POLYMARKET · PREDICTION MARKET · SPORTS

Will Igor Thiago be the top goalscorer at the 2026 FIFA World Cup?

YES · live
0.9¢
NO · live
99.1¢

▸ Advanced metrics · M2M bundle

polymarket · will-igor-thiago-be-the-top-goalscorer-at-the-2026-fifa-world-cup · fresh · feed 0s old
realized vol (ann.)
—
max drawdown
—
sharpe
—
ulcer index
—
RMS drawdown
pain index
—
mean drawdown
mod. VaR 95%
—
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
—
cond. drawdown
gain/pain
—
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
—
upside/downside
roll spread
—
implied (price-only)
bars used
0
insufficient
spread
—
24h Δ
—
flow lean
—
carry
flat
signalNEUTRALconfidence 0%
  • insufficient history for risk metrics — directional read only
Same bundle via M2M API: /api/m2m/pm-will-igor-thiago-be-the-top-goalscorer-at-the-2026-fifa-world-cup/bundle · venue execution: polymarket →
LIVEPOLL0SRCFRESH30ms⏱--:--:-- UTC8NEXT8.0sUP0s--:--HIST0/30
▶ STREAMING·HYPERLIQUID·POLYMARKET·0 POLLS·SRC FRESH·UPTIME 0s·NEXT POLL 8.0s·CC0 OPEN DATA·HYPO.MARKETS·▶ STREAMING·HYPERLIQUID·POLYMARKET·0 POLLS·SRC FRESH·UPTIME 0s·NEXT POLL 8.0s·CC0 OPEN DATA·HYPO.MARKETS·
YES · live
0.9¢
NO · live
99.1¢
YES price · live 24h
n=25 · μ=0.0130 · σ=0.0077 · range [0.0085, 0.0370] · R²=0.412▼ FALLING -53.66%σ EXTREME 58.70%LAST 0.00950.03700.02990.02270.01560.0085μ = 0.0130max 0.0370min 0.0085dataMA(5)OLS R²=0.41μ lineμ ± σ bandmaxminlive endpoint
25 ticks · last 0.95¢
YES / NO split · live
YES 0.9%NO 99.1%NO99.1%99.05¢ · odds 1/1.01
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.077 / 1.00 bits (8%) · informative — one side favoured
YES
0.9%0.9¢105.26×▬ +0.00pp
NO
99.1%99.1¢1.01×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Per-tick activity · |Δp| in basis points · live
n=24 · Σ=510 · μ=21.3 · σ=50.7 · CV=2.38BURSTY · concentratedcumulative energy ↗ · 50% by h=4056113169225μ = 2122550%h1h5h9h13h17h21#1 peak#2-3> μactivequietμ linecum energy
Σ 510bp moved · peak 225bp · n=24 ticks
Live numerics · pulse on poll
LIVE NUMERICS8 metrics·POLL 0
⏱snapshot age
30ms
▲YES mid
0.95¢ (0.95%)
▼NO mid
99.05¢ (99.05%)
ΣΣ sides
100.00%
⚖arb gap
0.000pp
$24h vol $
$29.0k
≋liquidity $
$121.5k
◳history points
25 ticks (live)

§1 · 24h price history (YES + NO tokens)

YES price · CLOB mid
n=25 · μ=0.0130 · σ=0.0077 · range [0.0085, 0.0370] · R²=0.412▼ FALLING -53.66%σ EXTREME 58.70%LAST 0.00950.03700.02990.02270.01560.0085μ = 0.0130max 0.0370min 0.0085dataMA(5)OLS R²=0.41μ lineμ ± σ bandmaxmin
25 YES observations from clob.polymarket.com · last 0.95¢
NO price · CLOB mid
n=25 · μ=0.9870 · σ=0.0077 · range [0.9630, 0.9915] · R²=0.412▲ RISING +1.12%σ LOW 0.78%LAST 0.99050.99150.98440.97720.97010.9630μ = 0.9870max 0.9915min 0.9630dataMA(5)OLS R²=0.41μ lineμ ± σ bandmaxmin
25 NO observations from clob.polymarket.com · last 99.05¢

§2 · Distribution of Δp

Histogram of hourly increments
n=24 · 10 bins · μ=-0.0002 · σ=0.0049 · skew=-2.64 (left-skewed) · kurt=11.23 (leptokurtic (fat tails))201510501-2.08ppbin -2.08pp · n=1 · 5.0% peakbin -2.08pp · n=1 · 5.0% peak-1.73pp-1.38pp-1.03pp-0.67pp1-0.32ppbin -0.32pp · n=1 · 5.0% peakbin -0.32pp · n=1 · 5.0% peak200.03ppbin 0.03pp · n=20 · 100.0% peakbin 0.03pp · n=20 · 100.0% peak10.38ppbin 0.38pp · n=1 · 5.0% peakbin 0.38pp · n=1 · 5.0% peak0.73pp11.07ppbin 1.07pp · n=1 · 5.0% peakbin 1.07pp · n=1 · 5.0% peakμΔ < 0 · loss barsΔ ≈ 0 · flatΔ > 0 · gain barsN(μ,σ²) referenceμ line · ±σ band shaded
n=24
Q-Q plot · standardised Δp vs N(0,1)
n=24 · skew=-2.27 · kurt=10.19 · near 6 / mid 16 / far 2 · OLS slope=0.74 intercept=-0.00LEPTOKURTIC — FAT TAILSTHIN UPPER TAILLOWER TAIL NORMAL-3σ-3σ-2σ-2σ-1σ-1σ+0σ+0σ+1σ+1σ+2σ+2σ+3σ+3σΔ=-2.06σsample ↓marginal: sample bars + theoretical N(0,1) curve →theoretical Φ⁻¹(p) →↑ sample z-quantile|Δ| < 0.3σ · on the line|Δ| < 1σ · moderate|Δ| ≥ 1σ · outliery = x refOLS fit
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.

§3 · Sample moments

Descriptive statistics · 5-number summary · shape diagnostics
SAMPLE MOMENTS · N=25LEPTOKURTIC · FAT TAILS (G₂=3.16)
μ MEAN1.30¢95% CI: [1.00¢, 1.60¢]
σ STD DEV0.77ppσ² = 0.586 · CV = 58.70%
med MEDIAN1.05¢Q₁ 0.90¢ · Q₃ 1.20¢
FIVE-NUMBER SUMMARY · BOX PLOTrange 2.85pp · IQR 0.30pp (1.349σ→1.03pp)
min 0.85¢Q₁ 0.90¢med 1.05¢Q₃ 1.20¢max 3.70¢μ
SKEWNESS · G₁2.083right-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂3.163leptokurtic · fat tails
−30+2+4+6
μ ↔ medianμ > med · right-tailed|μ−med| / σ = 0.33
σ × 1.349 ↔ IQRdiverges from normalratio = 3.44
range ↔ σconcentrated (range < 4σ)range / σ = 3.72
μ = mean YES probability · σ = standard deviation · 95% CI = μ ± 1.96·SE. Skew/kurt diagnose departure from normality.

§5 · Time-series structure

Regime & autocorrelation diagnostics
TIME-SERIES STRUCTUREREGIME: MEAN-REVERTING · ADF rejects unit root
ρ(1) AUTOCORR+0.116within white-noise band
ρ(2) AUTOCORR-0.477lag-2 dependence detected
H · HURST EXPONENT0.998strongly persistent
OLS TREND · t-STAT-4.016significant @ α=0.05
HURST EXPONENT [0, 1]strongly persistent · H = 0.998
H = 0.998STRONGLY PERSISTENT
0
anti-persistent0.45
mean-reverting0.5
random walk0.55
persistent1
strongly trending
AUTOCORRELATION FUNCTION · ρ(k) for k=1..51 SIGNIFICANT LAG · k=2
k=1+0.116k=2 ★-0.477k=3-0.151k=4+0.016k=5+0.0180+1−1+0.41−0.41+ momentum (ρ > +0.41)− reversal (ρ < −0.41)noise (within band)±2/√n threshold
OLS TREND · t-STAT · [-5, +5]SIGNIFICANT @ 1% (|t|=4.02)
−5 reject−1.960 retain H₀+1.96+5 reject
REGIME CLASSIFICATIONMEAN-REVERTING · ADF rejects unit rootfrom Hurst + ρ(1) joint diagnosis
PREDICTABILITY · score 1.00very high · strong structure|ρ(1)| + 2·|H − 0.5| heuristic
TREND SIGNIFICANCESIGNIFICANT @ 1% (|t|=4.02)α=0.05 critical |t|=1.96 · α=0.01 |t|=2.58
ρ(k) = lag-k sample autocorrelation · H = R/S Hurst exponent · t = OLS-trend t-statistic. Significance bands at ±2/√n approximate the 95% white-noise envelope. α=0.05 critical |t|=1.96; α=0.01 |t|=2.58.

§6 · Microstructure

Market quality · two-sided pricing · activity
MICROSTRUCTURE · MARKET QUALITYPERFECT · ARB-FREE Σ=100.00%
MARKET ID2069648
SLUGwill-igor-thiago…fa-world-cup
CATEGORYSports
TWO-SIDED PRICINGFAVOURS NO (99¢)
PRIMARY · YES0.95¢implied prob 0.95% · decimal odds 105.26×
COUNTER · NO99.05¢implied prob 99.05% · decimal odds 1.01×
0.95¢
99.05¢
Σ-SIDES ARBITRAGE TESTPERFECT · ARB-FREE Σ=100.00%
0%50%100% · target110%
Σ = 100.00% · |1 − Σ| = 0.000pp
24H ACTIVITY · LIQUIDITYACTIVE
24H VOLUME29.00k USD 24h
LIQUIDITY121.52k USD
MARKET QUALITYPERFECT · ARB-FREE Σ=100.00%|1−Σ| ≤ 0.5pp ⇒ fair · > 2pp ⇒ inefficient
PRICING SKEWFAVOURS NO (99¢)|primary − counter| = 0.981 · entropy 0.077 bits
LIQUIDITY DEPTHACTIVE100k+ deep · 10k+ active · 1k+ modest · 100+ thin
Σ-sides = YES + NO implied probabilities. Perfect arb-free Σ = 100%. |1−Σ| > 2pp suggests synthetic outright arbitrage.

§7 · Position sizing & edge analysis

Probability split · YES vs NO · Kelly · entropy · arbitrage
FAIR MARKET · no edge
YES 0.9%NO 99.1%YES p̂0.9%H = 0.077 / 1.00 bits
Probability scale (YES)
0%25%50%
fair75%100%
Implied decimal odds
YES105.26×(1¢)NO1.01×(99¢)
Kelly bet-size (% of bankroll) K* = 0.00%
K* full
0.00%
½K half
0.00%
¼K quarter
0.00%
Entropy H(p̂) = 0.077 bits (8% of max) · informative — one side strongly favoured
0 (certain)0.250.50.751.00 (max)
Σ-sides = 100.00% · |1 − Σ| = 0.00pp · tight cross-venue rounding
K* full = (b·p − q)/b · ½K and ¼K are conservative fractions of the full-Kelly bet. Entropy in bits — log₂(2)=1 is maximum uncertainty for a binary market.

§8 · Time decay & θ projection

Time decay & theta projection
⏱ URGENCY · DISTANTresolves 2026-07-20 00:00 UTC
35days
04hrs
51min
35.20 days·θ = 3.750 pp/day
YES →$1.00(P = 0.9%)
NO →$0.00(P = 99.1%)
current: $0.0095 · expected return per side: $0.99 on YES hit · $0.01 on NO hit
0%25%50%75%100%YES $1NO $0NOW+17.6dRESOLVESP projection · σ=0.77% · path funnel to settle at YES=1 or NO=0
Theta progression · θ ∝ σ / √t_remainingθ_now = 3.750 pp/day
now35.20d left
3.750 pp/day×1.00
−25%26.40d left
4.330 pp/day×1.15
−50%17.60d left
5.303 pp/day×1.41
−75%8.80d left
7.499 pp/day×2.00
−90%3.52d left
11.857 pp/day×3.16
θ approximation: σ/√T (expected daily move magnitude). The cone shows ±√(p̂(1−p̂)) widening as time decays, funneling to {0, 1} at resolution. Theta accelerates as √(t_left)→0.

§9 · Hourly return heatmap

24-hour signed Δp grid · green = up · red = down
HOURLY RETURN HEATMAP · n=24 bars · best 1.25% · worst -2.25% · typical |Δ| 0.21%BEARISH SESSION -1.10%BEST+1.25%2hWORST-2.25%4hTYPICAL |Δ|0.21%mean absoluteCUMULATIVE-1.10%Σ signed ΔSTREAK▬ 0flat-runASIA · 00-08 UTCμ -0.12% · Σ -0.85%EUROPE · 08-16 UTCμ -0.04% · Σ -0.35%US · 16-24 UTCμ +0.01% · Σ +0.10%CUMULATIVE Δ PATH · final -1.10%+1.65%-1.20%0.40% · 1h0.40% · 1h0.40%1h1.25% · 2h1.25% · 2h1.25%2h★ BEST-0.35% · 3h-0.35% · 3h-0.35%3h-2.25% · 4h-2.25% · 4h-2.25%4h▼ WORST0.00% · 5h0.00% · 5h·5h0.10% · 6h0.10% · 6h0.10%6h0.00% · 7h0.00% · 7h·7h0.00% · 8h0.00% · 8h·8h-0.05% · 9h-0.05% · 9h-0.05%9h0.00% · 10h0.00% · 10h·10h-0.10% · 11h-0.10% · 11h-0.10%11h-0.10% · 12h-0.10% · 12h-0.10%12h-0.05% · 13h-0.05% · 13h-0.05%13h-0.05% · 14h-0.05% · 14h-0.05%14h0.00% · 15h0.00% · 15h·15h0.00% · 16h0.00% · 16h·16h0.00% · 17h0.00% · 17h·17h0.00% · 18h0.00% · 18h·18h0.00% · 19h0.00% · 19h·19h0.10% · 20h0.10% · 20h0.10%20h0.15% · 21h0.15% · 21h0.15%21h-0.10% · 22h-0.10% · 22h-0.10%22h-0.05% · 23h-0.05% · 23h-0.05%23h0.00% · 24h0.00% · 24h·24hTIME PATTERNUS-led (+0.10%)RUNSup max 2 · down max 4BREADTH21% up · 38% down · 42% flat
5 up bars · 9 down · best 1.25% · worst -2.25% · typical |Δ| 0.213%

§10 · Equity curve & underwater drawdown

Cumulative compounded return + running peak-to-trough
EQUITY & DRAWDOWN ANALYSIS · n=25 barsLOSS WITH MODERATE DD (-1.13%)FINAL-1.13%MAX DD-2.84%RECOVERYONGOING · 22 barsMAX RUN-UP+1.66%UNDERWATER22/25 (88%)STREAK▬ 0EQUITY CURVE · end 0.9887 · peak 1.0166 · range [0.9877, 1.0166]1.01660.9877break-even = 1★ PEAK 1.0166UNDERWATER DRAWDOWN · max -2.84% · moderate0%-2.84%▼ TROUGH -2.84%TOP DRAWDOWN PERIODS · 1 total#1 -2.84%bar 4-25 · 22 bars · ONGOINGDD SEVERITYmoderate (max -2.84%)RECOVERYongoing · 22 barsTIME UNDER WATER88% of session · 22/25 bars
final equity 0.9887 (-1.13%) · max DD -2.84% · time-under-water 22/25 bars

§11 · Rolling-window statistics (w = 6 bars)

Rolling annualised Sharpe ratio · green positive · red negative
n=19 · +6 / −13 (32% positive) · μ=-34.77 · σ=56.94UNPROFITABLE STRATEGYLAST 16.76 (+0.90σ vs μ)145.0672.530.00-72.53-145.06μ = -34.77-11.37-11.37-17.11-17.11-42.79-42.79-37.14-37.1415.8715.87-11.74-11.74-79.33-79.33-104.64-104.64-145.06-145.06-104.64-104.64-104.64-104.64-76.42-76.42-60.42-60.42-38.21-38.2138.2138.2158.6858.6826.5826.5816.7616.7616.7616.76v > 0 · positivev < 0 · negativeμ mean lineμ ± σ bandlatest bar (outlined)
latest 16.756 · range [-145.06, 58.68] · μ -34.771 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
Rolling annualised volatility (%)
n=19 · μ=24.3660 · σ=38.8794 · range [1.9105, 109.1260] · R²=0.483▼ FALLING -92.02%σ EXTREME 159.56%LAST 8.7132109.126082.322155.518328.71441.9105μ = 24.3660max 109.1260min 1.9105dataMA(3)OLS R²=0.48μ lineμ ± σ bandmaxmin
latest 8.71% · range [1.91%, 109.13%] · μ 24.37% · σ̂ scaled to annualised (×√8760)
Rolling lag-1 autocorrelation ρ(1)
n=19 · +10 / −9 (53% positive) · μ=0.083 · σ=0.195CLOSE TO MARTINGALELAST 0.071 (-0.06σ vs μ)0.5000.2500.000-0.250-0.500μ = 0.0830.0940.094-0.033-0.033-0.068-0.068-0.023-0.023-0.040-0.040-0.022-0.0220.1670.1670.0000.000-0.069-0.0690.0000.0000.5000.5000.3670.3670.4170.417-0.033-0.033-0.033-0.0330.4120.412-0.177-0.1770.0510.0510.0710.071v > 0 · positivev < 0 · negativeμ mean lineμ ± σ bandlatest bar (outlined)
latest 0.071 · |ρ| > 0.3 ⇒ regime with persistence (ρ > 0) or reversal (ρ < 0) · |ρ| ≤ 0.1 = consistent with random walk

§12 · Hypothesis tests (α = 0.05)

Formal inference at 5% significance
2 of 6 REJECT · mixed evidence2 reject·4 pass·α = 0.05
𝒩

Jarque-Bera

REJECT H₀***

H₀: Δp ~ Normal(μ, σ²)

STATISTIC
192.0577
p-VALUE (log scale)
< 0.0001
α
10⁻⁴10⁻³10⁻²10⁻¹1
p < α · rejection zonenon-normal · fat tails or skew present
ρ

Ljung-Box(h=5)

FAIL TO REJECTns

H₀: No serial autocorrelation up to lag 5

STATISTIC
7.4984
p-VALUE (log scale)
0.1849
α
10⁻⁴10⁻³10⁻²10⁻¹1
p ≥ α · null retainedconsistent with white noise
Ψ

Dickey-Fuller (τ_μ)

FAIL TO REJECTns

H₀: p has a unit root (non-stationary)

STATISTIC
-1.9019
p-VALUE (log scale)
0.3420
α
10⁻⁴10⁻³10⁻²10⁻¹1
p ≥ α · null retainedrandom-walk behaviour (crit ≈ -2.86)
±

Wald-Wolfowitz runs

FAIL TO REJECTns

H₀: Sign sequence of Δ is random

STATISTIC
-0.8719
p-VALUE (log scale)
0.3833
α
10⁻⁴10⁻³10⁻²10⁻¹1
p ≥ α · null retainedsigns appear random (6 runs)
χ

KPSS (μ stationarity)

REJECT H₀*

H₀: p IS level-stationary

STATISTIC
0.5237
p-VALUE (log scale)
0.0363
α
10⁻⁴10⁻³10⁻²10⁻¹1
p < α · rejection zonenon-stationary (crit 0.463)
χ

Variance ratio q=3

FAIL TO REJECTns

H₀: Δp is a random walk · VR = 1

STATISTIC
-0.8766
p-VALUE (log scale)
0.3807
α
10⁻⁴10⁻³10⁻²10⁻¹1
p ≥ α · null retainedVR 0.733 ≈ 1 (RW behaviour)
Each row states an explicit null H₀, the test statistic, an approximated p-value, and the decision. REJECT means evidence against H₀. KPSS complements ADF (rejecting both ⇒ ambiguous; rejecting one ⇒ clean verdict).

§13 · Spectral analysis (DFT periodogram)

Power spectrum of Δp · ‖X̂(k)‖²/n
n=12 bins · noise floor μ=2.91e-5 · top T=4.80h (19.3%) · top-3 cover 51.0%1 SIGNIFICANT CYCLEcumulative energy ↗ (1 bin above 2× noise)6.7e-55.1e-53.4e-51.7e-50.0e+0μ noise floor2× noise (significance)period 24.0 · power 1.04e-5 · 3.0% energyperiod 24.0 · power 1.04e-5 · 3.0% energyperiod 12.0 · power 1.58e-5 · 4.5% energyperiod 12.0 · power 1.58e-5 · 4.5% energyperiod 8.0 · power 3.65e-5 · 10.5% energyperiod 8.0 · power 3.65e-5 · 10.5% energyperiod 6.0 · power 5.70e-5 · 16.3% energyperiod 6.0 · power 5.70e-5 · 16.3% energyperiod 4.8 · power 6.75e-5 · 19.3% energyperiod 4.8 · power 6.75e-5 · 19.3% energyperiod 4.0 · power 5.34e-5 · 15.3% energyperiod 4.0 · power 5.34e-5 · 15.3% energyperiod 3.4 · power 3.88e-5 · 11.1% energyperiod 3.4 · power 3.88e-5 · 11.1% energyperiod 3.0 · power 3.11e-5 · 8.9% energyperiod 3.0 · power 3.11e-5 · 8.9% energyperiod 2.7 · power 2.16e-5 · 6.2% energyperiod 2.7 · power 2.16e-5 · 6.2% energyperiod 2.4 · power 8.63e-6 · 2.5% energyperiod 2.4 · power 8.63e-6 · 2.5% energyperiod 2.2 · power 4.17e-6 · 1.2% energyperiod 2.2 · power 4.17e-6 · 1.2% energyperiod 2.0 · power 4.17e-6 · 1.2% energyperiod 2.0 · power 4.17e-6 · 1.2% energy50% by T=4.8h#1 dominantT=4.80h#2T=6.00h#3T=4.00hT=2hT=3hT=4hT=6hT=8hT=12hT=16hT=24h← shorter cycle (high freq · Nyquist=½) · period T (bars per cycle) · longer cycle (low freq · 1/n) →#1 dominant#2 peak#3 peak> 2× noisenoiseμ floor2μ sig.cum energy
dominant period ≈ 4.80h (freq 0.208) · concentrates 19.3% of total energy · Σ|X̂|²/n = 3.491e-4

§14 · Honest position analytics

A binary-market analytics module framed in horizon time (days to resolution, not annualised). Estimators that need a model probability q as a first-class input (Kelly, KL divergence, Bayesian posterior, Mark-to-Market MC) only render when q is provided externally. Sweep an exploratory q at the interactive simulator →

§15 · Horizon returns

Returns · per bar / per day / per horizon
Horizon 35.2 d · σ/bar 0.549pp · expected |Δp| over horizon 15.97ppterminal variance p(1−p) = 0.0094 · n = 25⚠ low confidence · n < 100
μ per bar
-0.046pp
average Δp · drift
σ per bar
0.549pp
one-bar volatility · logit-free
Per-day movedaily
2.69pp
σ × √24
Per-horizon move35d
15.97pp
σ × √844.8537105555556
Terminal variancebinary
0.0094
p(1−p) at resolution
Current pricep
0.9¢
latest snapshot
Note: annualised Sharpe/Sortino are omitted — they are not meaningful for a bounded fixed-horizon binary contract that snaps to {0, 1} at resolution.
Annualised metrics are intentionally omitted — they don't apply to bounded probability series that resolve at a fixed date.

§16 · Tail risk

VaR · ES · max drawdown
VaR₉₅ 0.27pp · ES₉₅ 1.25pp · method empirical · drift-correcteddrift -0.046pp/bar · quantised: no · median step 0.05pp · unique ratio 0.48⊘ disabled · n < 30
VaR 95%
0.27pp
5th percentile of Δp
ES 95%
1.25pp
mean of the tail
Max drawdown
77.0pp
peak 3.7¢ → trough 0.9¢
Median step
0.05pp
price bucket granularity
Empirical quantiles unless the price series is bucketed (PM cent grid), in which case parametric N(0, σ²) is used to avoid grid collapse.

§17 · Odds conversion

Odds conversion · every dialect a bettor thinks in
Implied probabilityP
0.9%
= price
Decimal oddsEU
105.263
total return per $1
AmericanUS
+10426
$100 wins $10426
FractionalUK
104.26 / 1
profit per $1 risked
Profit per $100stake
+$10426.32
clean dollar framing
-1000-5000+500+1000020406080100you · 0.9%implied probability (%)American odds
underdog (+)favorite (-)your price
Price → implied probability → decimal odds → American moneyline → fractional. Five views of the same number, plus the moneyline curve.

§18 · Binary entropy

Binary entropy · uncertainty as bits of information
Market entropyH(p)
0.077 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.077 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
6.72 bit
self-information
Surprise · NO−log₂(1−p)
0.01 bit
self-information
0.000.260.530.791.050.00.20.40.60.81.0marketmodelprobabilityH (bits)
Market entropy only — model entropy requires an external q.

§19 · Model-dependent surfaces

§ Edge / Kelly / KL · no model probability provided

External model required

The position-economics, Kelly, KL-divergence, Bayesian and Monte-Carlo surfaces require a model probability q as input — a number independent of the market price p.

The previous build defaulted q to a tape-momentum heuristic derived from p; that produces apparent edge that is structurally guaranteed to be small and is not a useful skill signal. The auto-derived path has been removed.

To explore these surfaces with a hypothetical q, open the interactive simulator and drag the MODEL P(YES) slider. To wire a real model, POST to the NOSTRADAMUS hook (TBD) or pass ?q=… on the simulator URL.

§∞ · Provenance & attestation

Upstream (snapshot)
gamma-api.polymarket.com
Upstream (history)
clob.polymarket.com
YES token ID
90741541228100749175171313078520271556278752454644157332018302001113380633468
NO token ID
21298895115343355354430757718637119478746580906962159004285654829505235891139
Snapshot fetched
2026-06-14 19:08:46 UTC
Snapshot age
30ms
History points
25 CLOB mids
Page rendered
2026-06-14 19:08:46 UTC
Storage policy
no persistence — fetched on every request
SHA-256 attestation
29a04e1db721d19abd5ac0296d8942618465df7455cbabc17d8cbc0a7f5da766 · deterministic hash of source snapshot
Open data licence
CC0 / public domain

§∞-2 · Related markets · explore more

HL PREDGermany100.0¢HL PREDSpain89.5¢HL PREDUruguay66.9¢POLYMARKETWill Curaçao win on 2026-06-14?0¢POLYMARKETWill Australia win the 2026 FIFA World Cup?0¢POLYMARKETWill Scotland win the 2026 FIFA World Cup?0¢HL PERPBTC-USD perpetual$63,751.5HL PERPETH-USD perpetual$1,661.65HL PERPHYPE-USD perpetual$60.32

Also see: /arb opportunities · RSS feed · more in Sports

Market depth

▸ live order book · Polymarket YES
Depth within 1bp
$0
bid $0 · ask $0
Depth within 5bp
$0
bid $0 · ask $0
Depth within 10bp
$0
bid $0 · ask $0
Depth within 50bp
$0
bid $0 · ask $0
Mid price
0.009500
(best bid + best ask) / 2
Spread
1052.6bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.992
ask-heavy
Imbalance (top-5)
-0.398
ask-heavy top-of-book

Slippage scenarios

▸ live book walk · Polymarket YES

Simulating a market order at three notionals against the live book. Slippage = avg execution price vs. mid, in basis points. Worst fill = price of the deepest level touched. Live JSON: /api/asset/pm-will-igor-thiago-be-the-top-goalscorer-at-the-2026-fifa-world-cup/slippage?size=10000&side=buy

SideNotionalAvg fillSlippageWorst fillLevelsStatus
BUY$1.00K0.0122372880.78bp0.04900013FILLED
BUY$10.00K0.10412699606.11bp0.87800059FILLED
BUY$100.00K0.521732539191.94bp0.96000071FILLED
SELL$1.00K0.0043815388.18bp0.0010009PARTIAL
SELL$10.00K0.0043815388.18bp0.0010009PARTIAL
SELL$100.00K0.0043815388.18bp0.0010009PARTIAL

Risk metrics

▸ upstream candles · 25 bars
Realized vol (annualised)
—
σ per bar = 0.255939
Mean return (annualised)
—
μ per bar = -0.032047
Sharpe (rf=0)
—
annualised; risk-free assumed zero
Max drawdown
77.03%
peak 0.04 → trough 0.01 over 12 bars

/api/asset/pm-will-igor-thiago-be-the-top-goalscorer-at-the-2026-fifa-world-cup/risk · same metrics, JSON