· LaLiga
Levante
ATT
5/10
DEF
4/10
vs
Atletico Madrid
6/10
ATT
6/10
DEF
Scheduled· Sat 31 Oct · 13:00

AI Match Analysis

Atletico’s sharper attack should press a clear advantage against Levante’s leaky back line, while the hosts have enough attacking promise to make this more than a one-sided affair. That blend of visitor pressure and home resistance points to an open, dramatic contest, with the three-goal line likely to be breached.

Match GoalsOver 2.5
Best Pick
BTTS
56%
Correct Score
Top
1-1
11.8%
8.50
Top
1-0
9.6%
19.00
2-1
9.2%
15.00
0-1
7.5%
10.00
2-0
7.5%
29.00
1-2
7.2%
8.50
0-0
6.1%
17.00
2-2
5.6%
13.00
145%
4.75TOP
X25%
4.10
230%
1.60
1X70%
X255%
1275%

Combined Bet Builder

3 legs

Atletico Madrid look the likeliest winners, with a stronger 1.96 xG and solid attack facing Levante’s defense, rated 20% weaker than average. Levante’s 1.11 xG gives them a credible route to score, while the combined 3.07 xG points toward over 2.5 goals and both teams to score.

1Match WinnerAtletico Madrid
2Match GoalsOver 3.5
3Both Teams to ScoreYes
Confirmed XI4-3-3
13
Ryan
29
Gómez
2
Mandi
3
Ndukwe
23
Sánchez
8
Olasagasti
6
Requena
24
Fernández
11
Musuayi
9
Romero
10
Ratkov
Impact Player Hot Head
Levante29 players
47.9/ 99Average
Top Rated
1Carlos EspíFWD84
2Karl Etta EyongFWD65
3Dani RequenaMID64
Atletico Madrid35 players
48.3/ 99Average
Top Rated
1Pablo BarriosMID74
2Iker LuqueFWD70
3Jonathan DavidFWD68
Player Rankings
Snipers
Top goal threats
1
Carlos EspíLevante
86
2
Jonathan DavidAtletico Madrid
78
3
Alexander SørlothAtletico Madrid
70
4
Miguel CuboAtletico Madrid
70
5
Iker LuqueAtletico Madrid
63
Wall
Top defenders
1
Adrián de la FuenteLevante
68
2
José María Giménez Atletico Madrid
68
3
Ifeanyi NdukweLevante
65
4
Unai ElgezabalLevante
61
5
Robin Le NormandAtletico Madrid
59
Control
Top playmakers
1
Koke Atletico Madrid
84
2
Pablo BarriosAtletico Madrid
73
3
Hugo SoteloLevante
70
4
Morten Hjulmand Atletico Madrid
68
5
Johnny CardosoAtletico Madrid
63
Bad Boys
Most likely to get booked
1
K. ArriagaLevante
1
2
Thiago FernándezLevante
1
3
Obed VargasAtletico Madrid
1
4
Giuliano SimeoneAtletico Madrid
9
5
Aïssa MandiLevante
12
Power Rankings
Strongest overall
1
Carlos EspíLevante
84
2
Pablo BarriosAtletico Madrid
74
3
Iker LuqueAtletico Madrid
70
4
Jonathan DavidAtletico Madrid
68
5
Miguel CuboAtletico Madrid
68

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Levante vs Atletico Madrid 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 Levante vs Atletico Madrid 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 Levante 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 Levante vs Atletico Madrid 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 Levante 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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