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OddsFlow AI Analysis
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53総合
⚡攻撃
53
⚙️中盤
58
🛡️守備
48
🧤GK
51
26人数
27.4平均年齢
63.2平均OVR
87.0深度スコア
53総合
⚡攻撃
53
⚙️中盤
58
🛡️守備
48
🧤GK
51
26人数
27.4平均年齢
63.2平均OVR
87.0深度スコア
スター選手
M. Kovačić
Midfielder
OPR 77
75
J. Gvardiol
Defender
2G 4A (2201min)
M. Baturina
Midfielder
8G 4A (1872min)
I. Perišić
Attacker
10G 14A (3130min)
68
L. Modrić
Midfielder
3G 3A (3231min)
大会確率
4.1%優勝優勝
4.1%
85.2%グループ突破グループ突破
85.2%
強み
Prolific attack
Deep squad rotation
弱み
Uncertain goalkeeper
Manager & Tactical Profile
Zlatko Dalić
Croatian · Appointed October 2017
4-2-3-1Runner-up (2018)
2018 WC Runner-up2022 WC 3rd place
Qualifying
Winners — 7W-1D, 26 GF / 4 GA
Notable Facts
- ◆Modrić 5th World Cup (NOT 6th - missed 2010)
- ◆Modrić at AC Milan now, age 40, 196 caps
- ◆Best qualifying campaign by GD ratio
Philosophy
Midfield dominance, technical quality, tactical discipline. Blends youth with veteran experience.
Manager Analysis
Zlatko Dalić is Croatia's most successful manager and the embodiment of the nation's remarkable overachievement on the world stage. Since taking charge in October 2017, he has led Croatia to a World Cup final (2018, losing 4-2 to France) and a World Cup third place (2022, beating Morocco 2-1). His achievement is extraordinary given Croatia's population of just 3.9 million — making them by far the smallest nation to consistently reach World Cup semi-finals. This will be his third World Cup as Croatia manager.
Tactical Deep Dive
Dalić's 4-2-3-1 is built entirely around midfield control, with the iconic partnership of Luka Modrić and Mateo Kovačić as the double pivot. At 40 years old and 196 caps, Modrić (now at AC Milan) brings unmatched experience and game intelligence. The transition question is critical: when Modrić eventually tires, Croatia's entire tactical identity changes. Gvardiol provides a modern dimension at left-back — at Man City he plays a hybrid CB/LB role pushing extremely high, and for Croatia he provides overlapping width. The striker position features Ante Budimir (Osasuna), a target man who offers aerial threat but less mobility than some alternatives.
Predicted Starting XI
Dominik Livaković
Dinamo Zagreb (loan from Fenerbahce)
Josip Stanišić
Bayern Munich
Duje Ćaleta-Car
Lyon
Josip Šutalo
Ajax
Joško Gvardiol
Manchester City
Minor - more inverted at club
Luka Modrić
AC Milan
System change - 3-man MF to double pivot
Mateo Kovačić
Manchester City
Minor - midfield 3 to double pivot
Ivan Perišić
PSV Eindhoven
Andrej Kramarić
Hoffenheim
Mario Pašalić
Atalanta
Moderate - central at club, right for Croatia
Ante Budimir
Osasuna
Key Bench
Martin Baturina
Como · AM — next-gen Modrić heir
Luka Sučić
Real Sociedad · CM rotation
Nikola Vlašić
Torino · AM/SS rotation
Borna Sosa
Crystal Palace · LB backup for Gvardiol
Team Strength & Intelligence
52
ATK
58
MID
48
DEF
50
GK
1.60
λ Attack
1.28
λ Defense
25
Chemistry
87
Depth
9.4
Star Gap
27.4
Avg Age
Top Scorer
A. Budimir
18 goals
Best Rated
L. Modrić
7.55
Club Clusters
Manchester City (2)Real Sociedad (2)
WC Experience
Appearances6
Best FinishRunner-up 2018
Last WC2022
Physical Resilience
Avg Mins/Season2,573
FatigueGood recovery — peak physical years
MiamiMODERATE
MonterreyMODERATE
Hot Climate %0%
Cooperation
Score36/100
Total Clubs23
Same-Club Pairs2
Same-League75
Average — typical international squad
H2H Matchup Predictions(47)
WinDrawLoss
キュラソー
1-0▼
56%
24%
20%
xG 1.6 – 0.8O2.5 45%BTTS 46%
イラク
0-1▼
56%
25%
19%
xG 1.6 – 0.9O2.5 45%BTTS 47%
Iran
1-0▼
55%
24%
20%
xG 1.6 – 0.9O2.5 46%BTTS 48%
ウズベキスタン
0-1▼
55%
25%
21%
xG 1.6 – 0.9O2.5 45%BTTS 47%
南アフリカ
1-0▼
54%
25%
21%
xG 1.6 – 0.9O2.5 45%BTTS 47%
South Korea
1-0▼
54%
26%
20%
xG 1.6 – 0.9O2.5 44%BTTS 46%
ハイチ
0-1▼
54%
25%
21%
xG 1.6 – 0.9O2.5 44%BTTS 46%
Cape Verde Islands
0-1▼
54%
24%
22%
xG 1.7 – 1.0O2.5 51%BTTS 52%
エジプト
1-1▼
51%
24%
25%
xG 1.7 – 1.2O2.5 56%BTTS 57%
カタール
1-1▼
48%
25%
27%
xG 1.6 – 1.1O2.5 50%BTTS 53%
チュニジア
1-1▼
47%
25%
28%
xG 1.6 – 1.2O2.5 53%BTTS 56%
ガーナ
1-1▼
46%
25%
28%
xG 1.6 – 1.2O2.5 51%BTTS 55%
ヨルダン
1-1▼
46%
26%
29%
xG 1.5 – 1.1O2.5 48%BTTS 51%
オーストラリア
1-1▼
46%
25%
29%
xG 1.5 – 1.1O2.5 48%BTTS 51%
Bosnia & Herzegovina
1-1▼
45%
25%
30%
xG 1.5 – 1.2O2.5 51%BTTS 54%
ニュージーランド
1-1▼
45%
26%
29%
xG 1.5 – 1.2O2.5 50%BTTS 54%
パナマ
1-1▼
45%
26%
30%
xG 1.5 – 1.1O2.5 48%BTTS 52%
メキシコ
1-1▼
45%
25%
30%
xG 1.6 – 1.2O2.5 53%BTTS 57%
サウジアラビア
1-1▼
45%
26%
30%
xG 1.5 – 1.1O2.5 49%BTTS 53%
アルジェリア
1-1▼
45%
24%
31%
xG 1.6 – 1.3O2.5 54%BTTS 57%
カナダ
1-1▼
43%
25%
32%
xG 1.4 – 1.2O2.5 49%BTTS 53%
ノルウェー
1-1▼
43%
25%
32%
xG 1.5 – 1.3O2.5 54%BTTS 57%
エクアドル
1-1▼
43%
27%
31%
xG 1.4 – 1.1O2.5 46%BTTS 52%
コンゴ民主共和国
1-1▼
43%
25%
32%
xG 1.4 – 1.2O2.5 50%BTTS 54%
パラグアイ
1-1▼
42%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 53%
スコットランド
1-1▼
42%
25%
32%
xG 1.5 – 1.3O2.5 52%BTTS 55%
コロンビア
1-1▼
42%
26%
33%
xG 1.4 – 1.3O2.5 50%BTTS 54%
モロッコ
1-1▼
41%
26%
33%
xG 1.4 – 1.2O2.5 49%BTTS 54%
アメリカ
1-1▼
41%
25%
34%
xG 1.4 – 1.3O2.5 50%BTTS 54%
Ivory Coast
1-1▼
41%
27%
33%
xG 1.4 – 1.2O2.5 49%BTTS 54%
スイス
1-1▼
40%
25%
34%
xG 1.4 – 1.3O2.5 51%BTTS 55%
Czech Republic
1-1▼
40%
26%
34%
xG 1.4 – 1.2O2.5 48%BTTS 52%
日本
1-1▼
40%
26%
34%
xG 1.4 – 1.3O2.5 51%BTTS 55%
スウェーデン
1-1▼
40%
26%
34%
xG 1.4 – 1.2O2.5 48%BTTS 52%
オーストリア
1-1▼
39%
26%
35%
xG 1.3 – 1.3O2.5 48%BTTS 53%
ベルギー
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 50%BTTS 53%
ブラジル
1-1▼
38%
25%
37%
xG 1.4 – 1.4O2.5 51%BTTS 55%
セネガル
1-1▼
37%
26%
36%
xG 1.3 – 1.3O2.5 49%BTTS 53%
オランダ
1-1▼
37%
26%
37%
xG 1.4 – 1.4O2.5 51%BTTS 56%
トルコ
1-1▼
37%
26%
37%
xG 1.3 – 1.3O2.5 49%BTTS 54%
ウルグアイ
1-1▼
37%
26%
37%
xG 1.3 – 1.3O2.5 47%BTTS 53%
アルゼンチン
1-1▼
36%
26%
39%
xG 1.3 – 1.4O2.5 51%BTTS 56%
スペイン
1-1▼
35%
25%
39%
xG 1.3 – 1.4O2.5 51%BTTS 55%
ドイツ
1-1▼
35%
26%
39%
xG 1.3 – 1.4O2.5 51%BTTS 55%
ポルトガル
1-1▼
34%
26%
40%
xG 1.2 – 1.4O2.5 48%BTTS 53%
イングランド
1-1▼
34%
26%
40%
xG 1.3 – 1.4O2.5 50%BTTS 54%
フランス
1-1▼
33%
25%
42%
xG 1.2 – 1.4O2.5 48%BTTS 53%
Sensitivity Analysis(51 scenarios)
Rank Distribution Across Scenarios
Heat ×3▼4
#5
Altitude ×0.5▼4
#5
Altitude ×2▼4
#5
Travel ×0.75▼4
#5
Mismatch t=0.05 a=0.3▼4
#5
Penalty rand=0.3▼4
#5
Penalty rand=0.5▼4
#5
Uplift 1▼4
#5
Altitude ×2.5▼3
#6
Altitude ×1.75▼3
#6
Travel ×0.5▼3
#6
Spread 1▼3
#6
Spread 2.1▼3
#6
Spread 3▼3
#6
Penalty rand=0.7▼3
#6
Heat ×0▼2
#7
Heat ×0.5▼2
#7
Heat ×0.75▼2
#7
Heat ×2▼2
#7
Heat ×0.25▼2
#7
Altitude ×0▼2
#7
Altitude ×1.25▼2
#7
Altitude ×1.5▼2
#7
Altitude ×0.25▼2
#7
Travel ×0▼2
#7
Travel ×1.25▼2
#7
Travel ×2▼2
#7
Travel ×2.5▼2
#7
Travel ×3▼2
#7
Travel ×0.25▼2
#7
Host ×0▼2
#7
Mismatch t=0.1 a=0.5▼2
#7
Spread 1.5▼2
#7
Spread 2.5▼2
#7
Heat ×1.25▼1
#8
Heat ×1.5▼1
#8
Heat ×2.5▼1
#8
Heat ×1.75▼1
#8
Altitude ×0.75▼1
#8
Altitude ×3▼1
#8
Travel ×1.5▼1
#8
Travel ×1.75▼1
#8
Host ×0.5▼1
#8
Host ×1.5▼1
#8
Host ×2▼1
#8
Host ×3▼1
#8
Mismatch t=0.15 a=0.7▼1
#8
Mismatch t=0.2 a=0.9▼1
#8
Mismatch t=0.3 a=1.2▼1
#8
Uplift 1.15▼1
#8
Baseline—
#9
Baseline: #9·Lower rank = stronger
baseline
1 scenarios · #9
heat
10 scenarios · #5–#8
altitude
10 scenarios · #5–#8
travel
10 scenarios · #5–#8
host
5 scenarios · #7–#8
mismatch
5 scenarios · #5–#8
spread
5 scenarios · #6–#7
penalty
3 scenarios · #5–#6
uplift
2 scenarios · #5–#8
