Eurovision is full of surprises—not just in performances but also in how different countries score. A song might be a fan favorite with high televote support yet receive low jury points, or certain musical styles might consistently have higher energy levels than others. In this week’s challenge, you’ll explore key aspects of Eurovision scoring and song characteristics using KNIME. Among all songs that qualified for the final, which style has the highest average BPM?
Here is the challenge. Let’s use this thread to post our solutions to it, which should be uploaded to your public KNIME Hub spaces with tag JKISeason4-18 .
Need help with tags? To add tag JKISeason4-18 to your workflow, go to the description panel in KNIME Analytics Platform, click the pencil to edit it, and you will see the option for adding tags right there. Let us know if you have any problems!
Step 1 – Data preparation
Imported the dataset, converted key columns to numeric, and made sure dates and numbers behaved properly (because nothing ruins a party like misbehaving data ) corrected some wrong qualified_10 missing (thanks Maneskin for reminding me your victory).
Step 2 – Biggest televote vs. jury gap
Calculated the difference between final_televote_points and final_jury_points. After sorting, the country with the widest/lowest gap stood out clearly — proving once again that the jury and the public don’t always listen to the same tune.
Step 3 – BPM insights for finalists
Filtered for qualified_10 = 1, grouped by style, and computed average BPM. This revealed which style keeps the tempo the highest when it matters most. Let’s just say… some genres really know how to keep the audience on their feet .
Step 4 – Visual storytelling
Wrapped everything into a component with tables and bar charts, so results are easy to explore. I also added some extra KPIs (like average televote/jury scores per country, and distributions of BPM by style) for more context.
Takeaway
The exercise was a great reminder that Eurovision isn’t only about the music on stage, but also about the fascinating dynamics behind the numbers.
Looking forward to hearing how others approached this — I’m sure there are some creative “remixes” out there!
Our solution to last week’s JustKNIMEIt challenge is out!
We loved the unique spins our community added to their own solutions, with users implementing their filtering techniques to get to the highest scoring song and genre in different ways.
Join us tomorrow for a challenge that combines our beloved Pokémon with database management and API handling. And by the way: this challenge is courtesy of our amazing Just KNIME It! KNinja @KNIMEST!
New Wednesday, new Just KNIME It! challenge! 
Eurovision is full of surprises—not just in performances but also in how different countries score. A song might be a fan favorite with high televote support yet receive low jury points, or certain musical styles might consistently have higher energy levels than others. In this week’s challenge, you’ll explore key aspects of Eurovision scoring and song characteristics using KNIME. Among all songs that qualified for the final, which style has the highest average BPM? 
Need help with tags? To add tag JKISeason4-18 to your workflow, go to the description panel in KNIME Analytics Platform, click the pencil to edit it, and you will see the option for adding tags right there. 
Let us know if you have any problems!
My solution to the challenge:
Eurovision Insights – Challenge 18
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Step 1 – Data preparation
) corrected some wrong qualified_10 missing (thanks Maneskin for reminding me your victory).
.
Takeaway
Join us tomorrow for a challenge that combines our beloved Pokémon with database management and API handling. 
And by the way: this challenge is courtesy of our amazing Just KNIME It! KNinja