Bilbao’s expected territorial pressure and steady stream of set-piece chances should keep the contest alive, while Espanyol’s capable attack offers a route to respond. That combination points toward an increasingly open encounter, with the goal line likely breached.
Match GoalsOver 2.5
Nejlepší tip
OBÁ SKÓRUJÍ
53%
Přesný výsledek
Výborná
1-1
12.3%
8.00
Výborná
1-0
10.5%
6.50
2-1
9%
8.50
0-1
8.4%
17.00
2-0
7.7%
7.00
0-0
7.2%
11.00
1-2
7.2%
21.00
2-2
5.3%
19.00
144%
1.48TOP
X26%
4.10
230%
7.00
1X70%
X256%
1274%
Combined Bet Builder
3 legs
Athletic’s 1.71 xG against Espanyol’s 0.90 points to a clear home edge, while their 0.73 first-half xG and 2.91 projected first-half corners support an early lead. The 2.61 combined xG gives Over 2.5 a credible route, with Athletic’s pressure likely to turn their advantage into a winning, higher-scoring performance.
1Match WinnerAthletic Bilbao
2Match GoalsOver 2.5
3First Half WinnerAthletic Bilbao
Confirmed XI4-6-0
1
Simón
15
Rincón
3
Vivian
14
Laporte
17
Berchiche
28
Canales
24
Gerenabarrena
7
Berenguer
23
Navarro
10
Williams
9
Williams
Impact Player Hot Head
Athletic Bilbao31 hráči
51.0/ 99Průměrný
Nejlepší
1Nico SerranoFWD86
2Mikel VesgaMID80
3Mikel JauregizarMID70
RCD Espanyol33 hráči
51.8/ 99Průměrný
Nejlepší
1Miguel RubioDEF88
2Marcos FernándezFWD85
3Vanja DrkusicDEF70
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Marcos FernándezRCD Espanyol
99
2
Nico SerranoAthletic Bilbao
91
3
Kike GarcíaRCD Espanyol
61
4
Tyrhys DolanRCD Espanyol
54
5
Gorka GuruzetaAthletic Bilbao
53
Zeď
Nejlepší obránci
1
Miguel RubioRCD Espanyol
99
2
Vanja DrkusicRCD Espanyol
80
3
Leandro CabreraRCD Espanyol
74
4
Pablo RamónRCD Espanyol
73
5
Yeray Álvarez Athletic Bilbao
67
Kontrola
Nejlepší tvůrci hry
1
Mikel VesgaAthletic Bilbao
92
2
Edu ExpósitoRCD Espanyol
79
3
Unai VencedorAthletic Bilbao
71
4
Mikel JauregizarAthletic Bilbao
68
5
Peio CanalesAthletic Bilbao
62
Zlobivci
Nejpravděpodobnější žlutá karta
1
Edu ExpósitoRCD Espanyol
33
Agresivita 47
2
Pol LozanoRCD Espanyol
35
Agresivita 67
3
Roger HinojoRCD Espanyol
39
Agresivita 62
4
Pablo RamónRCD Espanyol
40
Agresivita 54
5
Yuri BerchicheAthletic Bilbao
41
Agresivita 42
Žebříček síly
Nejsilnější celkově
1
Miguel RubioRCD Espanyol
88
2
Nico SerranoAthletic Bilbao
86
3
Marcos FernándezRCD Espanyol
85
4
Mikel VesgaAthletic Bilbao
80
5
Mikel JauregizarAthletic Bilbao
70
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Athletic Bilbao vs RCD Espanyol 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 Athletic Bilbao vs RCD Espanyol 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 Athletic Bilbao 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 Athletic Bilbao vs RCD Espanyol 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 Athletic Bilbao 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.