Predict CAT percentile using balanced section performance assumptions. Track attempts, accuracy, and difficulty adjustments easily. Get clearer rank expectations before your next mock analysis.
Use this estimator to project percentile, rank, sectional standing, and score range using your mock performance assumptions. This tool is directional and should support planning, not replace official results.
These rows are illustrative examples for understanding how the predictor can be used.
| Profile | VARC | DILR | QA | Difficulty | Adj. Score | Predicted Percentile | Estimated Rank |
|---|---|---|---|---|---|---|---|
| Balanced strong mock | 38 | 30 | 35 | Moderate | 104.20 | 96.50% | 10,500 |
| High VARC, weaker DILR | 42 | 22 | 31 | Hard | 98.60 | 95.72% | 12,840 |
| Late-stage improvement | 34 | 28 | 40 | Moderate | 106.80 | 96.82% | 9,540 |
| Sectional risk case | 36 | 18 | 37 | Easy | 89.40 | 93.85% | 18,450 |
1) Raw Total Score
Raw Total = VARC Score + DILR Score + QA Score
2) Accuracy Factor
Accuracy Factor = 0.91 + (Average Accuracy × 0.16)
3) Attempt Factor
Attempt Factor = 0.93 + (Attempt Ratio × 0.09)
4) Balance Factor
Balance Factor rewards even sectional performance and reduces overdependence on one section.
5) Adjusted Predictor Score
Adjusted Score = Raw Total × Difficulty Factor × Accuracy Factor × Attempt Factor × Balance Factor + Normalization Adjustment + Improvement Buffer
6) Predicted Percentile
Percentile is estimated by linear interpolation across historical score anchors.
7) Estimated Rank
Rank = Candidate Pool × (100 − Predicted Percentile) ÷ 100
8) Sectional Percentiles
Each section gets its own adjusted score, then maps to a sectional percentile anchor curve.
This predictor is intended for strategy and mock analysis. Final official CAT percentiles depend on actual scaled scores and competition patterns.
No. It is a planning tool that estimates percentile and rank from your inputs. Official CAT percentiles depend on final scaled scores and overall candidate performance.
Those values improve prediction quality. Two students may have identical raw scores, but different attempt patterns and accuracy profiles suggest different stability and growth potential.
It represents the possible scaling impact between exam slots. Positive values raise your projected score, while negative values reduce it when you expect an easier slot effect.
It lets you model near-term gains from revision, better question selection, improved stamina, or cleaner execution in the final exam compared with recent mocks.
Yes. Percentiles move with competition strength, exam difficulty, and score distribution. That is why this tool gives directional ranges instead of claiming exact official outcomes.
Many institutes use both overall and sectional cutoffs. A strong overall score may still miss calls if one section remains below the required percentile.
Choose the option that most closely matches your mock quality or expected exam feel. Hard slots usually lift adjusted scores slightly, while easy slots can compress percentile gains.
Track mock results weekly, compare predicted ranges, and watch sectional weakness. It helps you see whether strategy changes improve projected calls and ranking potential.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.