Score
1.03
Kelly
12.8%
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Big Tech to late-stage startup
Career / job change · 18-month horizon
Score 1.03 · given your profile → proceed (GO)
With the resources you committed ($102,280 of equivalent capital) over a 18-month horizon, the prior win rate is 35.0%, the payoff ratio is b=2.93, giving an expected value of EV=$38,514 and a risk-adjusted return of RAROC=0.44. The expected value is positive, so the arithmetic supports acting. The Kelly-optimal stake of 12.8% sits inside the 20% risk budget for your Working professional profile.
Trade a liquid public-company grant for illiquid startup equity. What is at risk is the grant you walk away from, not cash. The win rate is the complement of the 65% of venture financings that return less than 1x.
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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.
Win rate p
35.0%
Prior probability of success
Payoff ratio b
2.93 ×
b = E[win] / E[loss]
Expected value
$38,514
EV = capital × (p × b − (1 − p))
Kelly
12.8%
(b·p − (1 − p)) / b
RAROC
0.44
EV / (capital × σ)
Score
1.03
score = p × b
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What you are committing
Equivalent capital $102,280, converted from five kinds of resource.
This opportunity carries $102,280 of equivalent capital. The largest single component is opportunity cost, at 98% of the total ($100,000). Opportunity cost is the return on what you gave up, and it is routinely underestimated. If this fails you pay twice: the loss itself, plus the alternative you never ran.
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.
Category mean win rate
53.3%
Beta(α=16, β=14)
Category payoff P50
3.00 ×
P90 6.00×
Your win rate of 35.0% is below the category baseline of 53.3% (-18.3%). That usually reflects conservative estimating rather than a weak opportunity, but it does pull the expected value down. The payoff ratio of 2.93× is close to the baseline median of 3.00×. Baseline source: Pew job-switcher real wage gains (US-CAR-05..06) + Atlanta Fed Wage Growth Tracker (US-CAR-01..02) + Correlation Ventures financing outcomes (US-CAR-16..17).
Baseline source: Pew job-switcher real wage gains (US-CAR-05..06) + Atlanta Fed Wage Growth Tracker (US-CAR-01..02) + Correlation Ventures financing outcomes (US-CAR-16..17)
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Action plan
Sized to the risk budget of your profile: Working professional.
Recommendation: GO — proceed, within your risk budget.
- Commit up to the Kelly-recommended stake and hold back at least 30% of your resources as a buffer.
- Explain the decision to one person you trust who is outside this field. Outsiders ask the question insiders have stopped asking.
- Write an exit condition now — for example, stop if metric X is not reached within six months — and put the review date in your calendar.
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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.
- · The tornado analysis identifies Win rate (p) as the variable the conclusion is most sensitive to: a ±20% swing moves the expected value by $56,317. Spend your research budget here first.
- · The second most sensitive variable is Payoff ratio (b) (expected value swings $41,998); 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-switch-job-unicorn — rerunning with the same inputs gives the same distribution.
P5
-$188,615
Median (P50)
-$74,723
P95
$705,590
Probability of a positive outcome
34.5%
Across 10,000 Monte Carlo runs: P5 = -$188,615, P50 = -$74,723, P95 = $705,590, and the probability of a positive outcome is 34.5%. 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%.
Scenario analysis (win rate and payoff ratio moved together by ±15%): bull = $77,744, base = $38,514, bear = $4,008. The $73,737 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.
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.
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Reproducibility appendix
Everything needed to recompute this report yourself.
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 = 0.35, 2.933, 0.85, 102280
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 = 0.35, b = 2.933, sigma = 0.85, capital = 102280;
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
- · 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: Pew job-switcher real wage gains (US-CAR-05..06) + Atlanta Fed Wage Growth Tracker (US-CAR-01..02) + Correlation Ventures financing outcomes (US-CAR-16..17)
- · Decision Lab methodology
- · Full business plan (PDF)