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OddsFlow AI Analysis
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53Общий
⚡Атака
53
⚙️Полузащита
58
🛡️Защита
48
🧤Вратарь
51
26Размер
27.4Ср. возраст
63.2Ср. OVR
87.0Счёт глубины
53Общий
⚡Атака
53
⚙️Полузащита
58
🛡️Защита
48
🧤Вратарь
51
26Размер
27.4Ср. возраст
63.2Ср. OVR
87.0Счёт глубины
Звёздные игроки
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)
Вероятность на турнире
4.1%ЧемпионЧемпион
4.1%
85.2%Выход из группыВыход из группы
85.2%
Сильные стороны
Prolific attack
Deep squad rotation
Слабые стороны
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
Кюрасао
1-0▼
56%
24%
20%
xG 1.6 – 0.8O2.5 45%BTTS 46%
Ирак
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%
Узбекистан
0-1▼
55%
25%
21%
xG 1.6 – 0.9O2.5 45%BTTS 47%
ЮАР
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%
Гаити
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%
Египет
1-1▼
51%
24%
25%
xG 1.7 – 1.2O2.5 56%BTTS 57%
Катар
1-1▼
48%
25%
27%
xG 1.6 – 1.1O2.5 50%BTTS 53%
Тунис
1-1▼
47%
25%
28%
xG 1.6 – 1.2O2.5 53%BTTS 56%
Гана
1-1▼
46%
25%
28%
xG 1.6 – 1.2O2.5 51%BTTS 55%
Иордания
1-1▼
46%
26%
29%
xG 1.5 – 1.1O2.5 48%BTTS 51%
Австралия
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%
Новая Зеландия
1-1▼
45%
26%
29%
xG 1.5 – 1.2O2.5 50%BTTS 54%
Панама
1-1▼
45%
26%
30%
xG 1.5 – 1.1O2.5 48%BTTS 52%
Мексика
1-1▼
45%
25%
30%
xG 1.6 – 1.2O2.5 53%BTTS 57%
Саудовская Аравия
1-1▼
45%
26%
30%
xG 1.5 – 1.1O2.5 49%BTTS 53%
Алжир
1-1▼
45%
24%
31%
xG 1.6 – 1.3O2.5 54%BTTS 57%
Канада
1-1▼
43%
25%
32%
xG 1.4 – 1.2O2.5 49%BTTS 53%
Норвегия
1-1▼
43%
25%
32%
xG 1.5 – 1.3O2.5 54%BTTS 57%
Эквадор
1-1▼
43%
27%
31%
xG 1.4 – 1.1O2.5 46%BTTS 52%
ДР Конго
1-1▼
43%
25%
32%
xG 1.4 – 1.2O2.5 50%BTTS 54%
Парагвай
1-1▼
42%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 53%
Шотландия
1-1▼
42%
25%
32%
xG 1.5 – 1.3O2.5 52%BTTS 55%
Колумбия
1-1▼
42%
26%
33%
xG 1.4 – 1.3O2.5 50%BTTS 54%
Марокко
1-1▼
41%
26%
33%
xG 1.4 – 1.2O2.5 49%BTTS 54%
США
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%
Швейцария
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%
Япония
1-1▼
40%
26%
34%
xG 1.4 – 1.3O2.5 51%BTTS 55%
Швеция
1-1▼
40%
26%
34%
xG 1.4 – 1.2O2.5 48%BTTS 52%
Австрия
1-1▼
39%
26%
35%
xG 1.3 – 1.3O2.5 48%BTTS 53%
Бельгия
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 50%BTTS 53%
Бразилия
1-1▼
38%
25%
37%
xG 1.4 – 1.4O2.5 51%BTTS 55%
Сенегал
1-1▼
37%
26%
36%
xG 1.3 – 1.3O2.5 49%BTTS 53%
Нидерланды
1-1▼
37%
26%
37%
xG 1.4 – 1.4O2.5 51%BTTS 56%
Турция
1-1▼
37%
26%
37%
xG 1.3 – 1.3O2.5 49%BTTS 54%
Уругвай
1-1▼
37%
26%
37%
xG 1.3 – 1.3O2.5 47%BTTS 53%
Аргентина
1-1▼
36%
26%
39%
xG 1.3 – 1.4O2.5 51%BTTS 56%
Испания
1-1▼
35%
25%
39%
xG 1.3 – 1.4O2.5 51%BTTS 55%
Германия
1-1▼
35%
26%
39%
xG 1.3 – 1.4O2.5 51%BTTS 55%
Португалия
1-1▼
34%
26%
40%
xG 1.2 – 1.4O2.5 48%BTTS 53%
Англия
1-1▼
34%
26%
40%
xG 1.3 – 1.4O2.5 50%BTTS 54%
Франция
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
