· LaLiga
Real Madrid
ATT
8/10
DEF
6/10
vs
RC Celta
4/10
ATT
7/10
DEF
Planlandı· Sat 21 Nov · 20:00

AI Maç Analizi

Real Madrid’s overwhelming attacking strength and sustained home pressure should dictate the tempo, while Celta’s defensive resilience may delay rather than prevent breakthroughs; mounting pressure makes a high-scoring, over-the-line contest the likeliest script.

Match GoalsOver 2.5
En iyi tahmin
Ev sahibi kazanır
64%
Doğru skor
Mükemmel
2-0
10.7%
7.50
Mükemmel
1-0
10.2%
10.00
2-1
9.9%
10.00
1-1
9.5%
12.00
3-0
7.5%
8.00
3-1
6.9%
10.00
0-0
4.9%
21.00
2-2
4.6%
21.00
164%
1.20TOP
X20%
6.25
216%
13.00
1X84%
X236%
1280%

Kombine

3 seçim

Real Madrid’s 2.57 xG and +63% attack power point to sustained pressure, while Celta’s 0.27 first-half xG suggests limited threat early. Madrid’s 1.34 HT xG supports a first-half lead; their scoring edge should carry them to victory, with enough attacking volume to make Over 3.5 a logical route despite Celta’s strong defensive profile.

1Match WinnerReal Madrid
2Match GoalsOver 3.5
3First Half WinnerReal Madrid
Confirmed XI3-5-2
1
Courtois
16
Konaté
4
Huijsen
17
Cucurella
8
Valverde
20
Silva
14
Tchouaméni
15
Güler
5
Bellingham
25
Diomande
10
Mbappé
Impact Player Hot Head
Real Madrid34 oyuncu
50.9/ 99Orta
En İyiler
1EndrickFWD82
2Carlos EspíFWD80
3Kylian Mbappé FWD79
RC Celta32 oyuncu
50.0/ 99Orta
En İyiler
1Hugo BurcioMID69
2Javi RuedaDEF66
3Matías VecinoMID65
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
EndrickReal Madrid
99
2
Kylian Mbappé Real Madrid
99
3
Carlos EspíReal Madrid
87
4
Vinicius JuniorReal Madrid
64
5
Hugo GonzálezRC Celta
63
Duvar
En iyi savunmacılar
1
Carl Starfelt RC Celta
74
2
Dean HuijsenReal Madrid
70
3
Yoel LagoRC Celta
70
4
Manu FernándezRC Celta
58
5
Abdoulaye FayeRC Celta
57
Kontrol
En iyi oyun kurucular
1
Arda GülerReal Madrid
81
2
Aurélien TchouaméniReal Madrid
78
3
Eduardo Camavinga Real Madrid
78
4
Hugo SoteloRC Celta
72
5
Federico ValverdeReal Madrid
68
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Aleix FebasRC Celta
38
2
Trent Alexander-ArnoldReal Madrid
42
3
Iago Aspas RC Celta
42
4
Dean HuijsenReal Madrid
43
5
Aurélien TchouaméniReal Madrid
43
Güç sıralaması
Genel olarak en güçlüler
1
EndrickReal Madrid
82
2
Carlos EspíReal Madrid
80
3
Kylian Mbappé Real Madrid
79
4
Eduardo Camavinga Real Madrid
78
5
Hugo BurcioRC Celta
69

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Real Madrid vs RC Celta 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 Real Madrid vs RC Celta 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 Real Madrid 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 Real Madrid vs RC Celta 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 Real Madrid 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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