{ "title": "StatsAI AI model performance, October 2026", "description": "Aggregate accuracy of StatsAI football predictions compared with bookmaker odds without margin on the same events. Totals only: no users, tickets or individual matches.", "page": "https://statsai.bet/en/research/ai-model-performance-2026-10", "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/" }, "attribution": "StatsAI (statsai.bet)", "citation": "StatsAI (2026). AI model performance: October 2026. StatsAI Football Prediction Research. Published 2026-11-02. https://statsai.bet/en/research/ai-model-performance-2026-10", "methodVersion": 1, "generatedAt": "2026-11-02T07:00:27.117Z", "period": { "from": "2026-10-01", "to": "2026-10-31", "comparedFrom": "2026-10-01", "days": 31 }, "definitions": { "event": "One match in one market. The 1X2 probabilities form one distribution; over/under and yes/no are two sides of one estimate.", "brier": "Sum over outcomes of (p - y)^2, averaged over events. 0 is perfect, lower is better; an even 1X2 forecast scores 0.667, a 50:50 two-way forecast 0.5.", "logloss": "-ln of the probability given to the observed outcome, floored at 0.01.", "acc": "Share of events where the most likely outcome happened.", "rps": "Ranked probability score for 1X2 with the order 1 < X < 2.", "raw": "AI model probabilities as published.", "cal": "AI model after out-of-sample calibration: each day's curve is learned only from the previous 90 days.", "mkt": "Pre-match bookmaker odds (first recorded), margin removed proportionally. Only events with odds for every outcome and an overround between 1.00 and 1.40.", "base": "Constant forecast equal to the observed outcome frequencies in the same events.", "intervals": "calVsMkt, rawVsCal and tier diff: Brier difference (first minus second; positive means the second is better) with a 95% bootstrap interval over whole days (1,000 resamples, fixed seed).", "calibration": "Bins of 10 percentage points (i = bin index): pred = mean forecast, obs = observed frequency, ciLo/ciHi = 95% Wilson interval. 1X2 uses all three outcomes, two-way markets only the over/yes side. ece = expected calibration error as a fraction.", "competitionTiers": "Same compared events for 1X2, over/under 2.5 and BTTS, grouped by competition tier: top = first division, second = second division, lower = third tier and below, other = cups, national teams, confederation competitions, youth, reserves and women's football. Groups below minEvents should not be interpreted." }, "totals": { "rows": 174229, "rowsKnown": 173569, "rowsWithOdds": 139707, "matches": 16013, "unresolvedRows": 5710, "unresolvedMatches": 751, "events": 37477, "eventsCompared": 34294, "marginExcluded": 171 }, "markets": { "1x2": { "n": 12829, "raw": { "n": 12829, "brier": 0.6299711665444665, "logloss": 1.04822687649722, "acc": 0.4545171096733962, "rps": 0.2188571576612138 }, "cal": { "n": 12829, "brier": 0.6290562685374681, "logloss": 1.0456663657554524, "acc": 0.4545171096733962, "rps": 0.21900558960518723 }, "mkt": { "n": 12829, "brier": 0.5918725150583006, "logloss": 0.9917792013296814, "acc": 0.5164860862109284, "rps": 0.20512189753141416 }, "base": { "n": 12829, "brier": 0.6467372944733784, "logloss": 1.0691385320742675, "acc": 0.4414997271806064, "rps": 0.23094289411884894 }, "calVsMkt": { "d": 0.03718375347916764, "lo": 0.0318002597468488, "hi": 0.043388803436161485 }, "rawVsCal": { "d": 0.0009148980069980719, "lo": 0.00041754044089540633, "hi": 0.0014078675898937904 } }, "over_under_2_5": { "n": 8771, "raw": { "n": 8771, "brier": 0.5236838900923498, "logloss": 0.7306587406094484, "acc": 0.5408733325732528 }, "cal": { "n": 8771, "brier": 0.49548055006847647, "logloss": 0.6886118948025189, "acc": 0.5408733325732528 }, "mkt": { "n": 8771, "brier": 0.47569499970217893, "logloss": 0.6682643423266996, "acc": 0.5879603237943222 }, "base": { "n": 8771, "brier": 0.4947471963666502, "logloss": 0.6878851407429547, "acc": 0.5512484323338274 }, "calVsMkt": { "d": 0.019785550366297637, "lo": 0.016275738613079193, "hi": 0.02280580201510784 }, "rawVsCal": { "d": 0.028203340023873293, "lo": 0.02307682462742503, "hi": 0.03345950555252911 } }, "btts": { "n": 11492, "raw": { "n": 11492, "brier": 0.5212406543682564, "logloss": 0.723530579340236, "acc": 0.5241907413853115 }, "cal": { "n": 11492, "brier": 0.4951922766233574, "logloss": 0.6883126649851793, "acc": 0.5197528715628263 }, "mkt": { "n": 11492, "brier": 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{ "tier": "other", "markets": { "1x2": { "n": 2332, "cal": 0.6011217856866891, "mkt": 0.5622591267251679, "diff": { "d": 0.038862658961521246, "lo": 0.027828702659259484, "hi": 0.048890045390867165 } }, "over_under_2_5": { "n": 1405, "cal": 0.4901025360269456, "mkt": 0.46591541653302077, "diff": { "d": 0.024187119493924852, "lo": 0.015064175991283022, "hi": 0.035172869765084054 } }, "btts": { "n": 1942, "cal": 0.49747575807813715, "mkt": 0.48795013876013477, "diff": { "d": 0.009525619318002373, "lo": 0.0021203679798888213, "hi": 0.01891927043148745 } } } } ] }, "report": { "id": "2026-10", "from": "2026-10-01", "to": "2026-10-31", "publishedAt": "2026-11-02T07:13:21.782Z", "reportVersion": 1, "missingDays": [], "corrections": [], "verify": { "at": "2026-11-02T07:13:21.782Z", "by": "research_publish.py (GitHub Actions)", "maxDiff": 4.218847493575595e-15, "compared": 52 } }, "findings": [ { "key": "verdict", "text": "In October 2026, margin-free bookmaker odds were more accurate than the AI model in every market measured (4). On 1X2 the market's Brier score was 0.592 against 0.629 for the calibrated AI model (difference +0.037, 95% interval +0.032 to +0.043; 12,829 matches)." }, { "key": "calibration", "text": "Calibration helped most in over/under 2.5 goals: the Brier score fell from 0.524 for the raw model to 0.495 (improvement +0.028, 95% interval +0.023 to +0.033; 8,771 events)." }, { "key": "tiers", "text": "On 1X2, the Brier difference between the AI model and the market was lowest in “Third tier and below” (+0.030; 4,762 matches) and highest in “Top tier” (+0.046; 3,164 matches). The intervals overlap, so this difference between groups is not reliable." }, { "key": "ece", "text": "Calibration: on 1X2 the calibrated AI model was off by 0.72 percentage points on average (expected calibration error), the market by 0.62." } ] }