DLDecision Lab
NO_GO · Do not proceed

Score

0.46

Kelly

0.0%

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Bootstrapped micro-SaaS

Side business / micro-venture · 12-month horizon

Free

Score 0.46 · expected value is negative under current assumptions (NO_GO)

With the resources you committed ($19,240 of equivalent capital) over a 12-month horizon, the prior win rate is 13.3%, the payoff ratio is b=3.47, giving an expected value of EV=-$7,791 and a risk-adjusted return of RAROC=-0.27. Under your current assumptions this is not worth acting on. The Kelly-optimal stake of 0.0% sits inside the 20% risk budget for your Working professional profile.

Ten hours a week for a year on a niche subscription product, with $240 of hosting. The win rate is the share of Stripe-verified indie projects that clear $1,000/month, discounted by the 54% that never earn anything. Almost all of the cost is your own time.

NO_GO · Do not proceed
Profile: Working professional

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The three numbers

Win rate, payoff ratio and expected value decide most of the recommendation; Kelly, RAROC and score turn them into an action.

Free

Win rate p

13.3%

Prior probability of success

Payoff ratio b

3.47 ×

b = E[win] / E[loss]

Expected value

-$7,791

EV = capital × (p × b − (1 − p))

Kelly

0.0%

(b·p − (1 − p)) / b

RAROC

-0.27

EV / (capital × σ)

Score

0.46

score = p × b

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What you are committing

Equivalent capital $19,240, converted from five kinds of resource.

Free

This opportunity carries $19,240 of equivalent capital. The largest single component is your own time, at 99% of the total ($19,000). Time cannot be recovered once spent. Set an explicit time ceiling and force a review when you are halfway through it.

How non-cash resources are priced

One hour of your own time is priced at $38 and one person-month of hired labour at $8,080. These are market shadow prices, not what you personally earn; changing them changes only the scale, not the ranking.

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How this compares with the category

Your assumptions against the Beta(α, β) prior for this kind of decision.

Free

Category mean win rate

6.7%

Beta(α=2, β=28)

Category payoff P50

2.50 ×

P90 13.00×

Your win rate of 13.3% is well above the category baseline of 6.7% (+6.6%). Make sure that edge rests on specific, checkable facts rather than optimism. The payoff ratio of 3.47× is above the baseline median of 2.50× — check that your success case has not quietly flattened the downside tail. Baseline source: Stripe-verified indie project revenue distribution (US-IND-01..04) + fitted Patreon earnings distribution (US-CRE-01..04).

Baseline source: Stripe-verified indie project revenue distribution (US-IND-01..04) + fitted Patreon earnings distribution (US-CRE-01..04)

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Action plan

Sized to the risk budget of your profile: Working professional.

Free

Recommendation: NO_GO — under your current win-rate and payoff assumptions the expected value is negative. This is not where your money and time should go.

  • The expected value is negative. That is arithmetic, not a lack of nerve.
  • Two ways forward: (a) restructure the inputs — less cash, more time, or a smaller scope — until the expected value turns positive; or (b) move the resources to a higher-ranked GO opportunity in your portfolio.
  • Save this decision and recompute every three months. Category win rates and payoff ratios move over time.
Caution: This verdict rests entirely on the win rate and payoff ratio you supplied. If you believe you hold information others do not, open the assumptions list, revise it, and recompute.

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Sensitivity analysis

How much the expected value moves when each variable is swung ±20%, ranked by impact. The top two are the ones worth researching before you commit.

Full PDF
  • · The tornado analysis identifies Win rate (p) as the variable the conclusion is most sensitive to: a ±20% swing moves the expected value by $4,579. Spend your research budget here first.
  • · The second most sensitive variable is Payoff ratio (b) (expected value swings $3,556); the remaining inputs matter markedly less.
  • · A quick test for whether to keep researching: buy the information only if it costs less than one fifth of the expected-value swing it would resolve.

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Monte Carlo distribution

10,000 simulated outcomes, seed demo-side-saas — rerunning with the same inputs gives the same distribution.

Full PDF

P5

-$62,683

Median (P50)

-$16,652

P95

$121,545

Probability of a positive outcome

14.2%

Across 10,000 Monte Carlo runs: P5 = -$62,683, P50 = -$16,652, P95 = $121,545, and the probability of a positive outcome is 14.2%. The distribution is right-skewed — a long upside tail carries most of the value.

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Scenarios

Bull, base and bear, moving win rate and payoff ratio together by ±15%.

Full PDF

Scenario analysis (win rate and payoff ratio moved together by ±15%): bull = -$4,541, base = -$7,791, bear = -$10,642. The $6,101 gap between bull and bear measures how fragile your assumptions are — the wider it is, the more due diligence pays for itself.

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Counterfactual matrix

What the expected value becomes at other combinations of win rate and payoff ratio — how wrong your assumptions can be before the answer changes.

Full PDF
b=0.5b=1.0b=1.5b=2.0b=3.0b=4.0b=6.0b=8.0p=10%-$16.4K-$15.4K-$14.4K-$13.5K-$11.5K-$9.6K-$5.8K-$1.9Kp=20%-$13.5K-$11.5K-$9.6K-$7.7K-$3.8K$0$7.7K$15.4Kp=30%-$10.6K-$7.7K-$4.8K-$1.9K$3.8K$9.6K$21.2K$32.7Kp=40%-$7.7K-$3.8K$0$3.8K$11.5K$19.2K$34.6K$50Kp=50%-$4.8K$0$4.8K$9.6K$19.2K$28.9K$48.1K$67.3Kp=60%-$1.9K$3.8K$9.6K$15.4K$26.9K$38.5K$61.6K$84.7Kp=70%$962$7.7K$14.4K$21.2K$34.6K$48.1K$75K$102Kp=80%$3.8K$11.5K$19.2K$26.9K$42.3K$57.7K$88.5K$119.3Kp=90%$6.7K$15.4K$24.1K$32.7K$50K$67.3K$102K$136.6K

The counterfactual grid shows that expected value turns non-negative once the win rate reaches 20%, or once the payoff ratio reaches 0.5×. Treat those two lines as the minimum bar any further evidence has to clear.

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Assumptions

Every input behind the numbers above, in one place.

Full PDF
Win rate (p)13.3%
Payoff ratio (b)3.47 ×
Volatility (σ)1.50
Horizon12 months
Cash$240
Your own time500 hours
Hired labour0.0 person-months
Physical assets$0
Opportunity cost$0
Profile risk_budget (Working professional)20%

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Reproducibility appendix

Everything needed to recompute this report yourself.

Full PDF

Core formulas

EV = capital × (p × b − (1 − p))      f* = (b·p − (1 − p)) / b      RAROC = EV / (capital × σ)

Python

from models.feasibility_model import expected_value, kelly_fraction, raroc, go_no_go

p, b, sigma, capital = 0.133, 3.474, 1.5, 19240
ev    = expected_value(p, b, capital)
kelly = kelly_fraction(p, b)
rr    = raroc(ev, capital, sigma)
print(ev, kelly, rr, go_no_go(ev, kelly, 0.20, rr))

TypeScript

import { expectedValue, kellyFraction, raroc, goNoGo } from "decision-lab/lib/feasibility";

const p = 0.133, b = 3.474, sigma = 1.5, capital = 19240;
const ev    = expectedValue(p, b, capital);
const kelly = kellyFraction(p, b);
const rr    = raroc(ev, capital, sigma);
console.log(ev, kelly, rr, goNoGo(ev, kelly, 0.20, rr));

Both implementations are in the repository and are kept in agreement by golden-parity tests: models/feasibility_model.py and lib/feasibility/.

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Sources and disclaimer

Full PDF
  • · Kelly J.L. (1956). A New Interpretation of Information Rate. Bell System Technical Journal.
  • · Markowitz H. (1952). Portfolio Selection. The Journal of Finance.
  • · Sharpe W.F. (1966). Mutual Fund Performance. The Journal of Business.
  • · Kahneman D. & Tversky A. (1979). Prospect Theory. Econometrica.
  • · Industry baseline: Stripe-verified indie project revenue distribution (US-IND-01..04) + fitted Patreon earnings distribution (US-CRE-01..04)
  • · Decision Lab methodology
  • · Full business plan (PDF)
Disclaimer. This report is decision support for information purposes only. It is not financial, legal, medical or professional advice. Verify the underlying data, make your own decision, and accept responsibility for the outcome. For anything material to your money, health or legal position, consult a qualified professional.