How I created my viz

Analysing jury bias in the Eurovision song contest

When I was new to #dataviz, seeing only finalised projects by seasoned practitioners used to puzzle me: I always have thousands of questions on the process: how did you get there?
That’s why I document most of my projects, explaining the reasoning, design choices, technical hurdles etc.

How I have created this data visualisation

The idea

I have watched Eurovision since I am a kid; every year, my parents would root for their native country. And yet, it came as a surprise to me that Portugal won in 2017. There’s this idea that blocs of countries vote for each other.

Deprived of a multitude of neighbouring countries, and being snobbed by other Mediterranean/Latin countries, how could Portugal get enough points from other countries to win?

We kept losing, therefore the competition was unfair, right? How can we explain 2017?

The question and the methodology

How to quantify the impact of bloc voting and bias? This is a tough question and fortunately for me, many clever people, with solid statistical knowledge, have already studied the topic.

I thought more interesting to focus on jury voting, because the 2009 re-introduction of jury was an attempt to limit bias. We know that televoting is biased, but what about juries of music professionals?

I reached out to Alexander V. Mantzaris who wrote with Samuel R. Rein & Alexander D. Hopkins a paper on Preference and neglect amongst countries in the Eurovision Song Contest in 2018. Not only did Alexander Mantzaris kindly helped me understand the statistical model, he also refreshed the data with updates from 2018 and 2019.

The visual inspiration

From the beginning, I thought a hex map was the way to represent the different countries.

The following visualisations were great inspirations:
– the Eurovision voting alliances from Delayed gratification is a little jewel
– Maarten Lambrechts’ work on Eurovision and Google searches and his making off are well worth a reading
The Economist already covered the paper I mentioned. I absolutely love the simplicity they chose for their chart, but I wanted to show more data and only recent one, so I went for another visualisation.

The dataviz challenge

I wouldn’t have been able to create this viz in Tableau without
– the hex map without Daniel RowlandsTableau Tilemap generator
– on the Tableau community forum, Bryce Larsen has patiently answered all my questions and fixed my issues regarding unioning tables and required calculations .

After two months ogling at the same viz, I needed external feedback. Michelle Frayman and Zaks Geis have a weekly #vizofficehours ; they provided great tips and guidance on what to focus on. If you are in a “I know there’s something wrong but I cannot put my finger on it”, I would encourage you to attend.

Among other life savers, mentions to
– Andy Kriebel’s tutorial Using Parameter Actions to Choose a Chart Type (to allow switching from hex to classic maps)
– Kevin Flerlage Use Cases for Transparent Shapes & Images

Let me know if you have any feedback or question!

2 thoughts on “Analysing jury bias in the Eurovision song contest”

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