· 2. Bundesliga
Magdeburg
ATT
6/10
DEF
5/10
vs
St. Pauli
5/10
ATT
6/10
DEF
Programmata· Sun 8 Nov · 12:30

Analisi IA della partita

Magdeburg’s solid attacking threat and leaky defensive profile set the stage for an open contest, while St Pauli’s pressure should create chances despite their modest scoring strength. With both sides generating attacking momentum, the match appears likely to clear the goals line.

Match GoalsOver 2.5
Migliore scelta
GOL DI ENTRAMBE
57%
Risultato esatto
Eccellente
1-1
11.1%
7.00
Eccellente
2-1
9.6%
9.50
1-0
9.2%
11.00
2-0
8%
13.00
1-2
6.7%
11.00
0-1
6.5%
12.00
2-2
5.8%
12.00
3-1
5.5%
17.00
149%
2.30TOP
X24%
3.50
227%
2.80
1X73%
X251%
1276%

Combinata

4 selezioni

Magdeburg’s 1.69 xG against St Pauli’s 1.09 points to a home side capable of taking control, while its stronger attack and leaky defence make goals at both ends plausible. Combined xCorners of 9.47 supports Over 8.5, and Zukowski is a credible anytime threat as Magdeburg or Draw anchors the builder.

1Double ChanceMagdeburg or Draw
2Match GoalsOver 2.5
3GoalscorersAnytime
4Corners Over/UnderOver 8.5
Confirmed XI3-5-2
1
Reimann
5
Müller
3
MacNulty
17
Nollenberger
28
Nadjombe
38
Hyryläinen
21
Michel
26
Müsel
8
Iyoha
22
Żukowski
23
Atik
Impact Player Hot Head
Magdeburg25 giocatori
49.2/ 99Medio
Migliori
1Richmond TachieFWD77
2Paul JaeckelDEF76
3Eldin DzogovicDEF70
St. Pauli24 giocatori
53.8/ 99Medio
Migliori
1Samuel KleinMID76
2Marcus MathisenDEF75
3Branimir Hrgota MID75
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Richmond TachieMagdeburg
97
2
Mateusz ŻukowskiMagdeburg
87
3
Ricky-Jade Jones St. Pauli
74
4
Abdoulie CeesaySt. Pauli
73
5
Alexander Ahl-HolmströmMagdeburg
64
Muro
Migliori difensori
1
Eldin DzogovicMagdeburg
99
2
Paul JaeckelMagdeburg
65
3
Marcus MathisenSt. Pauli
65
4
Anselmo García MacNultyMagdeburg
63
5
Hauke WahlSt. Pauli
63
Controllo
Migliori registi
1
Felipe ChavezMagdeburg
73
2
Joel Chima FujitaSt. Pauli
71
3
Mathias RasmussenSt. Pauli
69
4
Erik AhlstrandSt. Pauli
64
5
Samuel KleinSt. Pauli
62
Cattivi ragazzi
Più propensi ai cartellini
1
Anselmo García MacNultyMagdeburg
1
2
Baris AtikMagdeburg
1
3
Mateusz ŻukowskiMagdeburg
1
4
Ricky-Jade Jones St. Pauli
9
5
Pierre NadjombeMagdeburg
10
Classifica potenza
Più forti in assoluto
1
Richmond TachieMagdeburg
77
2
Paul JaeckelMagdeburg
76
3
Samuel KleinSt. Pauli
76
4
Marcus MathisenSt. Pauli
75
5
Branimir Hrgota St. Pauli
75

Domande frequenti

Our Poisson model calculates the probability of every possible scoreline for Magdeburg vs St. Pauli based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.

The Bet Builder combines multiple selections from the Magdeburg vs St. Pauli match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.

xG (expected goals) measures the quality of scoring chances. If Magdeburg has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).

Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Magdeburg vs St. Pauli without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.

A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Magdeburg a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.

AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.

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