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OddsFlow AI Analysis
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56종합
⚡공격
54
⚙️미드필드
60
🛡️수비
47
🧤골키핑
66
26인원
25.7평균 나이
64.8평균 OVR
86.0깊이 점수
56종합
⚡공격
54
⚙️미드필드
60
🛡️수비
47
🧤골키핑
66
26인원
25.7평균 나이
64.8평균 OVR
86.0깊이 점수
스타 선수
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)
대회 확률
5.2%우승우승
5.2%
83.0%조별 통과조별 통과
83.0%
강점
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
이라크
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%
퀴라소
0-1▼
59%
24%
17%
xG 1.7 – 0.8O2.5 46%BTTS 46%
우즈베키스탄
0-1▼
59%
23%
18%
xG 1.7 – 0.8O2.5 46%BTTS 45%
아이티
1-0▼
59%
24%
17%
xG 1.7 – 0.8O2.5 46%BTTS 45%
남아프리카
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%
이집트
1-1▼
55%
22%
23%
xG 1.8 – 1.1O2.5 56%BTTS 55%
튀니지
1-1▼
51%
24%
25%
xG 1.7 – 1.1O2.5 54%BTTS 55%
호주
1-1▼
50%
24%
26%
xG 1.6 – 1.1O2.5 50%BTTS 52%
카타르
1-1▼
49%
25%
26%
xG 1.6 – 1.1O2.5 49%BTTS 52%
멕시코
1-1▼
49%
25%
26%
xG 1.7 – 1.2O2.5 54%BTTS 56%
파나마
1-1▼
49%
25%
26%
xG 1.6 – 1.1O2.5 49%BTTS 51%
알제리
1-1▼
49%
24%
27%
xG 1.7 – 1.2O2.5 55%BTTS 56%
사우디아라비아
1-1▼
49%
26%
26%
xG 1.6 – 1.1O2.5 49%BTTS 52%
뉴질랜드
1-1▼
49%
25%
27%
xG 1.6 – 1.1O2.5 51%BTTS 54%
가나
1-1▼
48%
25%
27%
xG 1.5 – 1.1O2.5 49%BTTS 52%
캐나다
1-1▼
48%
26%
27%
xG 1.6 – 1.1O2.5 51%BTTS 54%
요르단
1-1▼
47%
26%
27%
xG 1.5 – 1.0O2.5 46%BTTS 49%
파라과이
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%
콩고민주공화국
1-1▼
47%
25%
29%
xG 1.5 – 1.1O2.5 50%BTTS 54%
스코틀랜드
1-1▼
45%
26%
29%
xG 1.5 – 1.1O2.5 48%BTTS 53%
에콰도르
1-1▼
45%
27%
28%
xG 1.4 – 1.1O2.5 47%BTTS 51%
노르웨이
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%
일본
1-1▼
44%
26%
30%
xG 1.5 – 1.2O2.5 51%BTTS 54%
미국
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%
콜롬비아
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 52%
스웨덴
1-1▼
43%
25%
31%
xG 1.5 – 1.2O2.5 49%BTTS 53%
세네갈
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 53%
오스트리아
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 52%
스위스
1-1▼
43%
26%
32%
xG 1.4 – 1.2O2.5 47%BTTS 52%
모로코
1-1▼
42%
27%
31%
xG 1.4 – 1.1O2.5 46%BTTS 51%
우루과이
1-1▼
42%
26%
32%
xG 1.4 – 1.2O2.5 47%BTTS 51%
튀르키예
1-1▼
42%
26%
33%
xG 1.4 – 1.2O2.5 49%BTTS 53%
크로아티아
1-1▼
41%
26%
32%
xG 1.4 – 1.2O2.5 48%BTTS 52%
벨기에
1-1▼
39%
26%
35%
xG 1.4 – 1.3O2.5 48%BTTS 54%
네덜란드
1-1▼
39%
27%
34%
xG 1.3 – 1.2O2.5 47%BTTS 53%
브라질
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 49%BTTS 54%
아르헨티나
1-1▼
38%
25%
37%
xG 1.4 – 1.3O2.5 50%BTTS 54%
포르투갈
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 48%BTTS 53%
독일
1-1▼
38%
27%
35%
xG 1.3 – 1.3O2.5 47%BTTS 53%
잉글랜드
1-1▼
38%
26%
37%
xG 1.3 – 1.3O2.5 48%BTTS 53%
프랑스
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
