Is Eurovision a Rigged Competition? Unveiling the Truth

Tools used in this project
Is Eurovision a Rigged Competition? Unveiling the Truth

About this project


On May 13, 2023, I, like countless others, gathered with loved ones to witness the annual extravaganza known as "Eurovision" or as I like to call it “my yearly homage to kitsch and silliness.” This musical showdown, featuring European nations and a few non-European ones such as Israel and Australia, has been an iconic television event for decades, recognized by Guinness World Records as the longest-running annual TV music competition. This year, France had a strong contender, raising hopes of victory, but once again, they fell short, leaving me pondering: Is Eurovision a fair competition? Is there a hidden bias in the voting process? Are there unspoken alliances influencing the results? Do global events play a role? This sparked my curiosity and led to the creation of my final Data Analysis Capstone project.

Data Collection:

Collecting Eurovision-related datasets from the internet, I realized that they varied in richness, complexity, and cleanliness. I selected five datasets and cleaned them as best I could using PowerQuery, albeit taking some shortcuts due to time constraints. While I managed to analyze the data and create most of the desired visualizations, I couldn't help but think that more time spent on data cleansing would have yielded better results. It's a valuable lesson for future projects.

Key Questions:

At the outset, I aimed to answer three key questions:

  1. What factors contribute to victory in Eurovision? Can we identify patterns that define a winning profile?
  2. Is there evidence of bias in the voting process? Can we unveil "Friendship Networks" or unspoken alliances?
  3. Are world events impacting the competition's outcome?

Project Approach:

Step 1 – Background and Fun Facts:

I introduced the Eurovision competition to my readers, especially those less familiar with it, and shared some fascinating tidbits. For instance, Eurovision dates back to 1956 and has seen 67 contests with 70 winners, including stars like Olivia Newton John, Elton John, Celine Dion, and Abba. Some as singers and some as song writers. Notably, Celine Dion and Abba's careers kickstarted with Eurovision. I also uncovered unique facts, like nearly half of Iceland's population tuning in every year for example. Did you know? I didn’t and it was fun to explore some of this data!

Step 2 – Winner Profile:

Since France lost again, I put myself in the shoes of a music producer and think about what information would be interesting to them to select who will be representing France next year and what type of song should they sing. In other words, could I use the past to derive a winning formula? To understand the winning formula, I constructed a "winner profile" examining categories such as soloists, duos, or bands, gender, languages used, and song characteristics according to criteria defined by Spotify. I limited this analysis to recent decades, considering the evolution of music styles.

Step 3 – Winner Profile vs. Country Comparison:

I created an interactive dashboard allowing users to select a country, view key statistics, and compare it against the "winner profile." Additionally, I explored which countries the chosen country voted for, revealing intriguing connections beyond linguistic or geographical ties. For example, I was expecting that France will mostly vote for French speaking countries like Belgium or Switzerland. To my surprise, this is not the case. Apparently, France voted the most for countries like Portugal and Israel! Why is that!? Well, if you know France a little bit, this is not too surprising and actually, explainable. France has a large Portuguese immigrants’ population especially in big cities like Paris. Similarly, France also has a large Jewish community with strong affinity with Israel. This was very interesting to me and triggered the ‘bias in voting?’, ‘Friendship Network?’, ‘Unspoken Alliances’ questions.

Step 4 – Voting Bias Tool:

Building upon the previous analysis, I delved into voting patterns, seeking unspoken alliances based on language, history, and geopolitics. An interactive map allowed users to see the top 10 countries that voted for a selected country, revealing interesting networks and connections.

For example, on the language front, there is a clear friendship network ... UK and Ireland vote for each other massively; Latin countries vote for each other massively (i.e., Spain, Portugal, Italy etc.) as well; Switzerland and Belgium vote massively for France etc.

On the history front, I could also identify obvious friendship network … For example, Russia seems to primarily vote for ex-USSR countries and vice versa. Cyprus and Greece favor each other as well. Scandinavian countries (i.e., Sweden, Denmark, Norway, Finland) seem to be attached to the hip! and many more …

On the geo-politics front, there are also some interesting findings. For example, Greece and Cyprus simply do not vote for Turkey! Probably due to the Turkish invasion of Cyprus. Similarly, Ukraine does not vote for Russia. Many countries also do not vote for Israel due to religion preferences.

Step 5 – World Events Impacting Competition Outcome:

Although time constraints limited my exploration of world events' impact, I identified instances where global events seemed to influence Eurovision results. For example, Ukraine's victory in 2022 coincided with the Russian invasion. I provided a few more examples in my analysis but I wish I had more time to delve deeper into this aspect.


In conclusion, Eurovision offers a rich dataset for exploration, yielding various insights and diverse interpretations. Ultimately, it is an art form, not a science. The competition's goal remains to celebrate talent and fun, serving as a symbol of unity in Europe after World War II. Despite any biases, alliances, or influences, the heart of Eurovision lies in embracing kitsch and silliness. Enjoy the artists, the music, the songs, and the show – that's what Eurovision is all about! 😊

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