Methodology

Science Behind the Score

SCOREMAX™ uses rigorous mathematical models and thousands of data points for truly objective startup health measurement.

Mathematical Model

Three layers of analysis for precision and prediction.

1. Normalization Functions

4 Methods

We use advanced normalization including NormRatio(x), NormCount(x; Xref), NormKey(x; Xref), and NormRate(rmin, rmax) to standardize diverse metrics onto a unified 0-100 scale.

2. Weighted Maturity Index

Dynamic Weights

Maturity is calculated as Maturity = wa * nAge + wr * nARR + wf * nStreams, dynamically adjusting importance based on your startup's stage.

3. Murphy's Law Curve

30% Buffer

We apply the Murphy law 30% curve basic Formula 96 * 1.30^96 to project revenues and timelines, accounting for the inevitable "unknown unknowns".

Complete Mathematical Specification

The exact formulas powering the SCOREMAX™ engine — expanded and explicit.

Core Constants

Fixed Values

Pillar Weights (mᵢ):

mₛ=10, mᴄ=15, mₒ=10, mᵣ=15
mₑ=10, mₘ=10, mₐ=15, mₓ=15

Growth Reference: rᵣₑf = 0.30

Baseline: B = 96

Iterations: Tₘₐₓ = 96

Decay Sensitivity: k = 1.0

Normalization Functions

Data Standardization

Normratio(x):
  0 if x ≤ 0
  x if 0 < x ≤ 1
  1 if x > 1

Normcount(x; Xref):
  min(1, log(1+x) / log(1+Xref))

Normrate(r; rmin, rmax):
  (clip(r, rmin, rmax) - rmin) / (rmax - rmin)

Normsent(s): (s + 1) / 2

Maturity Index

Stage Calibration

normAge: min(1, A / Aref)

normARR: Normmoney(ARR; ARRref)

normStreams: min(1, Sstreams / Sref)

M:
wa·normAge + wr·normARR + wf·F + ws·normStreams

t: 1 + (Tmax - 1)·M

Pillar Raw Score

Weighted Average

Each pillar score s̃ᵢ is computed as:

s̃ᵢ = (Σⱼ wᵢⱼ cᵢⱼ Nᵢⱼ) / (Σⱼ wᵢⱼ cᵢⱼ)

where:
• wᵢⱼ = weight of variable j in pillar i
• cᵢⱼ = confidence [0,1] of variable j
• Nᵢⱼ = normalized value of variable j

Growth & Variance Analysis

Murphy's Law

Implied Growth: gᵢ = s̃ᵢ · rref

Curve: fᵢ(t) = B(1 + gᵢ)ᵗ

Derivative: f'ᵢ(t) = B(1 + gᵢ)ᵗ ln(1 + gᵢ)

Reference: f'ref(t) = B · 1.30ᵗ · ln(1.30)

Variance:
Vᵢ(t) = |[ (1+gᵢ)ᵗ ln(1+gᵢ) ] / [ 1.30ᵗ ln(1.30) ] - 1|

Final SCOREMAX Computation

0–100 Scale

Soft-Decay Mapping:
Scorei = mᵢ · e-kVᵢ(t)

Total Mass: Mtotal = Σᵢ mᵢ

Total Score: Stotal = Σᵢ Scorei

SCOREMAX = (Stotal / Mtotal) × 100

Band Thresholds:
V̄ ≤ 0.20 → ≥80 (Investment Grade)
0.20 < V̄ ≤ 0.40 → 60–80 (Sustainable)
0.40 < V̄ ≤ 0.60 → 40–60 (At Risk)
V̄ > 0.60 → <40 (Exit/Rethink)

Data Sources & Verification

Garbage in, garbage out. We ensure data integrity through multi-source validation.

Direct Integration

Real-Time API

API connections to your CRM, Accounting (QuickBooks/Xero), and Analytics (GA4/Mixpanel) tools.

Public Web Data

1M+ Sources

Scraping of LinkedIn, Crunchbase, and news outlets to verify team claims and market presence.

Peer Benchmarking

50k+ Startups

Comparison against our proprietary database of 50,000+ startups to contextualize your performance.

Investment Grade Bands

What your score actually means for investors.

Score Range Grade Investor Signal
80–100 Investment Grade Top 1% of startups. Ready for investors—expect term sheets and strong interest.
60–79 Sustainable / Needs Scale Solid fundamentals. Due diligence recommended.
40–59 At Risk Promising but risky. Monitor for 6 months.
0–39 Exit / Rethink Fundamental flaws in model or execution.