No vertical equity curves. No 96% accuracy claims. Just the actual numbers, with the actual definitions.
Every staked bet logged automatically by the model runner, then settled match-by-match. Real money, not paper.
Bets won ÷ bets settled. For comparison, a coin-flip strategy run at ~2.0 odds breaks even at ~50%; we need ~52-55%+ to clear typical bookmaker margin depending on average odds taken.
Return on every stake across the full settled sample. We keep the focus on the rate, not the pennies — a young track record is best judged on ROI, strike rate, and closing-line value, not the size of the bankroll behind it. Settled results only; open futures positions (outrights still running) sit outside these numbers until they settle.
CLV measures whether our entry odds beat the final market price — the metric tipsters can't fake, which is exactly why we won't publish a soft one. We're re-verifying our closing-odds data for quality right now, so we're holding the CLV figure until it's clean. We'd rather show “under review” than a number we can't stand behind.
🇲🇽 Mexico Under 2.5 ✅ · 🇨🇦 Canada BTTS Yes ✅ · 🇺🇸 USA −0.5 ✅ · 🇶🇦 Qatar +1.5 ✅ · 🇧🇷 Brazil BTTS No ✅ · 🇪🇸 Spain BTTS No ✅ · 🇨🇮 Ivory Coast BTTS No ✅ · 🇫🇷 France −0.5 ✅ · 🏴 England Win-to-Nil ✅ · 🇿🇦 SA-Canada BTTS No ✅ · 🇫🇷 France Over 2.5 (Sweden) ✅
🇳🇱 Netherlands Under 2.5 ❌ · 🇬🇭 Ghana/Panama +0.5 ❌ · 🇫🇷 France Over 3.5 (Iraq) ❌ · 🏴 Kane 2+ SOT ❌ · 🇩🇪 Germany Win-to-Nil ❌ · 🇩🇪 Germany BTTS No ❌ · 🇫🇷 France Over 3.5 (Sweden) ❌ — we post the losses the same as the wins.
Eighteen model bets settled, eleven landed. England's win-to-nil over Panama — built on Panama failing to score a single goal all tournament — and France's −0.5 win over Norway kept the run going; the misses stay public too — France's 3-0 over Iraq fell a goal short of our Over line (the exact risk we flagged), and a Kane shots-on-target call lost. The Round of 32 then brought a double clean-sheet loss on Germany v Paraguay, where Paraguay scored and went on to knock Germany out, a lesson on filtering clean-sheet bets to genuinely toothless opponents. Then France's 3-0 win over Sweden split our two overs: the Over 2.5 landed comfortably while the Over 3.5 juice fell a goal short, a clean safe-won, juice-missed night that nudged the ROI down honestly. Non-model and for-fun bets are logged separately and stay out of the model's column, win or lose.
| Market | Verdict |
|---|---|
| Over/Under goals | Strongest edge — best-performing market across the track |
| BTTS (both teams to score) | Mid — selectively profitable, regional bias |
| 1X2 (match result) | Worst — efficient market, hardest to beat |
| Cards & corners | Backtested vs AFCON/CONCACAF — no per-match edge found, so we don't stake them |
Posting the worst market alongside the best is intentional. If a tipster only shows you their winners, they're showing you a survivor bias, not a strategy.
We use a multi-layer ensemble model. Layers include Dixon-Coles match probability fitting, Elo ratings, expected-goals divergence (rolling xG vs actual finishing), weather effects on goal totals, confirmed lineup/absence gating, referee discipline profiles, head-to-head venue patterns, and others.
We don't reveal the exact layer count, weights, thresholds, or layer names — that's the only piece of IP we have, and giving it away would kill the edge. What we do reveal: every bet, every edge %, the odds taken, every win and loss, in real time.
The bets are public. The results are settled in real time. The method stays private.