Methodology

Every ranking on Tenstara is sourced, dated, and produced by an explicit formula. No hidden weighting, no editorial cooking of numbers.

Where the data comes from

Movies: TMDB (themoviedb.org) — open community-curated dataset with multilingual titles, posters, and aggregate ratings.

How we rank: Bayesian shrinkage

Naive ranking by raw score is broken — a movie with a single 10/10 vote outranks a beloved classic at 8.7/10 with 28,000 votes. Real ranking sites including IMDb's Top 250 use the same correction: a Bayesian-weighted score that pulls items with few votes back towards the dataset mean.

R = (v / (v + m)) × Rᵢ + (m / (v + m)) × C

R   = computed score
v   = number of votes for this item
Rᵢ  = average score for this item
C   = mean score across the dataset
m   = minimum-vote threshold (we use the larger of 100 or 10% of dataset size)

Items with fewer than m votes are pulled toward the dataset mean and rank lower than they otherwise would. This is anti-spam by design.

What we never do

❌ Fabricate scores or vote counts
❌ Hide community vote counts behind round numbers — if 47 people voted, you see "47 votes"
❌ Suppress negative reviews
❌ Add fake authority badges that imply external awards we did not win
❌ Inflate sample sizes

What we always do

✅ Show n (vote count) next to every score
✅ Link directly to the source dataset
✅ Stamp every page with last-updated date
✅ Distinguish aggregate score (large-n external dataset) from community vote (our visitors)
✅ Localize titles and overviews to your reading language where source data exists

Update cadence

Movie data is re-fetched from TMDB on a weekly cadence. Major events (Oscars, Cannes, festival debuts) trigger ad-hoc fetches.

Last data fetch: 2026-04-25 · Source: TMDB (themoviedb.org) — seed dataset

Found an error?

Email hi@tenstara.com with the page URL and what's wrong. We acknowledge within 24 hours and fix within 48 hours of acknowledgement.