POLYMARKET · PREDICTION MARKET · SPORTS
Will Charles Leclerc be the 2026 F1 Drivers' Champion?
2.5¢
97.5¢
▸ Advanced metrics · M2M bundle
polymarket · will-charles-leclerc-be-the-2026-f1-drivers-champion · fresh · feed 0s old24h sparkline · 60 pts▼ —
realized vol (ann.)
18.05%
max drawdown
0.00%
sharpe
—
ulcer index
0.00%
RMS drawdown
pain index
0.00%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
0.00%
cond. drawdown
gain/pain
—
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
—
upside/downside
roll spread
10.6 bps
implied (price-only)
bars used
122
store
spread
—
24h Δ
—
flow lean
—
carry
flat
signalNEUTRALconfidence 20%
Same bundle via M2M API:
/api/m2m/pm-will-charles-leclerc-be-the-2026-f1-drivers-champion/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
2.5¢
NO · live
97.5¢
25 ticks · last 2.45¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.166 / 1.00 bits (17%) · informative — one side favoured
YES2.5%2.5¢40.82×▬ +0.00pp
NO97.5%97.5¢1.03×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 230bp moved · peak 50bp · n=24 ticks
11ms
2.45¢ (2.45%)
97.55¢ (97.55%)
100.00%
0.000pp
$51.3k
$114.6k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 2.45¢
25 NO observations from clob.polymarket.com · last 97.55¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25STRONGLY RIGHT-SKEWED (G₁=1.27)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁1.269right-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂0.075mesokurtic · normal-like
−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: INDETERMINATE · weak signal at n=24
HURST EXPONENT [0, 1]
H = 0.720STRONGLY 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
2.45¢
97.55¢
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
YES40.82×(2¢)NO1.03×(98¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.166 bits (17% 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 · DISTANTresolves 2026-12-06 00:00 UTC
174days
03hrs
35min
YES →$1.00
NO →$0.00
current: $0.0245 · expected return per side: $0.98 on YES hit · $0.02 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 1.426 pp/day
now174.15d left1.426 pp/day×1.00
−25%130.61d left1.647 pp/day×1.15
−50%87.07d left2.017 pp/day×1.41
−75%43.54d left2.852 pp/day×2.00
−90%17.41d left4.509 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 · 8 down · best 0.50% · worst -0.30% · typical |Δ| 0.096%
§10 · Equity curve & underwater drawdown
final equity 1.0070 (0.70%) · max DD -0.35% · time-under-water 18/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 36.671 · range [-44.62, 57.80] · μ 12.401 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 27.87% · range [4.91%, 28.96%] · μ 13.00% · σ̂ scaled to annualised (×√8760)
latest 0.217 · |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)
2 of 6 REJECT · mixed evidence2 reject·4 pass·α = 0.05
Jarque-Bera
REJECT H₀***H₀: Δp ~ Normal(μ, σ²)
STATISTIC
17.5752
p-VALUE (log scale)
0.0002
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
6.5805
p-VALUE (log scale)
0.2528
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-0.9153
p-VALUE (log scale)
0.7832
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
1.3641
p-VALUE (log scale)
0.1725
KPSS (μ stationarity)
REJECT H₀*H₀: p IS level-stationary
STATISTIC
0.6253
p-VALUE (log scale)
0.0203
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-0.2959
p-VALUE (log scale)
0.7673
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 ≈ 4.00h (freq 0.250) · concentrates 21.5% of total energy · Σ|X̂|²/n = 3.042e-5
▸ Depth section using sovereign-store price series (122 bars · effective 1753297 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 174.1 d · σ/bar 0.014pp · expected |Δp| over horizon 0.88ppterminal variance p(1−p) = 0.0239 · n = 122✓ n = 122
μ per bar
+0.001pp
average Δp · drift
σ per bar
0.014pp
one-bar volatility · logit-free
Per-day movedaily
0.07pp
σ × √24
Per-horizon move174d
0.88pp
σ × √4179.590050277779
Terminal variancebinary
0.0239
p(1−p) at resolution
Current pricep
2.5¢
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 0.02pp · ES₉₅ 0.03pp · method parametric · drift-correcteddrift +0.001pp/bar · quantised: yes · median step 0.15pp · unique ratio 0.02⚠ low confidence · n < 200
VaR 95%
0.02pp
1.645·σ (parametric) of Δp
ES 95%
0.03pp
mean of the tail
Max drawdown
0.0pp
peak 2.3¢ → trough 2.3¢
Median step
0.15pp
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
2.5%
= price
Decimal oddsEU
40.816
total return per $1
AmericanUS
+3982
$100 wins $3982
FractionalUK
39.82 / 1
profit per $1 risked
Profit per $100stake
+$3981.63
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)
0.166 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.166 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
5.35 bit
self-information
Surprise · NO−log₂(1−p)
0.04 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
2709284834738258468924839192854829143750961889384406449536217899798175177890- NO token ID
46955778407439783468859052837985793249006652932715101652741257747909716565323- Snapshot fetched
- 2026-06-14 20:24:35 UTC
- Snapshot age
- 11ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 20:24:35 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
e301fdd84e5cea8148dc26c80eb05bf03927e88453f8239f9d4b2a6171af3b8e· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
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Also see: /arb opportunities · RSS feed · more in Sports
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
0.024500
(best bid + best ask) / 2
Spread
408.2bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.699
ask-heavy
Imbalance (top-5)
-0.228
ask-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-charles-leclerc-be-the-2026-f1-drivers-champion/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.034229 | 3971.10bp | 0.043000 | 19 | FILLED |
| BUY | $10.00K | 0.119276 | 38684.13bp | 0.394000 | 80 | FILLED |
| BUY | $100.00K | 0.543628 | 211888.97bp | 0.995000 | 99 | FILLED |
| SELL | $1.00K | 0.003553 | 8549.71bp | 0.002000 | 20 | FILLED |
| SELL | $10.00K | 0.001402 | 9427.91bp | 0.001000 | 21 | PARTIAL |
| SELL | $100.00K | 0.001402 | 9427.91bp | 0.001000 | 21 | PARTIAL |
Risk metrics
▸ sovereign store · 122 barsperiods/year ≈ 1.75MRealized vol (annualised)
760.51%
σ per bar = 0.005744
Mean return (annualised)
91546.62%
μ per bar = 0.000522
Sharpe (rf=0)
120.37
annualised; risk-free assumed zero
Max drawdown
0.00%
peak 0.02 → trough 0.02 over 0 bars
/api/asset/pm-will-charles-leclerc-be-the-2026-f1-drivers-champion/risk · same metrics, JSON