Best AI football prediction tools, rated on their records
We rate 25 AI football (soccer) prediction tools. 7 publish enough of a level-stakes record for a full Performance score; the strongest published record is OddAlerts Alpha (Performance 53/100, Transparency 90/100, self-reported). 3 of the 7 are in profit at level stakes and 7 are self-reported, so check the evidence line under each one. Below they are ranked by Performance, then Transparency score, then settled bets.
Can AI predict football?
AI can forecast football about as well as the betting market, but independent tests so far haven't found it beating the market once the bookmaker's margin is paid. Of the 25 AI football tools we rate, 7 publish enough for a full Performance score and 3 of those are in profit at level stakes (median ROI -1.29%); 18 don't publish enough to score.
Football is one of the most heavily bet and most efficiently priced sports, so the market's odds are a strong benchmark. An accuracy figure only means something next to the market's accuracy on the same matches: in the 2026 World Cup test, the market's favourite won 68% of matches, the same as the most accurate AI model.
What independent research says (4 studies)
- FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents (Ding, Guo and Xu, 2026; Prospective test, 104 matches)
Four frontier chatbots forecast all 104 matches of the 2026 World Cup before kick-off. None beat the betting market's Brier score (0.469 for the market against 0.471 for the best model). Virtual betting returns ranged from −18% to +10% over a single tournament, betting against the market lost money for all four, and a flat stake on the market favourite out-earned all four in absolute terms. The authors say per-model differences have wide intervals. - KellyBench: Can Language Models Beat the Market? (Grady et al., General Reasoning, 2026; Simulation with real market odds)
Frontier language models were asked to build betting strategies across a simulated 2023/24 English Premier League season using real market odds. Every model lost money over the season and several were ruined; the best (Claude Opus 4.6) averaged a return of −11%, and only 2 of 24 runs finished in profit. - Are Betting Markets Inefficient? Evidence From Simulations and Real Data (Winkelmann, Ötting, Deutscher and Makarewicz, 2024; 14 seasons, 5 leagues)
Across 14 seasons of the five biggest European football leagues, pricing inefficiencies showed up in single seasons but were not persistent or systematic, and simulations showed that such single-season effects are likely to appear by chance even in a fully efficient market. - Beating the bookies with their own numbers, and how the online sports betting market is rigged (Kaunitz, Zhong and Kreiner, 2017; Strategy test with real bets)
A strategy that bet when one bookmaker's football odds were out of line with the market average showed a profit in simulation and with real money, but the bookmakers limited the authors' accounts within months. It used the market's own prices, not a prediction model, as its estimate of the true chances.
Summaries checked against each source on 19 Oct 2026: the full paper where we could read it, otherwise the published abstract. Studies use their own samples and methods, so they aren't directly comparable with the records we score, and none tests the specific tools we list.
What we don't rate: “sure wins” and “correct score”
Football prediction sites often promise “sure wins”, “fixed” or “correct score” tips. No model can make a football result certain, and UK advertising rules say tipsters must not state or imply that success is guaranteed (ASA guidance). We don't list services on the strength of such claims, and we never publish tips ourselves. 6 of the 25 tools on this page make marketing claims that we show on their entry page as stated, not verified.
What counts here is a published level-stakes record with a bet count and a stated price basis. Accuracy percentages alone don't show whether a tool makes money.
How this ranking works
- Ranked by Performance score, then Transparency score, then settled bets.
- A full Performance score needs a published level-stakes record of 200+ settled bets; 50 to 199 is provisional and not ranked.
- Every record is labelled self-reported, recomputed by us or independently proofed; self-reported records count half their bets.
Ranked: tools with a full Performance score
Order: Performance score (high to low), then Transparency score, then settled bets. Full scores need 200+ settled bets at level stakes. Of these 7, 7 are self-reported and 0 independently proofed: check the evidence line under each. Tiers explained.
- 1
OddAlerts Alpha
+2.30% ROI over 14,343 level-stakes bets (22 Apr – 26 Oct 2026), self-reported. Price basis: Odds available when each selection was published.
• Self-reported⚑ Advised odds, may be hard to matchRecord period: 22 Apr – 26 Oct 2026 (whole record)Checked 3 Nov · next re-check 10 Nov53PerformanceProfitable record90TransparencyHigh transparency - 2
OddsIntel
+2.19% ROI over 3,027 level-stakes bets (4 May – 3 Nov 2026), self-reported. Price basis: Best price on any listed bookmaker at or before pick time, €10 flat per pick.
• Self-reported⚑ Advised odds, may be hard to matchRecord period: 4 May – 3 Nov 2026 (all time)Checked 4 Nov · next re-check 11 Nov52PerformanceProfitable record85TransparencyHigh transparency - 3
GoalGrid AI
+2.20% ROI over 289 level-stakes bets (23 Jul – 3 Nov 2026), self-reported. Price basis: Pre-kick-off odds stored with each pick.
• Self-reported⚑ Advised odds, may be hard to matchRecord period: 23 Jul – 3 Nov 2026 (Official Picks)Checked 3 Nov · next re-check 10 Nov51PerformanceProfitable record65TransparencyHigh transparency - 4
Kepler
-1.29% ROI over 1,353 level-stakes bets (Aug – 26 Oct 2026), self-reported. Price basis: Price posted with each pick (also shown at kick-off and close).
• Self-reported⚑ Advised odds, may be hard to matchRecord period: Aug – 26 Oct 2026Checked 27 Oct · re-check overdue49PerformanceLosing record80TransparencyHigh transparency - 5
Footlab AI
-4.10% ROI over 2,049 level-stakes bets (Live log of served prediction cards to 3 Nov 2026), self-reported. Price basis: Average market (bookmaker) odds.
• Self-reportedRecord period: Live log of served prediction cards to 3 Nov 2026 (start date not stated)Checked 3 Nov · next re-check 10 Nov47PerformanceLosing record65TransparencyHigh transparency - 6
Football Analytics (footballanalytics.pro)
-6.40% ROI over 866 level-stakes bets (14 Aug – 4 Nov 2026), self-reported. Price basis: Closing odds (last captured price before kick-off), 1 unit on the model's 1X2 pick.
• Self-reportedRecord period: 14 Aug – 4 Nov 2026Checked 4 Nov · next re-check 11 Nov46PerformanceLosing record75TransparencyHigh transparency - 7
Football Charts
-11.94% ROI over 8,414 level-stakes bets (23 Jul – 3 Nov 2026), self-reported. Price basis: Multi-bookmaker average price frozen about 30 minutes before kick-off.
• Self-reportedRecord period: 23 Jul – 3 Nov 2026 (staked record)Checked 3 Nov · next re-check 10 Nov37PerformanceLosing record90TransparencyHigh transparency
The rest, in brief
- All 7 with a full Performance score are listed above.
- 0 with a small sample (provisional, 50 to 199 settled bets, not ranked).
- 18 don't publish enough for a Performance score yet: see the not enough data yet list.
Every tool, with both scores and the evidence behind them: all 122 AI tool ratings · the scoreboard.
Accuracy and calibration, as stated (6)
Some football tools publish how often they pick the right result, or how well calibrated their probabilities are, sometimes next to the betting market's own figure. We quote these as stated by each tool, unverified. They don't feed the Performance score: accuracy alone can't show whether bets at the available odds make money.
| Tool | Measure | As stated | Market figure (as stated) | Sample | Source |
|---|---|---|---|---|---|
| Footlab AI | 1X2 accuracy (most likely outcome) | 54.7% | 55.4% (bookmakers’ favourite, same matches) | 2,049 matches with odds | Source checked 3 Nov |
| 1X2 accuracy, all served cards | 54.1% | Not published for this set | 4,382 matches | Source checked 3 Nov | |
| Pitchside Lab | Brier score | 0.596 | 0.582 (margin removed, same matches) | 8,894 matches the market priced | Source checked 3 Nov |
| Correct call (same matches) | 51.9% | 52.7% (bookmakers’ favourite, same matches) | 8,894 matches | Source checked 3 Nov | |
| Football Arena | Accuracy, leading model on the full record (Claude Opus 5) | 54% | +0.7 points above the market favourite on the same fixtures | 835 games | Source checked 25 Oct |
| ROI, same model and games (hypothetical) | −0.1% | −5.5% (market favourite) | 835 games | Source checked 25 Oct | |
| AI Betting Tips (aibetting.tips) | ROI, top model (DeepSeek) | +1.0% | Not published | 5,672 settled tips (stake basis not stated) | Source checked 27 Oct |
| AIPredict | Over 1.5 goals hit rate | 82% | Over 1.5 goals | 8,983 of 10,892 tips (last 30 days) | Source checked 27 Oct |
| Double chance hit rate | 74% (67% natural rate shown on the home page) | Double chance | 14,337 of 19,324 tips (last 30 days) | Source checked 27 Oct | |
| SmartMatchPick | Accuracy (picks with 70%+ confidence, last 30 days) | 54% | Not published | 52 settled picks (of 7,026 published) | Source checked 27 Oct |
Brier score: lower is better; it measures how close forecast probabilities were to what happened. A tool beats the market on this measure only if its Brier score is lower than the market's on the same matches.
Multi-sport tools that also cover football (13)
Not ranked here: their published record covers several sports, so it doesn't tell you how they do on football alone.
| Tool | Covers | Performance |
|---|---|---|
| Sharper Points | NFL, NBA, NCAAF, NHL, soccer | 53 Profitable record |
| Prophet | 30+ sports (mostly football; also basketball, ice hockey, darts, baseball, NFL, tennis, racing) | 50 Break-even record |
| Olympus Bets Analytics | MLB, golf, college basketball, NBA, tennis, NHL, soccer, CS2, LoL and more | 49 Losing record |
| Kickscore AI | Football, esports (Dota 2, CS2) | 49 Losing record |
| Botcappers | US sports and tennis: NCAAF, NFL, UFC/MMA, MLB, ATP and WTA | 50 Provisional (small sample) |
| AI Sports Metrics | MLB, NBA, NFL, CFB, NHL, golf, NASCAR, tennis, soccer, MMA | Not enough data yet |
| Bet | Football, basketball, MMA, hockey, NFL, tennis, cricket | Not enough data yet |
| Model | Football, tennis, NFL, MLB, MMA, boxing | Not enough data yet |
| Prediction | Multi-sport (NBA, NFL, MLB, NHL, soccer) | Not enough data yet |
| Predixsport | NBA, football, tennis | Not enough data yet |
| Props | Multi-sport (US leagues, soccer, tennis, esports) | Not enough data yet |
| Score | International (football), USA (NFL, college football) | Not enough data yet |
| Turing | International | Not enough data yet |
How we check the records
The transparency checklist sets out what counts as evidence for each Transparency criterion, the same for every listing. The AI tipping transparency report 2026 shows how many of the AI tools and tipsters we rate meet each criterion.