· Europa Conference League
Trabzonspor
ATT
7/10
DEF
5/10
vs
Hearts
5/10
ATT
4/10
DEF
Scheduled· Thu 22 Oct · 16:45

AI Match Analysis

Trabzonspor’s +31% attacking edge and third-place standing point to sustained pressure, while Hearts’ defence is 24% weaker than average—a clear opening for goals. Hearts’ attack is below par, but that may leave them chasing the game as Trabzonspor carry momentum in this Conference League clash. Over 2.5 looks the logical call.

Match GoalsOver 2.5
Best Pick
Home Win
61%
Correct Score
Top
1-0
10.9%
10.00
Top
2-0
10.7%
8.00
1-1
10.1%
11.00
2-1
9.9%
9.00
3-0
7%
9.00
3-1
6.5%
10.00
0-0
5.5%
21.00
0-1
5.1%
29.00
161%
1.25TOP
X21%
6.25
217%
8.50
1X82%
X238%
1278%

Combined Bet Builder

3 legs

Trabzonspor’s 1.55 xG and +31% attack power point to a strong home performance against a Hearts defence rated 24% weaker than average. With 2.89 combined xG, Over 2.5 looks well supported, while Ali Habesoglu is a logical anytime-scorer pick as Trabzonspor press their advantage.

1Match WinnerTrabzonspor
2GoalscorersAnytime
3Match GoalsOver 2.5
Confirmed XI4-4-2
24
Onana
20
Pina
27
Nwaiwu
15
Savic
55
Cabral
6
Fabinho
11
Tufan
10
Salah
8
Bouchouari
58
Şimşir
9
Franculino
Impact Player Hot Head
Trabzonspor43 players
50.1/ 99Average
Top Rated
1Cenk ÖzkacarDEF74
2Arseniy BatagovDEF73
3Benjamin Bouchouari MID71
Hearts29 players
48.0/ 99Average
Top Rated
1Oisin McEnteeDEF76
2Cláudio BragaFWD66
3Laurent MendyMID65
Player Rankings
Snipers
Top goal threats
1
Noah Jose SavioloTrabzonspor
65
2
Paul OnuachuTrabzonspor
62
3
FranculinoTrabzonspor
60
4
Mohamed SalahTrabzonspor
60
5
Felipe AugustoTrabzonspor
59
Wall
Top defenders
1
Craig HalkettHearts
84
2
Cenk ÖzkacarTrabzonspor
80
3
Samet AkaydinTrabzonspor
79
4
Jamie McCart Hearts
68
5
Chibuike NwaiwuTrabzonspor
67
Control
Top playmakers
1
Salih MalkoçoğluTrabzonspor
71
2
Blair SpittalHearts
70
3
Christ Inao Oulaï Trabzonspor
69
4
Benjamin Bouchouari Trabzonspor
63
5
Yan Dhanda Hearts
63
Bad Boys
Most likely to get booked
1
Stefan SavicTrabzonspor
1
2
Ernest MuciTrabzonspor
1
3
Umut NayirTrabzonspor
1
4
Blair SpittalHearts
1
5
Yan Dhanda Hearts
1
Power Rankings
Strongest overall
1
Oisin McEnteeHearts
76
2
Cenk ÖzkacarTrabzonspor
74
3
Arseniy BatagovTrabzonspor
73
4
Benjamin Bouchouari Trabzonspor
71
5
Felipe AugustoTrabzonspor
69

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Trabzonspor vs Hearts 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 Trabzonspor vs Hearts 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 Trabzonspor 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 Trabzonspor vs Hearts 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 Trabzonspor 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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