About the project
Sequel Movies Meter started as a curiosity: do franchises usually get better, worse, or just stranger as they continue? The project collects movie ratings, organizes them into franchise timelines, and turns those timelines into quick visual reads.
Movie sequels are perfect little arguments. Critics might reward ambition while audiences reward comfort. A second film might refine the formula, or a fifth film might reveal that the formula ran out three movies ago.
The goal here is not to declare the one true best franchise. It is to make patterns visible: the sequel drop, the late recovery, the critic-audience split, and the rare series that stays strong for years.
The first step was not ratings at all. It was finding movie franchises and sequel groups from Wikipedia, then turning that research into a starting list of series and films worth tracking.
Once the franchise list existed, the project gathered movie metadata, posters, summaries, links, ratings, and vote counts from public movie pages. The important lesson from that era: save stable identifiers and raw source data, because website HTML changes faster than old scripts age.
Ratings arrive in different shapes. IMDb and Metacritic users use a 0-10 scale, while Rotten Tomatoes and Metacritic critics are already closer to 0-100. Missing values, old links, and odd title formatting have to be handled before anything can be compared fairly.
Individual movies are grouped into franchise timelines in release order. That ordering is what makes the project more interesting than a plain movie list: each film becomes a point in a story about momentum.
The dashboard computes critic and audience composites on a shared 0-100 scale. Critics average Metacritic and Rotten Tomatoes critic scores. Audience scores average IMDb, Metacritic users, and Rotten Tomatoes audiences after normalizing the 0-10 sources.
The charts draw each franchise as a compact timeline. The page also calculates quick reads like biggest sequel drop, strongest recovery, best long-running franchise, and critic-audience disagreement.
A movie score is not a verdict. It is a compressed signal from several rating communities. The useful part is comparison: whether a sequel rose or fell relative to the films around it, and whether critics and audiences moved together.
The original crawlers were a good prototype, but the better modern version should use a repeatable data pipeline. That means canonical movie IDs, provider-specific raw files, generated dashboard JSON, and a visible last-updated date.
The fun next layer is editorial: reboot filters, genre filters, vote-count weighting, and franchise detail pages that explain the weird cases rather than hiding them.