GMAT Score Calculator
GMAT Score is evaluated from Quantitative Score and Verbal Score. The calculation reports Estimated GMAT Total, Approximate Percentile and Target MBA Tier.
Results
About the GMAT Score Calculator
The calculator uses a multi formula configuration. Each reported value is read as a direct evaluation of the stored rules with the declared field formats and units.
Formula basis:
_q = (parseFloat(quant_score)||0)
_v = (parseFloat(verbal_score)||0)
_est = Math.round((_q * 7 + _v * 8.5 + 200) / 10) * 10
estimated_total = _est
percentile = _est >= 760 ? 'Top 1% (99th)': _est >= 740 ? 'Top 2% (98th)': _est >= 720 ? 'Top 5% (95th)': _est >= 710 ? 'Top 7% (93rd)': _est >= 700 ? 'Top 11% (89th)': _est >= 680 ? 'Top 16% (84th)': _est >= 660 ? 'Top 22% (78th)': _est >= 640 ? 'Top 29% (71st)': _est >= 620 ? 'Top 38% (62nd)': _est >= 600 ? 'Top 45% (55th)': 'Below average'
mba_tier = _est >= 730 ? 'Top 10 MBA programs (Wharton, Booth, Kellogg)': _est >= 700 ? 'Top 15 - 25 MBA programs (Darden, Haas, Ross)': _est >= 660 ? 'Top 25 - 50 MBA programs': _est >= 600 ? 'Regional MBA programs': 'Consider additional GMAT prep'
points_to_700 = Math.max(0, 700 - _est)
Interpret the outputs in the order shown by the result fields. Optional inputs affect only the outputs that depend on those variables.
Formula & How It Works
The calculation applies the following relations exactly as recorded in the metadata: _q = (parseFloat(quant_score)||0) _v = (parseFloat(verbal_score)||0) _est = Math.round((_q * 7 + _v * 8.5 + 200) / 10) * 10 estimated_total = _est percentile = _est >= 760 ? 'Top 1% (99th)': _est >= 740 ? 'Top 2% (98th)': _est >= 720 ? 'Top 5% (95th)': _est >= 710 ? 'Top 7% (93rd)': _est >= 700 ? 'Top 11% (89th)': _est >= 680 ? 'Top 16% (84th)': _est >= 660 ? 'Top 22% (78th)': _est >= 640 ? 'Top 29% (71st)': _est >= 620 ? 'Top 38% (62nd)': _est >= 600 ? 'Top 45% (55th)': 'Below average' mba_tier = _est >= 730 ? 'Top 10 MBA programs (Wharton, Booth, Kellogg)': _est >= 700 ? 'Top 15 - 25 MBA programs (Darden, Haas, Ross)': _est >= 660 ? 'Top 25 - 50 MBA programs': _est >= 600 ? 'Regional MBA programs': 'Consider additional GMAT prep' points_to_700 = Math.max(0, 700 - _est) Each output field is produced by substituting the supplied inputs into the relevant relation and then applying the declared rounding or text format.
Worked Examples
Example 1: Quant 47, Verbal 38
Inputs
With Quantitative Score = 47 and Verbal Score = 38 as the stated inputs, the result is Estimated GMAT Total = 850, Approximate Percentile = Top 1% (99th) and Target MBA Tier = Top 10 MBA programs (Wharton, Booth, Kellogg). Each value corresponds to the declared output fields.
Example 2: Quant 50, Verbal 44
Inputs
With Quantitative Score = 50 and Verbal Score = 44 as the stated inputs, the result is Estimated GMAT Total = 920, Approximate Percentile = Top 1% (99th) and Target MBA Tier = Top 10 MBA programs (Wharton, Booth, Kellogg). Each value corresponds to the declared output fields.
Example 3: First attempt: Quant 40, Verbal 32
Inputs
With Quantitative Score = 40 and Verbal Score = 32 as the stated inputs, the result is Estimated GMAT Total = 750, Approximate Percentile = Top 2% (98th) and Target MBA Tier = Top 10 MBA programs (Wharton, Booth, Kellogg). Each value corresponds to the declared output fields.
Example 4: Strong quant, weak verbal: Quant 50, Verbal 28
Inputs
With Quantitative Score = 50 and Verbal Score = 28 as the stated inputs, the result is Estimated GMAT Total = 790, Approximate Percentile = Top 1% (99th) and Target MBA Tier = Top 10 MBA programs (Wharton, Booth, Kellogg). Each value corresponds to the declared output fields.
Common Use Cases
- Estimate GMAT total score from section scores
- Compare GMAT score to target MBA program averages
- Understand GMAT percentile ranking