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OddsFlow AI Analysis
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56Общий
⚡Атака
54
⚙️Полузащита
60
🛡️Защита
47
🧤Вратарь
66
26Размер
25.7Ср. возраст
64.8Ср. OVR
86.0Счёт глубины
56Общий
⚡Атака
54
⚙️Полузащита
60
🛡️Защита
47
🧤Вратарь
66
26Размер
25.7Ср. возраст
64.8Ср. OVR
86.0Счёт глубины
Звёздные игроки
Rodri
Midfielder
3G 0A (2970min)
David Raya
Goalkeeper
OPR 77
Lamine Yamal
Attacker
25G 21A (4320min)
Joan García
Goalkeeper
OPR 71
Pedri
Midfielder
2G 13A (3378min)
Вероятность на турнире
5.2%ЧемпионЧемпион
5.2%
83.0%Выход из группыВыход из группы
83.0%
Сильные стороны
Prolific attack
Top-class goalkeeper
Strong team chemistry
Deep squad rotation
Balanced scoring threat
Manager & Tactical Profile
Luis de la Fuente
Spanish · Appointed December 2022
4-3-3Winners (2010)
Euro 2024 (7/7 wins)2022-23 Nations LeagueU-19 Euro 2015U-21 Euro 2019
Qualifying
Winners — 16pts (5W-1D)
Notable Facts
- ◆ZERO Real Madrid players (first since 1950)
- ◆Morata + Carvajal excluded
- ◆Yamal hamstring - will miss game 1
- ◆Rodri is 2024 Ballon d Or winner
Philosophy
Traditional Spanish possession + modern pace. Rodri as pivot, Yamal + Williams provide width and directness.
Manager Analysis
Luis de la Fuente is the quiet revolutionary who has rebuilt Spain into the most exciting team in world football. After spending approximately 9.5 years within the Spanish FA system (coaching U-19, U-21, and Olympic teams from 2013), he was promoted to senior manager in December 2022 — and immediately delivered Euro 2024 with a perfect 7-match winning record, including a 2-1 final victory over England. His squad selection for WC 2026 made history: for the first time since 1950, Spain's World Cup squad contains zero Real Madrid players.
Tactical Deep Dive
De la Fuente's 4-3-3 is a modernized version of Spain's traditional possession philosophy. Rodri (2024 Ballon d'Or winner) anchors the midfield as the single pivot, with Pedri and Fabián Ruiz as the interior midfielders. The revolutionary element is the width and directness provided by Lamine Yamal (18, right wing) and Nico Williams (left wing) — two explosive wingers who give Spain a pace dimension they've never had before. The full-backs (Cucurella, Llorente) overlap aggressively in attack, creating numerical advantages in wide areas.
Predicted Starting XI
Unai Simón
Athletic Bilbao
Marcos Llorente
Atletico Madrid
Pau Cubarsí
Barcelona
Aymeric Laporte
Athletic Bilbao
Marc Cucurella
Chelsea
At club inverts, for Spain traditional overlap
Rodri
Manchester City
Pedri
Barcelona
Fabián Ruiz
PSG
Minor - higher at PSG
Nico Williams
Athletic Bilbao
Mikel Oyarzabal
Real Sociedad
Lamine Yamal
Barcelona
None (hamstring - may miss game 1)
Key Bench
Dani Olmo
Barcelona · Yamal deputy (RW) or #10
Gavi
Barcelona · CM rotation
Mikel Merino
Arsenal · Box-to-box CM
Martín Zubimendi
Arsenal · CDM backup for Rodri
Team Strength & Intelligence
54
ATK
60
MID
47
DEF
66
GK
1.68
λ Attack
1.06
λ Defense
100
Chemistry
86
Depth
10.3
Star Gap
25.7
Avg Age
Top Scorer
Lamine Yamal
24 goals
Best Rated
Lamine Yamal
7.99
Club Clusters
Barcelona (8)Arsenal (3)Athletic Club (3)Atletico Madrid (3)
WC Experience
Appearances16
Best FinishChampion 2010
Last WC2022
Physical Resilience
Avg Mins/Season3,435
FatigueFast recovery — young squad handles cumulative fatigue well
MiamiMODERATE
MonterreyMODERATE
Hot Climate %0%
Cooperation
Score79/100
Total Clubs13
Same-Club Pairs37
Same-League232
Excellent — strong club connections
H2H Matchup Predictions(47)
WinDrawLoss
Ирак
1-0▼
60%
23%
17%
xG 1.7 – 0.8O2.5 47%BTTS 45%
Iran
0-1▼
60%
23%
17%
xG 1.7 – 0.8O2.5 47%BTTS 45%
Кюрасао
0-1▼
59%
24%
17%
xG 1.7 – 0.8O2.5 46%BTTS 46%
Узбекистан
0-1▼
59%
23%
18%
xG 1.7 – 0.8O2.5 46%BTTS 45%
Гаити
1-0▼
59%
24%
17%
xG 1.7 – 0.8O2.5 46%BTTS 45%
ЮАР
0-1▼
59%
24%
17%
xG 1.7 – 0.8O2.5 46%BTTS 46%
South Korea
0-1▼
58%
24%
18%
xG 1.7 – 0.8O2.5 46%BTTS 46%
Cape Verde Islands
1-0▼
57%
23%
20%
xG 1.8 – 1.0O2.5 53%BTTS 52%
Египет
1-1▼
55%
22%
23%
xG 1.8 – 1.1O2.5 56%BTTS 55%
Тунис
1-1▼
51%
24%
25%
xG 1.7 – 1.1O2.5 54%BTTS 55%
Австралия
1-1▼
50%
24%
26%
xG 1.6 – 1.1O2.5 50%BTTS 52%
Катар
1-1▼
49%
25%
26%
xG 1.6 – 1.1O2.5 49%BTTS 52%
Мексика
1-1▼
49%
25%
26%
xG 1.7 – 1.2O2.5 54%BTTS 56%
Панама
1-1▼
49%
25%
26%
xG 1.6 – 1.1O2.5 49%BTTS 51%
Алжир
1-1▼
49%
24%
27%
xG 1.7 – 1.2O2.5 55%BTTS 56%
Саудовская Аравия
1-1▼
49%
26%
26%
xG 1.6 – 1.1O2.5 49%BTTS 52%
Новая Зеландия
1-1▼
49%
25%
27%
xG 1.6 – 1.1O2.5 51%BTTS 54%
Гана
1-1▼
48%
25%
27%
xG 1.5 – 1.1O2.5 49%BTTS 52%
Канада
1-1▼
48%
26%
27%
xG 1.6 – 1.1O2.5 51%BTTS 54%
Иордания
1-1▼
47%
26%
27%
xG 1.5 – 1.0O2.5 46%BTTS 49%
Парагвай
1-1▼
47%
26%
27%
xG 1.5 – 1.1O2.5 49%BTTS 52%
Bosnia & Herzegovina
1-1▼
47%
25%
28%
xG 1.5 – 1.1O2.5 50%BTTS 54%
ДР Конго
1-1▼
47%
25%
29%
xG 1.5 – 1.1O2.5 50%BTTS 54%
Шотландия
1-1▼
45%
26%
29%
xG 1.5 – 1.1O2.5 48%BTTS 53%
Эквадор
1-1▼
45%
27%
28%
xG 1.4 – 1.1O2.5 47%BTTS 51%
Норвегия
1-1▼
44%
25%
31%
xG 1.5 – 1.2O2.5 52%BTTS 55%
Czech Republic
1-1▼
44%
26%
30%
xG 1.4 – 1.1O2.5 48%BTTS 53%
Япония
1-1▼
44%
26%
30%
xG 1.5 – 1.2O2.5 51%BTTS 54%
США
1-1▼
44%
25%
31%
xG 1.5 – 1.2O2.5 49%BTTS 53%
Ivory Coast
1-1▼
44%
25%
31%
xG 1.4 – 1.2O2.5 49%BTTS 52%
Колумбия
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 52%
Швеция
1-1▼
43%
25%
31%
xG 1.5 – 1.2O2.5 49%BTTS 53%
Сенегал
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 53%
Австрия
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 52%
Швейцария
1-1▼
43%
26%
32%
xG 1.4 – 1.2O2.5 47%BTTS 52%
Марокко
1-1▼
42%
27%
31%
xG 1.4 – 1.1O2.5 46%BTTS 51%
Уругвай
1-1▼
42%
26%
32%
xG 1.4 – 1.2O2.5 47%BTTS 51%
Турция
1-1▼
42%
26%
33%
xG 1.4 – 1.2O2.5 49%BTTS 53%
Хорватия
1-1▼
41%
26%
32%
xG 1.4 – 1.2O2.5 48%BTTS 52%
Бельгия
1-1▼
39%
26%
35%
xG 1.4 – 1.3O2.5 48%BTTS 54%
Нидерланды
1-1▼
39%
27%
34%
xG 1.3 – 1.2O2.5 47%BTTS 53%
Бразилия
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 49%BTTS 54%
Аргентина
1-1▼
38%
25%
37%
xG 1.4 – 1.3O2.5 50%BTTS 54%
Португалия
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 48%BTTS 53%
Германия
1-1▼
38%
27%
35%
xG 1.3 – 1.3O2.5 47%BTTS 53%
Англия
1-1▼
38%
26%
37%
xG 1.3 – 1.3O2.5 48%BTTS 53%
Франция
1-1▼
37%
26%
37%
xG 1.3 – 1.3O2.5 48%BTTS 54%
Sensitivity Analysis(51 scenarios)
Rank Distribution Across Scenarios
Travel ×0▼1
#9
Travel ×0.5▼1
#9
Travel ×0.25▼1
#9
Host ×0▼1
#9
Mismatch t=0.2 a=0.9▼1
#9
Mismatch t=0.3 a=1.2▼1
#9
Baseline—
#10
Heat ×0—
#10
Heat ×0.5—
#10
Heat ×0.75—
#10
Heat ×1.25—
#10
Heat ×1.5—
#10
Heat ×2—
#10
Heat ×2.5—
#10
Heat ×3—
#10
Heat ×0.25—
#10
Heat ×1.75—
#10
Altitude ×0—
#10
Altitude ×0.5—
#10
Altitude ×0.75—
#10
Altitude ×1.25—
#10
Altitude ×1.5—
#10
Altitude ×2—
#10
Altitude ×2.5—
#10
Altitude ×3—
#10
Altitude ×0.25—
#10
Altitude ×1.75—
#10
Travel ×0.75—
#10
Travel ×1.25—
#10
Travel ×1.5—
#10
Travel ×2—
#10
Travel ×2.5—
#10
Travel ×3—
#10
Travel ×1.75—
#10
Host ×0.5—
#10
Host ×1.5—
#10
Host ×2—
#10
Host ×3—
#10
Mismatch t=0.05 a=0.3—
#10
Mismatch t=0.1 a=0.5—
#10
Mismatch t=0.15 a=0.7—
#10
Spread 1—
#10
Spread 1.5—
#10
Spread 2.1—
#10
Spread 2.5—
#10
Spread 3—
#10
Penalty rand=0.3—
#10
Penalty rand=0.5—
#10
Penalty rand=0.7—
#10
Uplift 1—
#10
Uplift 1.15—
#10
Baseline: #10·Lower rank = stronger
baseline
1 scenarios · #10
heat
10 scenarios · #10
altitude
10 scenarios · #10
travel
10 scenarios · #9–#10
host
5 scenarios · #9–#10
mismatch
5 scenarios · #9–#10
spread
5 scenarios · #10
penalty
3 scenarios · #10
uplift
2 scenarios · #10
