Hannover 96 vs Preußen Münster 予想・オッズ・AIベッティングヒント

Hannover 96

May 02, 2026 - 11:30

1.77

0.86

57% 26% 17%

Preußen Münster

メイン推奨（最高+EV）

アンダー 2.5 — 価値

EV 47.2% モデル 51.1%

二次 (バランスのとれた価値): BTTS いいえ (EV 10.5%) — 50.7% モデル

プライマリよりも EV が低くなりますが、モデル確率が高くなります (表示されている場合はより「安定」しています)。

両チーム得点最良の価値（+EV）

はい 49.3% · いいえ 50.7%

EV はい -17.67% · EV番号 10.53%

価値の読み: BTTS いいえ

1X2 価値が低い

Hannover 96 · モデル 57.5%

暗黙の 68.7%

EV: -15.4%

1X2の最良EV -1.2%

コレクトスコアの要点穴 / エンジョイ

最も可能性が高い

1-0

確率 12.8%

最もお得な選び

0-0 @26.0 (+87% EV)

Marathonbet

スコアは分散が大きいため、娯楽としての極小額のみを想定しています。

賭けの決定 (モデルと市場の EV)

価値ある機会 — 少なくとも 1 つの市場が、現在の最良の小数オッズで推定 +EV を示しています (しきい値: 2.0%)。

判断の強さ: 7.5 / 10

プライマリラインの識別 (+1.0)
プライマリー EV が 10% 以上 (+1.0)
しきい値 (+0.5) で 2 つ以上の有効な +EV ライン

O/U 2.5: EVオーバー -27.63% · EVアンダー 47.17% (8 本のペア)
防弾少年団: EV はい -17.67% · EV番号 10.53%

Should you bet on this match? Only where +EV is shown; always compare with your own limits.

これの使い方

実用的なアイデアが 1 つ必要な場合は、Primary 行に焦点を当てます。
多数の薄いエッジのピックを一緒に使用しないでください。エッジが確実に追加されません。
ロングショットはオプションの、一か八かのプレイのみとして扱います。

OddsGPT 意思決定エンジン v2.0.0(EV + 市場構造 + リスク調整) オッズを確認する 1xBet

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

League: 2. Bundesliga
Fixture: Hannover 96 vs Preußen Münster
Kickoff: 2026-05-02 11:30:00
1X2 (model): Home 57.5% · Draw 26.0% · Away 16.5%
xG (showing): Hannover 96 1.77 — Preußen Münster 0.86 (total xG ≈ 2.63)
Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
Model: 51.1% · Implied: 34.4% · Probability edge: +16.7 pts · Est. EV: +47.2%
BTTS (model): Yes 49.3% · No 50.7%
Correct score (top bin): 1-0 (12.8%)

Where EV is shown, it is estimated return per unit stake at the best tracked decimal price — not the same thing as a raw probability gap.

Correct score remains high-variance even when a line is most likely on paper.

Best Bet + Reason

Primary pick from the decision engine: Under 2.5 goals.

If 1X2 looks tight, the engine may still find clearer structure in totals or BTTS — that is intentional.

Edges shrink quickly if prices move; always re-check the number on your book.

FAQ

What changes first if odds move?

Implied probabilities and EV move immediately with price; model probabilities in this snapshot do not update until the pipeline is re-run. Refresh after material line moves.

What is the best-supported line in this snapshot?

Match the hero card above: if it says “Betting Primary Pick”, that leg cleared primary rules; if it says “Best +EV (tracked markets)”, it is the strongest +EV line that did not meet stricter Primary thresholds. The bullets below repeat the same model %, implied %, edge (pts), and EV % as that card.

How should I read EV versus a probability gap?

Probability edge = model probability minus implied probability (reported here in percentage points). EV ≈ model probability × best tracked decimal odds − 1, shown as return per unit stake. They are related but not interchangeable labels.

Safer market than correct score?

Markets with more liquidity and smoother prices (often 1X2 or O/U 2.5 from many books) are usually easier to reason about than long-tail correct-score prices; still read EV on each leg.

Risk Factors

Price movement: implied probabilities and EV move with odds.
Sample / data gaps: low-information leagues widen forecast bands.
In-play state: goals and red cards are not modelled here.
Scoreline variance: the most likely scoreline is still usually a low absolute probability outcome (often well below 20%).

Methodology

Inputs: Same structured facts bundle as the public prediction page (xG / Poisson snapshot, market EV where available, decision engine v2).
Narrative: Template sentence library with fixture-stable selection (no per-request LLM for this block).
Compliance: Educational framing only; not personalised advice.

Last Updated

May 01, 2026 (UTC)

その他の予想コンテンツを探す

プレミアム予想を取得： Hannover 96 & Preußen Münster!

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2. Bundesliga — 順位表

#	チーム	試	勝	分	敗	点
1	FC Schalke 04	30	18	7	5	61
2	SC Paderborn 07	30	17	7	6	58
3	Hannover 96	31	16	9	6	57
4	SV Elversberg	31	16	8	7	56
5	SV Darmstadt 98	31	13	12	6	51
6	Hertha BSC	31	13	9	9	48
7	1. FC Kaiserslautern	31	14	4	13	46
8	Karlsruher SC	31	11	7	13	40
9	1. FC Nürnberg	30	10	8	12	38
10	Holstein Kiel	31	10	8	13	38
11	VfL Bochum	30	9	9	12	36
12	Dynamo Dresden	31	9	8	14	35
13	Arminia Bielefeld	31	9	8	14	35
14	Eintracht Braunschweig	31	9	7	15	34
15	Fortuna Düsseldorf	31	10	4	17	34
16	1. FC Magdeburg	30	10	3	17	33
17	SpVgg Greuther Fürth	30	9	6	15	33
18	Preußen Münster	31	6	10	15	28

#	チーム	試	得	失	+/-	点
1	SV Elversberg	31	55	35	+20	56
2	SV Darmstadt 98	31	55	40	+15	51
3	Hannover 96	31	53	37	+16	57
4	SC Paderborn 07	30	52	35	+17	58
5	Dynamo Dresden	31	50	50	0	35
6	1. FC Kaiserslautern	31	49	46	+3	46
7	1. FC Magdeburg	30	48	55	-7	33
8	Karlsruher SC	31	48	59	-11	40
9	Arminia Bielefeld	31	46	47	-1	35
10	FC Schalke 04	30	45	26	+19	61
11	Hertha BSC	31	44	36	+8	48
12	VfL Bochum	30	43	43	0	36
13	SpVgg Greuther Fürth	30	43	63	-20	33
14	Holstein Kiel	31	40	43	-3	38
15	1. FC Nürnberg	30	39	41	-2	38
16	Eintracht Braunschweig	31	34	50	-16	34
17	Preußen Münster	31	34	54	-20	28
18	Fortuna Düsseldorf	31	30	48	-18	34

#	チーム	試	xG	xGC	+/-	点
1	SC Paderborn 07	30	53.0	32.7	+20.3	58
2	FC Schalke 04	30	46.4	27.8	+18.6	61
3	Hannover 96	31	49.4	34.2	+15.2	57
4	SV Elversberg	31	46.1	35.3	+10.8	56
5	1. FC Magdeburg	30	48.9	42.7	+6.2	33
6	VfL Bochum	30	48.9	43.7	+5.2	36
7	1. FC Kaiserslautern	31	43.3	40.0	+3.3	46
8	1. FC Nürnberg	30	41.4	38.1	+3.3	38
9	SV Darmstadt 98	31	47.0	46.1	+0.9	51
10	Arminia Bielefeld	31	42.7	42.9	-0.2	35
11	Hertha BSC	31	41.5	43.5	-2.0	48
12	Dynamo Dresden	31	38.7	41.9	-3.2	35
13	Fortuna Düsseldorf	31	38.4	45.3	-6.9	34
14	Eintracht Braunschweig	31	35.1	43.0	-7.9	34
15	Holstein Kiel	31	37.7	49.8	-12.1	38
16	SpVgg Greuther Fürth	30	33.6	48.1	-14.5	33
17	Preußen Münster	31	34.0	49.5	-15.5	28
18	Karlsruher SC	31	35.7	57.1	-21.4	40