Data sources & methodology

Official and government publications wherever they exist, and universities' own published figures where they don't. Every acceptance rate we store is recorded against its source, so a figure can always be traced back.

National education registries almost everywhere publish enrolment — how many students started — but never applications. Without both numbers an acceptance rate cannot be calculated. The United States is the exception, because IPEDS requires institutions to report applications received.

So for a large share of universities outside the US, a published acceptance rate genuinely does not exist anywhere. We would rather say that than manufacture a figure.

Two stages, in this order.

1. Place the university. We establish how selective it is using the strongest evidence available, in strict priority: a published acceptance rate first; failing that, its world ranking as a proxy; failing that, a model estimate. Real data always wins — a lower tier is only ever reached when the one above it is empty.

2. Place you against it. Your profile is scored on academic performance, subject fit for your field, extracurricular evidence and how you meet stated entry requirements. Those are combined using weights that differ by country, because admissions differ by country: UK decisions turn heavily on subject-specific grades, Indian ones largely on exam performance against a cutoff, US ones on a wider picture. The result is anchored to the university's real selectivity rather than floating free.

Every estimate carries the basis it rests on, in plain words on the card. A figure derived from a published acceptance rate is described as such. One derived from world ranking says so. One produced by the model says so, and carries low confidence. Nothing we estimate is ever presented as a published statistic.

The model is deliberately strict at the most selective universities. If a tool tells a strong but ordinary applicant they have a comfortable chance at a single-digit-admit-rate school, the tool is broken. An estimate that errs low is recoverable; one that errs high costs someone a place on their list.

Essays, recommendations, interviews, portfolios, demonstrated interest, an admissions committee's priorities in a given year, and your personal context are all invisible to us. They routinely decide outcomes. Treat every number here as a way to build a balanced list, never as a prediction of a decision.