· Friendly International
Uganda
ATT
5/10
DEF
5/10
vs
Congo DR
5/10
ATT
5/10
DEF
Planlandı· Mon 5 Oct · 00:00
En iyi tahmin
KG VAR
54%
Doğru skor
Mükemmel
1-1
11.7%
8.53
Mükemmel
1-0
10.4%
9.58
2-1
9.5%
10.54
2-0
8.5%
11.82
0-1
7.2%
13.83
1-2
6.6%
15.21
0-0
6.4%
15.51
2-2
5.3%
18.78
149%
2.04TOP
X25%
4.05
226%
3.79
1X74%
X251%
1275%
Probable XI4-4-2
Kusiima
Odong
Kayondo
Mugabi
Capradossi
Okello
Oyirwoth
Alhassan
Byaruhanga
Kabuye
Omedi
Impact Player Hot Head
Kadrolar henüz açıklanmadı — aşağıda tam kadro analizi
Uganda10 oyuncu
48.1/ 99Orta
En İyiler
1Denis Onyango GK52
2Baba AlhassanMID52
3Khalid AuchoMID52
Congo DR8 oyuncu
50.8/ 99Orta
En İyiler
1Lionel Mpasi-NzauGK54
2Steve KapuadiDEF54
3Gédéon KaluluDEF53
Uganda63
PlayerGenel DereceEtkiAgresyonDisiplin
Denis Onyango 52524550
Baba Alhassan52534950
Khalid Aucho52495056
Allan Okello51525150
U. Ikpeazu51457056
Jordan Obita49494750
Steven Mukwala47474750
Toby Sibbick46464550
Hilary Mukundane44565526
Kenneth Semakula37536713
Crispus Kusiima————
Hannington Sebwalunyo————
Ismail Watenga————
Congo DR64
PlayerGenel DereceEtkiAgresyonDisiplin
Lionel Mpasi-Nzau54554550
Steve Kapuadi54614550
Gédéon Kalulu53455361
Axel Tuanzebe51544550
Chancel Mbemba49514550
Ngal'ayel Mukau49454856
Samuel Moutoussamy49454856
Cédric Bakambu47474550
Baggio Siadi————
Brudel Efonge————
Dimitry Bertaud————
Esdras Kabamba————
Jackson Lunanga————
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
Steven MukwalaUganda
47
2
Cédric BakambuCongo DR
47
3
U. IkpeazuUganda
45
Duvar
En iyi savunmacılar
1
Steve KapuadiCongo DR
61
2
Hilary MukundaneUganda
56
3
Axel TuanzebeCongo DR
54
4
Chancel MbembaCongo DR
51
5
Jordan ObitaUganda
49
Kontrol
En iyi oyun kurucular
1
Baba AlhassanUganda
53
2
Kenneth SemakulaUganda
53
3
Allan OkelloUganda
52
4
Khalid AuchoUganda
49
5
Ngal'ayel MukauCongo DR
45
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Kenneth SemakulaUganda
13
2
Hilary MukundaneUganda
26
3
Denis Onyango Uganda
50
4
Jordan ObitaUganda
50
5
Toby SibbickUganda
50
Güç sıralaması
Genel olarak en güçlüler
1
Lionel Mpasi-NzauCongo DR
54
2
Steve KapuadiCongo DR
54
3
Gédéon KaluluCongo DR
53
4
Denis Onyango Uganda
52
5
Baba AlhassanUganda
52

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Uganda vs Congo DR 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 Uganda vs Congo DR 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 Uganda 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 Uganda vs Congo DR 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 Uganda 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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