NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will Matt Fitzpatrick win the 2026 U.S. Open?
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A live, interactive instrument for dissecting a single binary position. Sweep the inputs and watch every indicator recompute — payoff geometry, Kelly growth, Bayesian posterior, KL divergence, cost waterfall, Monte-Carlo equity fan, forecast calibration. Companion to the live /feed/pm-2026-us-open-winner-matt-fitzpatrick-win page.
▲ YES EDGE · +0.009 · f★ 0.9% · deploy 0.5% · net 0.11pp
§1 · Position economics
YES · Expected P/L per share +0.0086@ model P(YES) = 0.054
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 0.90% · g(f★) = 0.081%deploy 0.45% · g = 0.062%
g(f)f★ optimumdeployed fgrowth zone
Underbet leaves growth on the table; overbet destroys capital. The interior maximum is f★.
§2 · The trade ticket
YES @ 0.045 · EV +$21stake $113 · 0.45% of bankroll
Deployed stakestake
$113
0.45% of bankroll
Sharesunits
2,482
each pays $1 if YES
Max payoutwin
$2,482
gross, if win
Max profitwin
+$2,369
net of cost
Max losslose
-$113
binary settles to $0
Payout multiple×
×21.98
$1 → $21.98
Risk:RewardR:R
20.98 : 1
win $20.98 per $1
Expected P/LE[P/L]
+$21
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 5.4% | +$2,369 | +$128 |
| Resolves against (lose) | 94.6% | -$113 | -$107 |
| Expected value | 100.0% | — | +$21 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +0.9 pprelative edge +19.0%
Required win ratebreak-even
4.5%
price = implied probability
Model win rateP(win)
5.4%
what you forecast
Cushionedge
+0.9 pp
margin of safety
Fair pricemodel
0.054
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
4.5%
= price
Decimal oddsEU
21.978
total return per $1
AmericanUS
+2098
$100 wins $2098
FractionalUK
20.98 / 1
profit per $1 risked
Profit per $100stake
+$2097.80
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 329% · APY 1941%ROI 19.0% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+19.0%
APR (simple)scaled
+329%
ROI × 365/days
APY (compounded)if redeployed
+1941%
(1+ROI)^(365/d) − 1
Daily expectedper day
+0.83%
geometric, per day held
Capital turns/yrvelocity
×17.4
how often this slot recycles
simple APRcompounded APYyour horizon
Rank positions by APR, not raw ROI. A thin edge tomorrow beats a fat edge next year.
§5 · Costs & net edge
Net edge +0.11 pperosion 87% · break-even w/ fees 5.3%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$226
0.90% · g = 0.081%
Half Kelly½ f★
$113
0.45% · g = 0.062%
Quarter Kelly¼ f★
$56
0.23% · g = 0.037%
Flat 1%1%
$250
1.00% · g = 0.080%
Flat 2%2%
$500
2.00% · g = -0.015%
Flat 5%5%
$1,250
5.00% · g = -0.970%
Recommended¼ f★
$56
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.267 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.304 bit
Δ +0.037 bit vs market
Surprise · YES−log₂ p
4.46 bit
self-information
Surprise · NO−log₂(1−p)
0.07 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
NOISE · D_KL(q ‖ p) = 0.0008 nat (0.0012 bit)belief ≈ market — stand down
YES contributionNO contributionbelief ‖ marketnoise
Zero KL ⇒ you know nothing the crowd doesn't.
§8 · Bayesian inference
MARKET PRICE INSIDE 95% CIposterior μ 0.054 · CI [0.00, 0.22] · κ 13.2
Posterior meanE[θ]
0.054
Beta(0.7, 12.5)
95% credible intervalHDI
[0.00, 0.22]
price INSIDE → weak edge
Concentrationκ
13.2
pseudo-obs behind belief
Disagreementvs crowd
+0.9 pp
posterior − price
market prior (dashed)model posterior95% credible bandmarket price
When the market price falls outside the 95% credible interval, your disagreement is statistically meaningful.
§9 · Tail risk · Monte-Carlo (mode A · single position to resolution)
E[P/L] +4.4% · P(YES) 4.8% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+4.40%
P(YES) empiricalq
4.8%
Best pathmax
+2097.8%
Worst pathmin
-100.0%
VaR 95%5%
100.0%
CVaR 95%ES
100.0%
median path25/75 + 5/95 bandsentry pricemodel q
Logit-space mean-reverting walk + terminal flip with probability q. Answers: 'what happens to THIS one position'. Distinct from the repeated-edge fan below.
§9b · Tail risk · Monte-Carlo (mode B · repeated independent edges)
Median CAGR/bet 0.11% · ruin rate 0.0%400 paths × 120 bets · f deploy 0.50%
Sharpe / betμ/σ
0.072
μ 0.19% · σ 2.7%
Sortino / betμ/σ↓
0.386
downside-only denominator
VaR 95%5%
-0.5%
per-bet worst-case
CVaR 95%ES
-0.5%
mean tail loss
Max drawdownMDD
-9.5%
Calmar 0.01
Ruin rate≤50%
0.0%
P(equity ever ≤ 50%)
median25/75 band5/95 bandruin line
Answers a different question: 'if I could find this exact edge forever, what is the bankroll trajectory'. Compounds 120 sequential resolutions which is NOT what happens to a single position.
§10 · Base-rate & macro context
ANCHORED · supported by convictionanchor gap -51.2pp · crowd gap -52.0pp
Anchor gapmodel − base
-51.2 pp
Crowd gapprice − base
-52.0 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 19.1% · AUC 0.763out-of-sample BSS (5-fold) 19.3% ± 0.6% · Brier 0.2021 · log-loss 0.6081 · n 1600✓ n = 1600
BrierBS
0.2021
lower = better · ō 0.51
BSSvs base
19.1%
improvement over base rate
ReliabilityREL
0.0062
miscalibration · want ↓
ResolutionRES
0.0537
decisiveness · want ↑
Log lossLL
0.6081
cross-entropy
AUCROC
0.763
0.5 coin · 1.0 oracle
calibration curveROCUNC (irreducible)RES (skill, ↑)REL (miscalib, ↓)
Computed on a seeded synthetic forecast ledger. Reseed (⟳) to redraw.
§12 · Journal vitals (synthetic ledger)
PROFITABLE · PF 1.06 · expectancy +0.027R180 trades · win 51.1% · Sharpe 0.023
Total P/Lnet
+$1,223
on $45,000 cycled
Win ratehit %
51.1%
92 W / 88 L
Profit factorPF
1.06
$ won / $ lost
Expectancyper trade
+$6.79
avg $ per position
R-expectancyper risk
+0.027R
in units of risk taken
Avg win / losspayoff
$252.42 / -$250.00
ratio 1.01 : 1
Sharpe / traderisk-adj
0.023
μR / σR
Closing line valueCLV
+3.51 pp
avg edge vs close
cumulative P/Lprofitable zonered zonesynthetic · seeded from asset
The scorecard every trader checks. Synthetic ledger seeded from the asset slug — recomputes against your real fill history once wired.