The Eurovision Song Contest stands as one of the world's most anticipated annual television events, bringing together nations from across Europe and beyond to showcase musical talent on an international stage. At the heart of Eurovision's evaluation process lies the jury voting system, where professional music experts from participating countries cast votes to determine which nations receive points and, ultimately, which country wins the contest's top honor. This collection of 35 prediction markets captures market sentiment around which countries will secure the jury vote across the Eurovision 2026 Grand Final, offering a real-time window into how the global prediction community assesses the competitive landscape. Each market represents a specific country's chances of winning the jury's highest vote, from historical powerhouses like Finland and Denmark to emerging contenders such as Montenegro and Portugal. By observing market prices across these related predictions, you gain insight into several key dimensions: which countries the prediction market community perceives as strongest performers, how confidence varies between established Eurovision leaders and nations making their mark, and how broader trends in European taste and musical preferences are shifting. The prices themselves reflect the aggregate judgment of thousands of participants who have examined the competing performances, studied voting patterns, and assessed each nation's trajectory throughout the competition season. These markets update continuously as new information emerges—from rehearsal footage to expert analysis to shifts in public and professional sentiment. Reading these prediction prices together provides a richer picture than examining any single country in isolation, revealing clusters of similarly positioned nations and identifying unexpected contenders that the prediction community is closely watching. This market-driven approach to Eurovision analysis complements traditional expert forecasts by incorporating diverse global perspectives and real-time information discovery.