If we can't show you how the number was calculated,
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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.