POLYMARKET · PREDICTION MARKET · IEM COLOGNE MAJOR 2026 WINNER
Will The MongolZ win IEM Cologne Major 2026?
1.1¢
99.0¢
▸ Advanced metrics · M2M bundle
polymarket · will-the-mongolz-win-iem-cologne-major-2026 · fresh · feed 0s old24h sparkline · 60 pts▲ 133.33%
realized vol (ann.)
24.23%
max drawdown
54.55%
sharpe
—
ulcer index
33.45%
RMS drawdown
pain index
27.31%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
54.55%
cond. drawdown
gain/pain
0.64
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
0.64
upside/downside
roll spread
5.0 bps
implied (price-only)
bars used
2000
store
spread
—
24h Δ
133.33%
flow lean
—
carry
flat
signalNEUTRALconfidence 25%
- 24h change +133.33%
Same bundle via M2M API:
/api/m2m/pm-will-the-mongolz-win-iem-cologne-major-2026/bundle · venue execution: polymarket →LIVEPOLL0SRCFRESH⏱--:--:-- UTCNEXT8.0sUP0s--:--HIST
▶ 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
1.1¢
NO · live
99.0¢
25 ticks · last 1.05¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.084 / 1.00 bits (8%) · informative — one side favoured
YES1.1%1.1¢95.24×▬ +0.00pp
NO99.0%99.0¢1.01×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 550bp moved · peak 120bp · n=24 ticks
3ms
1.05¢ (1.05%)
98.95¢ (98.95%)
100.00%
0.000pp
$70.3k
$122.7k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 1.05¢
25 NO observations from clob.polymarket.com · last 98.95¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25PLATYKURTIC · THIN TAILS (G₂=-1.19)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁0.266approximately symmetric
−3−10+1+3
EXCESS KURTOSIS · G₂-1.188platykurtic · thin tails
−30+2+4+6
μ = mean YES probability · σ = standard deviation · 95% CI = μ ± 1.96·SE. Skew/kurt diagnose departure from normality.
§5 · Time-series structure
TIME-SERIES STRUCTUREREGIME: MEAN-REVERTING · ρ(1) -0.30 + ADF rejected
HURST EXPONENT [0, 1]
H = 0.887STRONGLY PERSISTENT
0
anti-persistent0.45
mean-reverting0.5
random walk0.55
persistent1
strongly trending
anti-persistent0.45
mean-reverting0.5
random walk0.55
persistent1
strongly trending
AUTOCORRELATION FUNCTION · ρ(k) for k=1..5
OLS TREND · t-STAT · [-5, +5]
−5 reject−1.960 retain H₀+1.96+5 reject
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
MICROSTRUCTURE · MARKET QUALITYPERFECT · ARB-FREE Σ=100.00%
TWO-SIDED PRICING
1.05¢
98.95¢
Σ-SIDES ARBITRAGE TEST
Σ = 100.00% · |1 − Σ| = 0.000pp
24H ACTIVITY · LIQUIDITY
Σ-sides = YES + NO implied probabilities. Perfect arb-free Σ = 100%. |1−Σ| > 2pp suggests synthetic outright arbitrage.
§7 · Position sizing & edge analysis
FAIR MARKET · no edge
Probability scale (YES)
Implied decimal odds
YES95.24×(1¢)NO1.01×(99¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.084 bits (8% of max) · informative — one side strongly favoured
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
⏱ URGENCY · LOWresolves 2026-06-21 00:00 UTC
6days
07hrs
52min
YES →$1.00
NO →$0.00
current: $0.0105 · expected return per side: $0.99 on YES hit · $0.01 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 3.092 pp/day
now6.33d left3.092 pp/day×1.00
−25%4.75d left3.570 pp/day×1.15
−50%3.16d left4.372 pp/day×1.41
−75%1.58d left6.183 pp/day×2.00
−90%15.19h left9.776 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
7 up bars · 11 down · best 1.20% · worst -0.45% · typical |Δ| 0.229%
§10 · Equity curve & underwater drawdown
final equity 1.0058 (0.58%) · max DD -1.59% · time-under-water 22/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 14.393 · range [-118.65, 49.23] · μ -19.605 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 25.36% · range [11.07%, 62.20%] · μ 34.61% · σ̂ scaled to annualised (×√8760)
latest -0.600 · |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)
1 of 6 REJECT · mixed evidence1 reject·5 pass·α = 0.05
Jarque-Bera
REJECT H₀***H₀: Δp ~ Normal(μ, σ²)
STATISTIC
34.0446
p-VALUE (log scale)
< 0.0001
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
6.3482
p-VALUE (log scale)
0.2730
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-1.7590
p-VALUE (log scale)
0.4100
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
-0.7977
p-VALUE (log scale)
0.4250
KPSS (μ stationarity)
FAIL TO REJECTnsH₀: p IS level-stationary
STATISTIC
0.2169
p-VALUE (log scale)
0.3272
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-0.2814
p-VALUE (log scale)
0.7784
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)
dominant period ≈ 2.00h (freq 0.500) · concentrates 21.8% of total energy · Σ|X̂|²/n = 1.953e-4
▸ Depth section using sovereign-store price series (3819 bars · effective 1752908 bars/year) — annualisation reflects native polling cadence, not upstream timeframes.
§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
Horizon 6.3 d · σ/bar 0.031pp · expected |Δp| over horizon 0.38ppterminal variance p(1−p) = 0.0104 · n = 3819✓ n = 3819
μ per bar
+0.000pp
average Δp · drift
σ per bar
0.031pp
one-bar volatility · logit-free
Per-day movedaily
0.15pp
σ × √24
Per-horizon move6d
0.38pp
σ × √151.8772997222222
Terminal variancebinary
0.0104
p(1−p) at resolution
Current pricep
1.1¢
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
VaR₉₅ 0.05pp · ES₉₅ 0.06pp · method parametric · drift-correcteddrift +0.000pp/bar · quantised: yes · median step 0.05pp · unique ratio 0.01✓ n = 3819
VaR 95%
0.05pp
1.645·σ (parametric) of Δp
ES 95%
0.06pp
mean of the tail
Max drawdown
68.8pp
peak 2.4¢ → trough 0.8¢
Median step
0.05pp
price bucket granularity
Price series is bucketed (cent grid). Empirical quantiles collapse to grid points — parametric N(0, σ²) used instead.
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
Implied probabilityP
1.1%
= price
Decimal oddsEU
95.238
total return per $1
AmericanUS
+9424
$100 wins $9424
FractionalUK
94.24 / 1
profit per $1 risked
Profit per $100stake
+$9423.81
clean dollar framing
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
Market entropyH(p)
0.084 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.084 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
6.57 bit
self-information
Surprise · NO−log₂(1−p)
0.02 bit
self-information
Market entropy only — model entropy requires an external q.
§19 · Model-dependent surfaces
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
50801649153958293956418061994996717803220332059031976556680274544927191488062- NO token ID
29755067231707120652907449593507911966171087079331630935217343418182812844443- Snapshot fetched
- 2026-06-14 16:07:21 UTC
- Snapshot age
- 3ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 16:07:21 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
fdc2dc7868aa264c493f7dda6f307b53b283ee4c0d89d33e3df7de22896ddf6a· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
§∞-2 · Related markets · explore more
HL PREDGermany94.5¢HL PREDSpain89.4¢HL PREDUruguay66.9¢POLYMARKETWill Australia win the 2026 FIFA World Cup?0¢POLYMARKETWill Scotland win the 2026 FIFA World Cup?0¢POLYMARKETWill Turkiye win the 2026 FIFA World Cup?1¢HL PERPBTC-USD perpetual$64,017HL PERPETH-USD perpetual$1,663.1HL PERPHYPE-USD perpetual$60.34
Also see: /arb opportunities · RSS feed · more in IEM Cologne Major 2026 Winner
Market depth
▸ live order book · Polymarket YESDepth 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
0.010500
(best bid + best ask) / 2
Spread
2857.1bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.972
ask-heavy
Imbalance (top-5)
+0.357
bid-heavy top-of-book
Slippage scenarios
▸ live book walk · Polymarket YESSimulating 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-the-mongolz-win-iem-cologne-major-2026/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.021515 | 10490.12bp | 0.033000 | 7 | FILLED |
| BUY | $10.00K | 0.084912 | 70869.03bp | 0.753000 | 49 | FILLED |
| BUY | $100.00K | 0.474532 | 441935.20bp | 0.999000 | 58 | FILLED |
| SELL | $1.00K | 0.005251 | 4998.94bp | 0.001000 | 9 | PARTIAL |
| SELL | $10.00K | 0.005251 | 4998.94bp | 0.001000 | 9 | PARTIAL |
| SELL | $100.00K | 0.005251 | 4998.94bp | 0.001000 | 9 | PARTIAL |
Risk metrics
▸ sovereign store · 3,819 barsperiods/year ≈ 1.75MRealized vol (annualised)
3564.69%
σ per bar = 0.026924
Mean return (annualised)
38900.87%
μ per bar = 0.000222
Sharpe (rf=0)
10.91
annualised; risk-free assumed zero
Max drawdown
68.75%
peak 0.02 → trough 0.01 over 2708 bars
/api/asset/pm-will-the-mongolz-win-iem-cologne-major-2026/risk · same metrics, JSON