While consultants sell insider magic, AdmitGPT provides calculated reality. My logic is open-source and runs entirely in your browser. No data collection, no hidden formulas. A systematic strike against information inequality. The admissions industry thrives on fear and ambiguity. By making the math transparent, we return the power to the students. This is about leveling the playing field for everyone. Built for transparency, designed by students.
SAT Z-Score = (YourSAT - CollegeSATAverage) / ((SAT_75th - SAT_25th) / 1.35) GPA Z-Score = (YourGPA - DatasetGPAMean) / DatasetGPAStd Academic Composite = (SAT_Z × 0.55) + (GPA_Z × 0.45)
We compute how far your SAT and GPA are from the college's benchmarks, measured in standard deviations. SAT is weighted 55% because the data in studentsdata.json shows SAT has higher predictive power for acceptance outcomes.
S = Σ(W × T × R × P × D × V) × C / 2.5 + DiversityBonus W: Base (GM: 8.0, Outlier: 4.0, T1: 1.5, T2: 0.6, T3: 0.1) T: Scope (Local: 1.0, Nat: 3.0, Intl: 5.0, Global Elite: 8.0) R: Rarity (Common: 1.0, Rare: 1.8, Ultra: 3.5, Unique: 6.0) P: Institutional (Standard: 1.0, Rec: 1.25, Prest: 1.6, World: 2.2) D: Cog Load (Low: 0.8, Med: 1.0, High: 1.4, Research: 1.8) V: Validation (Self: 0.6, Peer: 0.75, Inst: 0.9, Audit: 1.0) C: Confidence % (0.0 to 1.0)
Criteria-based, never keyword-matching. v1.0 introduces a 6-dimensional rubric for every activity. We apply a saturation limit (sqrt) above 10 points per individual item contribution to prevent 'infinite spikes' from skewed data. The total is scaled by 2.5 to maintain a [0, 15] range.
Bonus = Top4_Weighted_Categories(Σ CategoryWeights) CategoryWeight: T3 (0.2), T2 (0.5), T1 (1.0), Outlier (1.5), GM (2.5) Scaling: Total Weight 1.5+ (+0.5), 3.0+ (+1.0), 5.0+ (+2.0), 8.0+ (+3.0)
Selective schools reward polymaths. If you have significant achievements across multiple distinct categories (STEM, Arts, Leadership, etc.), your spike score receives a non-linear boost up to +3.0.
MajorRate = AcceptedInMajor / AppliedInMajor (from dataset) OverallRate = AcceptedOverall / AppliedOverall Modifier = MajorRate / OverallRate (clamped to [0.5, 1.5]) Additive adjustment = (Modifier - 1) × 0.5
Computed dynamically from the dataset — never hardcoded. CS at top schools typically shows a modifier of 0.70–0.78× based on our data, meaning CS applicants face lower acceptance rates than the school average.
If domestic: modifier = 0 (no adjustment) If international: modifier = (college_nonresident_alien_rate / 0.10) × 0.1 - 0.3 Source: College Demographic Data
International applicants compete in a smaller pool. Schools with higher international enrollment rates penalize less. This modifier is always shown to you — it's never hidden in the score.
Find profiles where: same school + SAT ±80 + same major category + same intl status n ≥ 15 → High confidence (range width: ±12%) n ≥ 8 → Medium confidence (range width: ±20%) n ≥ 3 → Low confidence (range width: ±30%) n < 3 → Insufficient data (range width: ±35%)
More similar profiles = narrower range = higher confidence. We always tell you the exact number of profiles this estimate is based on.
Academic_Gate = 1 / (1 + e^-(5.0 * (Academic_Z + 1.2))) Impact = Impact(SpikeRating, MajorMod, IntlMod, Region) Final_Prob = Academic_Gate * Impact
AdmitGPT v1.0 uses a Gated Multiplicative Model. The 'Academic Gate' is a steep curve—if your academics are more than 2.0σ below the mean, the gate closes and your extracurricular 'Impact' is heavily reduced. This prevents unrealistic 'outlier' results for low-academic applicants at elite schools. You cannot 'buy' your way into a school with ECs if you don't meet the academic floor.
Distance = √( (0.40 × (SAT_diff / SAT_range))² + (0.30 × (GPA_diff / GPA_range))² + (0.20 × EC_tier_diff)² + (0.10 × Awards_diff)² )
Weighted Euclidean distance within your major category micro-cluster. This finds the most similar accepted and rejected profiles, so you can see exactly what distinguishes them from you.