Heutige Marktwahrscheinlichkeiten

Alle heute von unseren KI-Modellen analysierten Spiele, mit berechneten Wahrscheinlichkeiten pro Markt. Das sind die Zahlen, die unsere KI nutzt, um die besten Tipps auszuwählen.

CategoryMarketMin %Min oddsMax odds

Match	Score ⓘ	Home%	Draw%	Away%	O1.5	U3.5	BTTS
Tip: click a match row to see all betting markets. Markets are sorted by model value (positive value = model rates pick higher than bookmaker odds suggest). Markets without odds are shown last for reference.
Oleksandria vs Livyi Bereh ▼ Ukraine — Premier League	56 2/3	14% @2.11 -33.5%	20% @3.08 -12.4%	65% @3.18 +33.6%	80% @1.37 +6.7%	65% @1.22 -17.3%	52% @1.84 -2.3%
VALUE PICK Away Win 65% @ 3.18 +33.6% value VALUE PICK DC X2 85% @ 1.57 +21.3% value VALUE PICK Over 2.5 57% @ 2.13 +10.1% value VALUE PICK Over 1.5 80% @ 1.37 +6.7% value VALUE PICK Livyi Bereh +0.25 55% @ 2.02 +5.5% value SAFE BET Livyi Bereh +1.5 82% @ 1.23 +0.7% value SAFE BET Livyi Bereh +0.5 61% @ 1.65 +0.4% value SAFE BET Livyi Bereh +0.75 67% @ 1.50 +0.3% value LONGSHOT Over 3.5 35% @ 3.83 +8.9% value LONGSHOT Livyi Bereh -0.25 42% @ 2.75 +5.6% value LONGSHOT Livyi Bereh -0.75 31% @ 3.90 +5.4% value Livyi Bereh -1.25 21% @ 6.00 +4.3% value Livyi Bereh -0.5 36% @ 3.10 +3.7% value Livyi Bereh -1.5 16% @ 6.60 +0.8% value AVOID Livyi Bereh +1.75 85% @ 1.16 -1.2% value AVOID Livyi Bereh +1.25 77% @ 1.27 -1.7% value AVOID BTTS Yes 52% @ 1.84 -2.3% value AVOID Oleksandria -1.75 15% @ 5.25 -4% value AVOID Oleksandria -1.25 23% @ 3.55 -5.2% value AVOID Oleksandria +1.5 84% @ 1.11 -6.1% value AVOID Oleksandria -0.5 39% @ 2.20 -6.5% value AVOID Oleksandria -1.5 18% @ 4.00 -7% value AVOID Oleksandria -0.75 33% @ 2.50 -7% value AVOID Oleksandria +0.5 64% @ 1.35 -10.1% value AVOID Oleksandria +1.25 79% @ 1.12 -10.3% value AVOID Oleksandria -0.25 45% @ 1.78 -11.2% value AVOID Oleksandria +0.75 69% @ 1.24 -11.6% value AVOID Oleksandria +0.25 58% @ 1.42 -12.4% value AVOID Draw 20% @ 3.08 -12.4% value AVOID Home Win 14% @ 2.11 -33.5% value AVOID DC 1X 34% @ 1.27 -44.6% value Livyi Bereh -1.75 13% odds n/a Oleksandria +1.75 87% odds n/a

How to read the cards: Each card combines model probability (chance the pick wins) with value (edge vs bookmaker odds). VALUE PICK high probability (≥50%) with positive edge (≥+5%) - the best combination. LONGSHOT lower probability (<50%) but positive edge - higher risk, higher payout. SAFE BET high probability, modest edge (0-5%). AVOID bookmaker prices the outcome better than our model - negative expected value.

Important note: These categories reflect our model’s view versus bookmaker odds — they are analytical, not betting advice. Our historical backtest (Apr–May 2026, n=401 VALUE PICKs) shows that no category has consistently outperformed the bookmaker margin. Bookmakers price markets efficiently; use these signals to inform your thinking, not as guaranteed picks. Bet responsibly.

KI-Modell-Statistiken

Unser Machine-Learning-Modell analysiert jedes Fußballspiel, das es findet — nicht nur die 3 Tipps, die wir täglich veröffentlichen. Unten findest du die vollständigen Leistungsdaten der KI über Tausende von Spielen. So lernt und verbessert sich das Modell mit der Zeit.

14,853Matches Analysed

14,830Settled

44.2%Model Accuracy

6,557Correct Predictions

Outcome Distribution

Home Win

45% (6667)

Draw

25% (3675)

Away Win

30% (4488)

Over/Under 1.5 Distribution

Over 1.5

73% (15752)

Under 1.5

27% (5915)

O/U 1.5 Model Accuracy: 70.1% (15185/21667 correct)

Over/Under 2.5 Distribution

Over 2.5

55% (6210)

Under 2.5

45% (5090)

O/U Model Accuracy: 53.9% (6092/11300 correct)

Over/Under 3.5 Distribution

Over 3.5

31% (6800)

Under 3.5

69% (14867)

O/U 3.5 Model Accuracy: 67.1% (14535/21667 correct)

Both Teams to Score (BTTS)

BTTS Yes

50% (10848)

BTTS No

50% (10819)

BTTS Model Accuracy: 53% (11485/21667 correct)

Asian Handicap Results

Home Covered

133% (14140)

Away Covered

156% (16586)

Push

10% (1014)

AH Model Accuracy: 59.3% (5796/9781 correct, 878 push)

Accuracy by Competition

Competition	Matches	Correct	Accuracy
Regionalliga - Ost	92	71	77.2%
UEFA Champions League	741	505	68.2%
UEFA Europa Conference League	1147	772	67.3%
UEFA Europa League	682	441	64.7%
NPFL	69	43	62.3%
Primeira Liga	924	575	62.2%
Eredivisie	948	579	61.1%
3. Division	91	54	59.3%
Third League - Southeast	102	60	58.8%
Toppserien	50	29	58%
Serie A	1241	714	57.5%
La Liga	1140	651	57.1%
1st League - RS	62	35	56.5%
Bundesliga	973	546	56.1%
Third NL - Jug	83	46	55.4%
Serie D - Girone H	57	31	54.4%
Third League - Northeast	65	35	53.8%
Meistriliiga	52	28	53.8%
Division 2 - Södra Svealand	69	37	53.6%
Srpska Liga - Vojvodina	84	45	53.6%

Recent AI Predictions

Date	Match	1X2	AIC	O/U 1.5	O/U 2.5	O/U 3.5	BTTS	AH Line	Actual	Result
31 May 2026	Segunda DivisiónLeganes vs Mirandes	Away Win	57%	—	Over 2.5 ❌	—	—	-0.3 (Home) ✅	Home Win (1-0)	❌
31 May 2026	Segunda DivisiónCordoba vs Huesca	Home Win	57%	—	Over 2.5 ❌	—	—	-0.5 (Home) ❌	Draw (1-1)	❌
31 May 2026	Segunda DivisiónBurgos vs FC Andorra	Away Win	52%	—	Under 2.5 ✅	—	—	+0.0 (Away)	Home Win (1-0)	❌
31 May 2026	Segunda DivisiónDeportivo La Coruna vs Las Palmas	Home Win	66%	—	Over 2.5 ✅	—	—	-0.5 (Home) ❌	Away Win (1-2)	❌
31 May 2026	Segunda DivisiónCastellón vs Eibar	Away Win	47%	—	Over 2.5 ✅	—	—	-0.3 (Home) ✅	Home Win (2-1)	❌
31 May 2026	Segunda DivisiónAlmeria vs Valladolid	Home Win	60%	—	Under 2.5 ✅	—	—	-0.5 (Home) ✅	Home Win (1-0)	✅
31 May 2026	Segunda DivisiónRacing Santander vs Cadiz	Home Win	78%	—	Over 2.5 ✅	—	—	-0.5 (Home) ✅	Home Win (4-1)	✅
31 May 2026	Segunda DivisiónZaragoza vs Malaga	Away Win	71%	—	Under 2.5 ✅	—	—	-0.3 (Home) ❌	Away Win (0-2)	✅
30 May 2026	Segunda DivisiónGranada CF vs Sporting Gijon	Away Win	47%	—	Over 2.5 ✅	—	—	-0.3 (Home) ❌	Away Win (1-2)	✅
30 May 2026	Segunda DivisiónAD Ceuta FC vs Albacete	Away Win	52%	Over 1.5 ❌	Over 2.5 ❌	Under 3.5 ✅	BTTS Yes ❌	-0.3 (Home) ✅	Home Win (1-0)	❌
29 May 2026	Ligue 1Nice vs Saint Etienne	Home Win	47%	Over 1.5 ✅	Under 2.5 ❌	Under 3.5 ❌	BTTS Yes ✅	-0.5 (Home) ✅	Home Win (4-1)	✅
29 May 2026	Serie BMonza vs Catanzaro	Home Win	49%	Over 1.5 ✅	Over 2.5 ❌	Over 3.5 ❌	BTTS Yes ❌	-0.5 (Home) ❌	Away Win (0-2)	❌
28 May 2026	Primeira LigaCasa Pia vs Torreense	Away Win	49%	Over 1.5 ✅	Under 2.5 ✅	Under 3.5 ✅	BTTS Yes ❌	-0.3 (Home) ✅	Home Win (2-0)	❌
27 May 2026	UEFA Europa Conference LeagueCrystal Palace vs Rayo Vallecano	Home Win	43%	Over 1.5 ❌	Over 2.5 ❌	Under 3.5 ✅	BTTS No ✅	-0.3 (Home) ✅	Home Win (1-0)	✅
26 May 2026	Ligue 1Saint Etienne vs Nice	Away Win	43%	Over 1.5 ❌	Under 2.5 ✅	Under 3.5 ✅	BTTS Yes ❌	-0.3 (Home) ❌	Draw (0-0)	❌

Ensemble Model

40.9%Ensemble Accuracy

28,116Settled Matches

Our v4 GradientBoosting model — the AI behind every tip we publish — currently scores 47.5% accuracy on 912 settled matches since deployment. This is in line with the inherent difficulty of 3-way football outcomes (random = 33.3%). Accuracy will stabilise as more data accumulates.

34.2%When All Models Agree

9,656All Models Agreed

Ensemble Score Distribution & Accuracy

Score RangeMatchesAccuracy

0-92341

32.2%

10-1910261

40%

20-295725

40.1%

30-393918

39.4%

40-492902

44.2%

50-591369

49.3%

60-69962

47.7%

70-79437

59%

80-89152

63.8%

90-9939

66.7%

100-10910

90%

Modell-Kalibrierung

Bedeutet eine höhere Konfidenzbewertung tatsächlich eine genauere Vorhersage? Diese Grafik beantwortet das. Wir gruppieren jede abgeschlossene Vorhersage nach der internen Konfidenzbewertung, die das Modell zugewiesen hat, und zeigen dann die echte Trefferquote für jede Gruppe. Eine ansteigende Treppe ist das, was du sehen willst — sie bedeutet, dass das Modell weiß, wann es eher richtig liegt.

Each bar groups model predictions by internal confidence score (0–100). The height shows how often those predictions were actually correct on settled matches. A rising pattern means the model’s confidence is meaningful: higher scores genuinely correspond to more accurate predictions. Data covers our core leagues where the model has proven signal. Hover for sample sizes. This is the model’s internal ranking score across every match it analyses — not the per-tip AI Confidence shown on the tips and homepage.

Die Modellgenauigkeit wird nur auf abgeschlossenen Spielen berechnet. Das aktuelle Modell nutzt Form, Tordurchschnitt und H2H-Daten als Eingabemerkmale. Die Genauigkeit verbessert sich, je mehr Live-Daten gesammelt werden.