Marks vs Rank Predictor
Estimates your rank by modelling scores as a normal distribution — enter expected marks, total candidates and the exam's rough average.
How the prediction works
The tool assumes candidate scores follow a normal (bell-curve) distribution around the average you supply. Your percentile is the share of that curve below your score; predicted rank = candidates × (1 − percentile). A ±10% band is shown because real distributions have fatter tails at the top.
Frequently asked questions
How accurate is a rank predictor?
It is an estimate, not a guarantee. Real exam distributions are not perfectly normal, and normalization across shifts changes final ranks. Use it for a ballpark only.
Where do I find the average marks and spread?
Coaching institute answer-key analyses and previous year cut-off discussions usually give a good sense of the average and how spread out scores are.
What is standard deviation here?
A measure of how spread out scores are. If most candidates score close to the average, use a smaller value (8–12); if scores vary widely, use a larger one (15–25).
Does this account for normalization?
No. Multi-shift exams apply normalization formulas that can shift raw scores by several marks.