’t be done with Python, QGIS, R or Excel, but forces you to go analog.

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