· MLS
Dallas
ATT
5/10
DEF
5/10
vs
San Jose Earthquakes
6/10
ATT
7/10
DEF
Naplánováno· Sun 25 Oct · 00:30

AI Match Analysis

Dallas’s home pressure should keep the game moving, while San Jose bring a sharper attacking edge and enough set-piece threat to answer. That contrasting momentum points toward an open contest, with the goal line likely to be crossed.

Match GoalsOver 2.5
Nejlepší tip
OBÁ SKÓRUJÍ
62%
Přesný výsledek
Výborná
1-1
10.7%
8.50
Výborná
2-1
8.6%
8.50
1-2
8.2%
15.00
1-0
7%
12.00
0-1
6.7%
19.00
2-2
6.5%
12.00
2-0
5.6%
11.00
0-2
5.2%
26.00
139%
1.80TOP
X24%
4.20
237%
3.90
1X63%
X261%
1276%

Combined Bet Builder

3 legs

Dallas’ 2.09 xG gives them a strong platform to edge San Jose, while the visitors’ 1.44 xG points to a match with enough scoring potential to clear 2.5. San Jose’s resilient defense is a hurdle, but Dallas’ home advantage should create chances for Petar Musa to find the net and complete the builder.

1Match WinnerDallas
2Match GoalsOver 2.5
3GoalscorersAnytime
Confirmed XI3-5-2
42
Daniel
18
Moore
25
Ibeagha
32
Norris
14
Johansson
17
Ramiro
12
Cappis
77
Kamungo
8
Delgado
7
Job
9
Musa
Impact Player Hot Head
Dallas27 hráči
49.8/ 99Průměrný
Nejlepší
1Petar MusaFWD80
2Osaze UrhoghideDEF69
3Nolan NorrisDEF67
San Jose Earthquakes28 hráči
48.6/ 99Průměrný
Nejlepší
1Vítor CostaDEF79
2Nick FernandezMID74
3Jamar RickettsDEF64
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Petar MusaDallas
83
2
Sam SarverDallas
75
3
Preston JuddSan Jose Earthquakes
74
4
Logan FarringtonDallas
54
5
Daniel JobDallas
49
Zeď
Nejlepší obránci
1
Max FlorianiSan Jose Earthquakes
76
2
Osaze UrhoghideDallas
73
3
Sebastien IbeaghaDallas
72
4
Nolan NorrisDallas
71
5
Vítor CostaSan Jose Earthquakes
66
Kontrola
Nejlepší tvůrci hry
1
Eduard LöwenSan Jose Earthquakes
69
2
Beau LerouxSan Jose Earthquakes
66
3
RamiroDallas
64
4
Clay HolstadDallas
62
5
Nick FernandezSan Jose Earthquakes
59
Zlobivci
Nejpravděpodobnější žlutá karta
1
Sam SarverDallas
1
2
Preston JuddSan Jose Earthquakes
1
3
Reid RobertsSan Jose Earthquakes
3
4
Patrickson DelgadoDallas
8
5
Enes SaliDallas
9
Žebříček síly
Nejsilnější celkově
1
Petar MusaDallas
80
2
Vítor CostaSan Jose Earthquakes
79
3
Nick FernandezSan Jose Earthquakes
74
4
Osaze UrhoghideDallas
69
5
Nolan NorrisDallas
67

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Dallas vs San Jose Earthquakes 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 Dallas vs San Jose Earthquakes 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 Dallas 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 Dallas vs San Jose Earthquakes 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 Dallas 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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