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Hannover
ATT
6/10
DEF
6/10
6:1
TSV Havelse
5/10
ATT
4/10
DEF
Full-time
Best Pick
Over 2.5
62%
Correct Score90′
Top
2-1
9.8%
10.00
Top
1-1
9.8%
15.00
2-0
8.1%
10.00
1-0
8.1%
15.00
3-1
6.5%
10.00
1-2
6%
26.00
2-2
5.9%
21.00
3-0
5.3%
9.50
155%
1.81TOP
X22%
4.61
223%
4.35
1X77%
X245%
1278%
xG — Expected Goals
Hannover
Hannover exp
TSV Havelse
TSV Havelse exp
goal
per 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Hannover
TSV Havelse
Probable XI4-4-2
40
Schwanke
17
Allgeier
3
Tomiak
37
Ezeh
4
Blank
Vogel
32
Ikari
8
Leopold
13
Roggow
11
Pichler
9
Källman
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Hannover13 players
46.7/ 99Average
Top Rated
1Benedikt PichlerFWD58
2Tom HobrechtFWD53
3Leo WeinkaufGK46
TSV Havelse54 players
No squad metrics available
35
Alexander DlugaiczykGK
25
Fynn WolterGK
Jannis KrugGK
40
Jonah BusseGK
29
Norman QuindtGK
Omer HaninGK
1
Tom OpitzGK
4
Besfort KolgeciDEF
15
Dennis DuahDEF
5
Dominic MinzDEF
Donald NdukaDEF
7
Emre AytunDEF
Florian RiedelDEF
Hannover46
PlayerOverall RatingImpactAggressionDiscipline
11
Benedikt Pichler58624550
25
Tom Hobrecht53564550
30
Leo Weinkauf46454550
Pascal Loretz46454550
13
Franz Roggow46454550
27
Hayate Matsuda46454550
22
Noah Engelbreth46454550
20
Ime Okon45454550
35
Joshua Caspar Siewert45454550
6
Maik Nawrocki45454550
7
Taycan Etcibasi45454550
24
Yunus Ünal45454550
8
Enzo Leopold 41393950
TSV Havelse54
PlayerOverall RatingImpactAggressionDiscipline
35
Alexander Dlugaiczyk————
25
Fynn Wolter————
Jannis Krug————
40
Jonah Busse————
29
Norman Quindt————
Omer Hanin————
1
Tom Opitz————
4
Besfort Kolgeci————
15
Dennis Duah————
5
Dominic Minz————
Donald Nduka————
7
Emre Aytun————
Florian Riedel————
Player Rankings
Snipers
Top goal threats
1
Benedikt PichlerHannover
62
2
Tom HobrechtHannover
56
3
Taycan EtcibasiHannover
45
4
Yunus ÜnalHannover
45
Wall
Top defenders
1
Ime OkonHannover
45
2
Joshua Caspar SiewertHannover
45
3
Maik NawrockiHannover
45
Control
Top playmakers
1
Franz RoggowHannover
45
2
Hayate MatsudaHannover
45
3
Noah EngelbrethHannover
45
4
Enzo Leopold Hannover
39
Bad Boys
Most likely to get booked
1
Leo WeinkaufHannover
50
2
Pascal LoretzHannover
50
3
Ime OkonHannover
50
4
Joshua Caspar SiewertHannover
50
5
Maik NawrockiHannover
50
Power Rankings
Strongest overall
1
Benedikt PichlerHannover
58
2
Tom HobrechtHannover
53
3
Leo WeinkaufHannover
46
4
Pascal LoretzHannover
46
5
Franz RoggowHannover
46

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Hannover vs TSV Havelse 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 Hannover vs TSV Havelse 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 Hannover 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 Hannover vs TSV Havelse 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 Hannover 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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