· Friendly International
Rwanda
ATT
6/10
DEF
6/10
vs
Kenya
6/10
ATT
6/10
DEF
Scheduled· Mon 5 Oct · 16:00
Best Pick
BTTS
55%
Correct Score
Top
1-1
11.5%
8.71
Top
1-0
10.2%
9.85
2-1
9.6%
10.44
2-0
8.5%
11.80
0-1
6.9%
14.53
1-2
6.5%
15.40
0-0
6.1%
16.44
2-2
5.4%
18.45
150%
2.00TOP
X24%
4.12
226%
3.87
1X74%
X250%
1276%
Probable XI4-4-2
Twizere
Rwatubyaye
Hakizimana
Serumogo
Mutsinzi
Mugisha
Ngwabije
Niyo
Manishimwe
Biramahire
Ishimwe
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Rwanda10 players
45.6/ 99Average
Top Rated
1Abeddy BiramahireFWD47
2Bonheur MugishaMID46
3Djihad BizimanaMID46
Kenya28 players
42.8/ 99Average
Top Rated
1Bryne OdhiamboGK46
2F. ShikhaloGK46
3I. OtienoGK46
Rwanda54
PlayerOverall RatingImpactAggressionDiscipline
Abeddy Biramahire47474550
Bonheur Mugisha46454550
Djihad Bizimana46454550
Jojea Kwizera46454550
Leroy Mickels46464550
Ange Mutsinzi45454550
Claude Niyomugabo45454550
Gilbert Byiringiro45454550
Phanuel Kavita45454550
Joy-Lance Mickels45454550
Clement Twizere————
Fiacre Ntwari————
Gad Muhawenayo————
Kenya75
PlayerOverall RatingImpactAggressionDiscipline
Bryne Odhiambo46454550
F. Shikhalo46454550
I. Otieno46454550
Alpha Chris Onyango46454550
Austin Otieno46454550
Chrispine Erambo46454550
Clarke Oduor46454550
Marvin Omondi46454550
T. Akumu46454550
Daniel Sakari45454550
Frank Onyango Odhiambo45454550
M. Okwaro45454550
Michael Kibwage45454550
Player Rankings
Snipers
Top goal threats
1
Abeddy BiramahireRwanda
47
2
Leroy MickelsRwanda
46
3
Joy-Lance MickelsRwanda
45
4
B. OmondiKenya
45
5
M. BajaberKenya
45
Wall
Top defenders
1
Ange MutsinziRwanda
45
2
Claude NiyomugaboRwanda
45
3
Gilbert ByiringiroRwanda
45
4
Phanuel KavitaRwanda
45
5
Daniel SakariKenya
45
Control
Top playmakers
1
Bonheur MugishaRwanda
45
2
Djihad BizimanaRwanda
45
3
Jojea KwizeraRwanda
45
4
Alpha Chris OnyangoKenya
45
5
Austin OtienoKenya
45
Bad Boys
Most likely to get booked
1
Ange MutsinziRwanda
50
2
Claude NiyomugaboRwanda
50
3
Gilbert ByiringiroRwanda
50
4
Phanuel KavitaRwanda
50
5
Bonheur MugishaRwanda
50
Power Rankings
Strongest overall
1
Abeddy BiramahireRwanda
47
2
Bonheur MugishaRwanda
46
3
Djihad BizimanaRwanda
46
4
Jojea KwizeraRwanda
46
5
Leroy MickelsRwanda
46

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Rwanda vs Kenya 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 Rwanda vs Kenya 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 Rwanda 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 Rwanda vs Kenya 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 Rwanda 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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