Score
3.39
Kelly
52.9%
1 / 12
Professional certification (PMP)
Career / job change · 12-month horizon
Score 3.39 · worth doing, but scale the stake to your risk budget (GO_REDUCED)
With the resources you committed ($5,720 of equivalent capital) over a 12-month horizon, the prior win rate is 60.0%, the payoff ratio is b=5.65, giving an expected value of EV=$17,113 and a risk-adjusted return of RAROC=8.55. The expected value is positive, so the arithmetic supports acting. The Kelly-optimal stake of 52.9% exceeds the 20% risk budget for your Working professional profile, so the platform caps its recommendation at that ceiling.
Exam fees, membership and 135 hours. The cheapest commitment in the catalogue against a credible salary premium, which is why it ranks first. The win rate is the weakest number here: PMI has published no pass rate since 2005, so 60% is a vendor estimate with no primary basis.
2 / 12
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
60.0%
Prior probability of success
Payoff ratio b
5.65 ×
b = E[win] / E[loss]
Expected value
$17,113
EV = capital × (p × b − (1 − p))
Kelly
52.9%
(b·p − (1 − p)) / b
RAROC
8.55
EV / (capital × σ)
Score
3.39
score = p × b
3 / 12
What you are committing
Equivalent capital $5,720, converted from five kinds of resource.
This opportunity carries $5,720 of equivalent capital. The largest single component is your own time, at 90% of the total ($5,130). 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.
4 / 12
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 60.0% is well above the category baseline of 53.3% (+6.7%). Make sure that edge rests on specific, checkable facts rather than optimism. The payoff ratio of 5.65× is above the baseline median of 3.00× — check that your success case has not quietly flattened the downside tail. 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)
5 / 12
Action plan
Sized to the risk budget of your profile: Working professional.
Recommendation: GO_REDUCED — the expected value is positive and this is worth doing, but the Kelly-optimal stake exceeds your risk budget. Scale the commitment down to the risk_budget ceiling shown below.
- Kelly's stake clearly exceeds your risk budget, which means the opportunity is good but large relative to your resources today. Fund it to the risk-budget ceiling and no further.
- Replace one all-in commitment with staged funding: run a pilot or MVP first, then add capital once the metrics hold.
- Consider whether a partner can carry half the cash or time. That halves your effective position without halving the upside.
6 / 12
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 $9,133. Spend your research budget here first.
- · The second most sensitive variable is Payoff ratio (b) (expected value swings $7,760); 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.
7 / 12
Monte Carlo distribution
10,000 simulated outcomes, seed demo-professional-cert — rerunning with the same inputs gives the same distribution.
P5
-$6,998
Median (P50)
$23,076
P95
$52,286
Probability of a positive outcome
59.8%
Across 10,000 Monte Carlo runs: P5 = -$6,998, P50 = $23,076, P95 = $52,286, and the probability of a positive outcome is 59.8%. The distribution is roughly symmetric — gains and losses are balanced.
8 / 12
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 = $23,885, base = $17,113, bear = $11,214. The $12,670 gap between bull and bear measures how fragile your assumptions are — the wider it is, the more due diligence pays for itself.
9 / 12
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.
10 / 12
Assumptions
Every input behind the numbers above, in one place.
11 / 12
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 = 0.6, 5.653, 0.35, 5720
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 = 0.6, b = 5.653, sigma = 0.35, capital = 5720;
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/.
12 / 12
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)