· Superliga
Tirana
ATT
4/10
DEF
5/10
vs
Dinamo City
5/10
ATT
6/10
DEF
Scheduled· Thu 29 Oct · 00:00
Best Pick
Under 2.5
59%
Correct Score
Top
1-1
13.2%
7.58
Top
1-0
11.8%
8.50
0-1
11%
9.11
0-0
9.8%
10.23
2-1
7.9%
12.60
1-2
7.4%
13.50
2-0
7.1%
14.13
0-2
6.2%
16.25
138%
2.64TOP
X28%
3.55
234%
2.95
1X66%
X262%
1272%
Probable XI4-4-2
Hasani
Nchama
Ilievski
Colley
Vitija
Krymi
Karabeci
Meta
Rodriguez
Bami
Berisha
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Tirana23 players
49.7/ 99Average
Top Rated
1R. XhixhaFWD87
2Luciano VeraDEF75
3Bojan IlievskiDEF68
Dinamo City30 players
54.5/ 99Average
Top Rated
1Fabjan PërndrecaDEF86
2Ibrahim MustaphaFWD82
3Klevi QefalijaMID77
Tirana29
PlayerOverall RatingImpactAggressionDiscipline
R. Xhixha87864691
Luciano Vera75576191
Bojan Ilievski68456791
Basilio Ndong Owono Nchama58384391
Serxho Vogli58374291
Donat Mehmeti58505078
Tomislav Kis56622556
Freddy Antonio Álvarez Rodriguez55641372
Mateo Maci55475661
Bernard Berisha55385591
Joseph Colley53593161
Visar Bekaj5049950
Serge Tabekou50317191
Dinamo City36
PlayerOverall RatingImpactAggressionDiscipline
Fabjan Përndreca86815091
Ibrahim Mustapha82775991
Klevi Qefalija77694691
Florenc Ferruku76686891
Lorenco Vila74576691
Saliou Sembene72565091
Eridon Qardaku69475791
Bekim Maliqi68485091
Mustapha Koma67558391
H. Berisha65537391
Flamur Ruci57268791
Ruben Silva-Richards56523778
Bakary Goudiaby55217491
Player Rankings
Snipers
Top goal threats
1
R. XhixhaTirana
86
2
Ibrahim MustaphaDinamo City
77
3
Florenc FerrukuDinamo City
68
4
Tomislav KisTirana
62
5
Mustapha KomaDinamo City
55
Wall
Top defenders
1
Fabjan PërndrecaDinamo City
81
2
Joseph ColleyTirana
59
3
Luciano VeraTirana
57
4
Patrick StanićTirana
56
5
Saliou SembeneDinamo City
56
Control
Top playmakers
1
Malomo Taofeek AyodejiDinamo City
70
2
Klevi QefalijaDinamo City
69
3
Freddy Antonio Álvarez RodriguezTirana
64
4
Lorenco VilaDinamo City
57
5
Skender MetaDinamo City
53
Bad Boys
Most likely to get booked
1
Leonat VitijaTirana
1
2
Ardit KrymiTirana
1
3
Patrik MyslovicTirana
1
4
Naser AlijiDinamo City
1
5
Ysni IsmailiDinamo City
1
Power Rankings
Strongest overall
1
R. XhixhaTirana
87
2
Fabjan PërndrecaDinamo City
86
3
Ibrahim MustaphaDinamo City
82
4
Klevi QefalijaDinamo City
77
5
Florenc FerrukuDinamo City
76

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Tirana vs Dinamo City 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 Tirana vs Dinamo City 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 Tirana 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 Tirana vs Dinamo City 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 Tirana 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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