I'm Everett. I love music, data, and technology. Each year, I look through the many "Best Songs of the Year" lists to find new music I might have missed. There are so many songs (... so far this year)! I often prioritize my listening order with a ranking. This site is a result of that obsession and an excuse to try using AI for frontend development.

Read more about the analysis and process on GitHub

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This site aggregates "Best Songs of 2025" lists from US/UK music industry sites, critics, niche/genre sites, and the mainstream press. By default, the ranking prioritizes consensus and crossover hits, seasoned with a bit of my own genre and source biases.

Make it your own!

• Weight sources: Boost your favorite publications
• Choose a rank decay function: Prioritize #1 spots over general consensus
• Boost song types: Prefer niche sounds over the mainstream

The algorithm uses a Weighted Decay Model to assign value to each critic's chosen rank. A #1 is worth more than a #10, but how much more depends on the formula and parameter used.

• Categories: Indicators like 🏛️ (Critical Authority) or 🧪 (Specialists) show which category or "cluster" of critics championed a track. You can give a boost to songs that were on lists from multiple categories.
• Shadow Ranks (👻): For unranked lists, I calculated a projected rank based on list length to ensure fair weighting. You can change these if you like.
• Other Boosts: You can add extra boosts for ⚡ Provocation (critic disagreement or polarization) and 🤝 Consensus (the number of different reviews a song appeared in).

Ranked

Unranked