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OddsFlow AI Analysis
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56総合
⚡攻撃
54
⚙️中盤
60
🛡️守備
47
🧤GK
66
26人数
25.7平均年齢
64.8平均OVR
86.0深度スコア
56総合
⚡攻撃
54
⚙️中盤
60
🛡️守備
47
🧤GK
66
26人数
25.7平均年齢
64.8平均OVR
86.0深度スコア
スター選手
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)
大会確率
5.2%優勝優勝
5.2%
83.0%グループ突破グループ突破
83.0%
強み
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
イラク
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%
キュラソー
0-1▼
59%
24%
17%
xG 1.7 – 0.8O2.5 46%BTTS 46%
ウズベキスタン
0-1▼
59%
23%
18%
xG 1.7 – 0.8O2.5 46%BTTS 45%
ハイチ
1-0▼
59%
24%
17%
xG 1.7 – 0.8O2.5 46%BTTS 45%
南アフリカ
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%
エジプト
1-1▼
55%
22%
23%
xG 1.8 – 1.1O2.5 56%BTTS 55%
チュニジア
1-1▼
51%
24%
25%
xG 1.7 – 1.1O2.5 54%BTTS 55%
オーストラリア
1-1▼
50%
24%
26%
xG 1.6 – 1.1O2.5 50%BTTS 52%
カタール
1-1▼
49%
25%
26%
xG 1.6 – 1.1O2.5 49%BTTS 52%
メキシコ
1-1▼
49%
25%
26%
xG 1.7 – 1.2O2.5 54%BTTS 56%
パナマ
1-1▼
49%
25%
26%
xG 1.6 – 1.1O2.5 49%BTTS 51%
アルジェリア
1-1▼
49%
24%
27%
xG 1.7 – 1.2O2.5 55%BTTS 56%
サウジアラビア
1-1▼
49%
26%
26%
xG 1.6 – 1.1O2.5 49%BTTS 52%
ニュージーランド
1-1▼
49%
25%
27%
xG 1.6 – 1.1O2.5 51%BTTS 54%
ガーナ
1-1▼
48%
25%
27%
xG 1.5 – 1.1O2.5 49%BTTS 52%
カナダ
1-1▼
48%
26%
27%
xG 1.6 – 1.1O2.5 51%BTTS 54%
ヨルダン
1-1▼
47%
26%
27%
xG 1.5 – 1.0O2.5 46%BTTS 49%
パラグアイ
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%
コンゴ民主共和国
1-1▼
47%
25%
29%
xG 1.5 – 1.1O2.5 50%BTTS 54%
スコットランド
1-1▼
45%
26%
29%
xG 1.5 – 1.1O2.5 48%BTTS 53%
エクアドル
1-1▼
45%
27%
28%
xG 1.4 – 1.1O2.5 47%BTTS 51%
ノルウェー
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%
日本
1-1▼
44%
26%
30%
xG 1.5 – 1.2O2.5 51%BTTS 54%
アメリカ
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%
コロンビア
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 52%
スウェーデン
1-1▼
43%
25%
31%
xG 1.5 – 1.2O2.5 49%BTTS 53%
セネガル
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 53%
オーストリア
1-1▼
43%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 52%
スイス
1-1▼
43%
26%
32%
xG 1.4 – 1.2O2.5 47%BTTS 52%
モロッコ
1-1▼
42%
27%
31%
xG 1.4 – 1.1O2.5 46%BTTS 51%
ウルグアイ
1-1▼
42%
26%
32%
xG 1.4 – 1.2O2.5 47%BTTS 51%
トルコ
1-1▼
42%
26%
33%
xG 1.4 – 1.2O2.5 49%BTTS 53%
クロアチア
1-1▼
41%
26%
32%
xG 1.4 – 1.2O2.5 48%BTTS 52%
ベルギー
1-1▼
39%
26%
35%
xG 1.4 – 1.3O2.5 48%BTTS 54%
オランダ
1-1▼
39%
27%
34%
xG 1.3 – 1.2O2.5 47%BTTS 53%
ブラジル
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 49%BTTS 54%
アルゼンチン
1-1▼
38%
25%
37%
xG 1.4 – 1.3O2.5 50%BTTS 54%
ポルトガル
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 48%BTTS 53%
ドイツ
1-1▼
38%
27%
35%
xG 1.3 – 1.3O2.5 47%BTTS 53%
イングランド
1-1▼
38%
26%
37%
xG 1.3 – 1.3O2.5 48%BTTS 53%
フランス
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
