· 2. Bundesliga
Darmstadt 98
ATT
6/10
DEF
5/10
vs
St. Pauli
5/10
ATT
5/10
DEF
Programmata· Fri 23 Oct · 16:30

Analisi IA della partita

Darmstadt’s solid attacking edge should set an assertive tone, while St Pauli’s forward threat adds danger at both ends. With each side carrying credible scoring intent, an open contest appears likely to clear the threshold.

Match GoalsOver 2.5
Migliore scelta
GOL DI ENTRAMBE
58%
Risultato esatto
Eccellente
1-1
10.9%
7.00
Eccellente
2-1
9.7%
11.00
1-0
9.1%
11.00
2-0
8.1%
17.00
1-2
6.5%
9.00
0-1
6.1%
9.50
2-2
5.8%
13.00
3-1
5.7%
23.00
151%
2.90TOP
X23%
3.60
226%
2.10
1X74%
X249%
1277%

Combinata

5 selezioni

Darmstadt’s 1.59 xG and St Pauli’s 1.13 support both teams to score and an over-2.5 contest; combined first-half xG of 1.59 points to an early goal. Their 10.18 projected corners clear 8.5, while Martijn Kaars is the anytime scorer pick as the game opens up.

1Match GoalsOver 2.5
2Both Teams to ScoreYes
3First Half Over/UnderOver 0.5
4Corners Over/UnderOver 8.5
5GoalscorersAnytime
Confirmed XI3-5-2
1
Schuhen
6
Pfeiffer
20
Vukotic
18
Petretta
25
Mikelbrencis
41
Duijvestijn
17
Klefisch
16
Akiyama
5
Richter
9
Lee
36
Smit
Impact Player Hot Head
Darmstadt 9833 giocatori
49.7/ 99Medio
Migliori
1Yosuke FurukawaFWD84
2Matej MaglicaDEF78
3Lance DuijvestijnMID66
St. Pauli24 giocatori
53.6/ 99Medio
Migliori
1Branimir Hrgota MID76
2Marcus MathisenDEF72
3Abdoulie CeesayFWD70
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Yosuke FurukawaDarmstadt 98
85
2
Ricky-Jade Jones St. Pauli
74
3
Abdoulie CeesaySt. Pauli
67
4
Martijn KaarsSt. Pauli
63
5
Isac LidbergDarmstadt 98
57
Muro
Migliori difensori
1
Aleksandar VukoticDarmstadt 98
84
2
Matej MaglicaDarmstadt 98
78
3
Patric PfeifferDarmstadt 98
76
4
Yannik LührsDarmstadt 98
76
5
Marcus MathisenSt. Pauli
67
Controllo
Migliori registi
1
Mathias RasmussenSt. Pauli
67
2
Erik AhlstrandSt. Pauli
65
3
Joel Chima FujitaSt. Pauli
65
4
Marco RichterDarmstadt 98
62
5
Lance DuijvestijnDarmstadt 98
59
Cattivi ragazzi
Più propensi ai cartellini
1
Aleksandar VukoticDarmstadt 98
1
2
Merveille PapelaDarmstadt 98
1
3
Ricky-Jade Jones St. Pauli
10
4
T. AndoSt. Pauli
15
5
Milan SmitDarmstadt 98
22
Classifica potenza
Più forti in assoluto
1
Yosuke FurukawaDarmstadt 98
84
2
Matej MaglicaDarmstadt 98
78
3
Branimir Hrgota St. Pauli
76
4
Marcus MathisenSt. Pauli
72
5
Abdoulie CeesaySt. Pauli
70

Domande frequenti

Our Poisson model calculates the probability of every possible scoreline for Darmstadt 98 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 Darmstadt 98 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 Darmstadt 98 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 Darmstadt 98 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 Darmstadt 98 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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