We maintain a database of 1.83 million entities — movies, TV series, games, companies, people, cryptocurrencies, universities — connected by 2.18 million knowledge graph links.
One of the things we built on top of this is the Feelgood Score: an algorithmic scoring system that rates 191,000 movies on comfort, not quality.
The stack:
- PostgreSQL database with 1.83M entities
- JSONB data columns per entity (ratings, genres, sentiment, streaming availability)
- Custom scoring algorithm weighing audience sentiment, rewatchability, genre patterns, and critical reception
- Knowledge graph links connecting actors to films to studios to streaming services
What the data shows:
When you score 191,000 movies on comfort instead of quality, the rankings flip completely. The Godfather (rating: 8.69) scores 29 on comfort. Zootopia (rating: 7.76) scores 80.
The correlation between audience rating and Feelgood Score is nearly zero. Quality and comfort are independent variables.
Some stats from our movie database:
- 209,684 total movies tracked
- 113 languages represented
- $820.7 billion in total tracked box office revenue
- Only 1,126 films confirmed profitable (5.5% of those with financial data)
- 685,896 streaming availability records across 678 services
We publish all the stats openly: Movie Statistics 2026
Full writeup on the Feelgood Score analysis: We Scored 191,000 Movies
The scoring methodology is transparent: How We Compute the Good Score
If you're building anything with large entity databases or knowledge graphs, happy to talk architecture.
Top comments (0)