NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will Josh Hokit win the Hokit vs Lewis professional MMA fight scheduled for Jun 14, 2026?
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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/kalshi-kxufcfight-26jun14hoklew-hok page.
▲ YES EDGE · +0.000 · f★ 0.0% · deploy 0.0% · net -0.74pp
§1 · Position economics
YES · Expected P/L per share +0.0001@ model P(YES) = 0.780
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 0.05% · g(f★) = 0.000%deploy 0.02% · g = 0.000%
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 @ 0.780 · EV +$0stake $6 · 0.02% of bankroll
Deployed stakestake
$6
0.02% of bankroll
Sharesunits
7
each pays $1 if YES
Max payoutwin
$7
gross, if win
Max profitwin
+$2
net of cost
Max losslose
-$6
binary settles to $0
Payout multiple×
×1.28
$1 → $1.28
Risk:RewardR:R
0.28 : 1
win $0.28 per $1
Expected P/LE[P/L]
+$0
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 78.0% | +$2 | +$1 |
| Resolves against (lose) | 22.0% | -$6 | -$1 |
| Expected value | 100.0% | — | +$0 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +0.0 pprelative edge +0.0%
Required win ratebreak-even
78.0%
price = implied probability
Model win rateP(win)
78.0%
what you forecast
Cushionedge
+0.0 pp
margin of safety
Fair pricemodel
0.780
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
78.0%
= price
Decimal oddsEU
1.282
total return per $1
AmericanUS
-355
risk $355 to win $100
FractionalUK
0.28 / 1
profit per $1 risked
Profit per $100stake
+$28.21
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 0% · APY 0%ROI 0.0% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+0.0%
APR (simple)scaled
+0%
ROI × 365/days
APY (compounded)if redeployed
+0%
(1+ROI)^(365/d) − 1
Daily expectedper day
+0.00%
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 -0.74 pperosion 7297% · break-even w/ fees 78.8%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$12
0.05% · g = 0.000%
Half Kelly½ f★
$6
0.02% · g = 0.000%
Quarter Kelly¼ f★
$3
0.01% · g = 0.000%
Flat 1%1%
$250
1.00% · g = -0.001%
Flat 2%2%
$500
2.00% · g = -0.005%
Flat 5%5%
$1,250
5.00% · g = -0.035%
Recommended¼ f★
$3
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.760 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.760 bit
Δ -0.000 bit vs market
Surprise · YES−log₂ p
0.36 bit
self-information
Surprise · NO−log₂(1−p)
2.18 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
NOISE · D_KL(q ‖ p) = 0.0000 nat (0.0000 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 0.780 · CI [0.65, 0.89] · κ 46.7
Posterior meanE[θ]
0.780
Beta(36.4, 10.3)
95% credible intervalHDI
[0.65, 0.89]
price INSIDE → weak edge
Concentrationκ
46.7
pseudo-obs behind belief
Disagreementvs crowd
+0.0 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] -0.0% · P(YES) 78.0% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
-0.00%
P(YES) empiricalq
78.0%
Best pathmax
+28.2%
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 0.00% · ruin rate 0.0%400 paths × 120 bets · f deploy 0.50%
Sharpe / betμ/σ
0.003
μ 0.00% · σ 0.3%
Sortino / betμ/σ↓
0.001
downside-only denominator
VaR 95%5%
-0.5%
per-bet worst-case
CVaR 95%ES
-0.5%
mean tail loss
Max drawdownMDD
-0.8%
Calmar 0.00
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 +27.5pp · crowd gap +27.5pp
Anchor gapmodel − base
+27.5 pp
Crowd gapprice − base
+27.5 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 21.0% · AUC 0.771out-of-sample BSS (5-fold) 21.0% ± 0.9% · Brier 0.1973 · log-loss 0.5867 · n 1600✓ n = 1600
BrierBS
0.1973
lower = better · ō 0.52
BSSvs base
21.0%
improvement over base rate
ReliabilityREL
0.0033
miscalibration · want ↓
ResolutionRES
0.0550
decisiveness · want ↑
Log lossLL
0.5867
cross-entropy
AUCROC
0.771
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)
BLEEDING · PF 0.88 · expectancy -0.061R180 trades · win 48.3% · Sharpe -0.059
Total P/Lnet
-$2,747
on $45,000 cycled
Win ratehit %
48.3%
87 W / 93 L
Profit factorPF
0.88
$ won / $ lost
Expectancyper trade
-$15.26
avg $ per position
R-expectancyper risk
-0.061R
in units of risk taken
Avg win / losspayoff
$235.67 / -$250.00
ratio 0.94 : 1
Sharpe / traderisk-adj
-0.059
μR / σR
Closing line valueCLV
+2.12 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.