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OddsFlow AI Analysis
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53종합
⚡공격
53
⚙️미드필드
58
🛡️수비
48
🧤골키핑
51
26인원
27.4평균 나이
63.2평균 OVR
87.0깊이 점수
53종합
⚡공격
53
⚙️미드필드
58
🛡️수비
48
🧤골키핑
51
26인원
27.4평균 나이
63.2평균 OVR
87.0깊이 점수
스타 선수
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)
대회 확률
4.1%우승우승
4.1%
85.2%조별 통과조별 통과
85.2%
강점
Prolific attack
Deep squad rotation
약점
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
퀴라소
1-0▼
56%
24%
20%
xG 1.6 – 0.8O2.5 45%BTTS 46%
이라크
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%
우즈베키스탄
0-1▼
55%
25%
21%
xG 1.6 – 0.9O2.5 45%BTTS 47%
남아프리카
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%
아이티
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%
이집트
1-1▼
51%
24%
25%
xG 1.7 – 1.2O2.5 56%BTTS 57%
카타르
1-1▼
48%
25%
27%
xG 1.6 – 1.1O2.5 50%BTTS 53%
튀니지
1-1▼
47%
25%
28%
xG 1.6 – 1.2O2.5 53%BTTS 56%
가나
1-1▼
46%
25%
28%
xG 1.6 – 1.2O2.5 51%BTTS 55%
요르단
1-1▼
46%
26%
29%
xG 1.5 – 1.1O2.5 48%BTTS 51%
호주
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%
뉴질랜드
1-1▼
45%
26%
29%
xG 1.5 – 1.2O2.5 50%BTTS 54%
파나마
1-1▼
45%
26%
30%
xG 1.5 – 1.1O2.5 48%BTTS 52%
멕시코
1-1▼
45%
25%
30%
xG 1.6 – 1.2O2.5 53%BTTS 57%
사우디아라비아
1-1▼
45%
26%
30%
xG 1.5 – 1.1O2.5 49%BTTS 53%
알제리
1-1▼
45%
24%
31%
xG 1.6 – 1.3O2.5 54%BTTS 57%
캐나다
1-1▼
43%
25%
32%
xG 1.4 – 1.2O2.5 49%BTTS 53%
노르웨이
1-1▼
43%
25%
32%
xG 1.5 – 1.3O2.5 54%BTTS 57%
에콰도르
1-1▼
43%
27%
31%
xG 1.4 – 1.1O2.5 46%BTTS 52%
콩고민주공화국
1-1▼
43%
25%
32%
xG 1.4 – 1.2O2.5 50%BTTS 54%
파라과이
1-1▼
42%
26%
31%
xG 1.4 – 1.2O2.5 48%BTTS 53%
스코틀랜드
1-1▼
42%
25%
32%
xG 1.5 – 1.3O2.5 52%BTTS 55%
콜롬비아
1-1▼
42%
26%
33%
xG 1.4 – 1.3O2.5 50%BTTS 54%
모로코
1-1▼
41%
26%
33%
xG 1.4 – 1.2O2.5 49%BTTS 54%
미국
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%
스위스
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%
일본
1-1▼
40%
26%
34%
xG 1.4 – 1.3O2.5 51%BTTS 55%
스웨덴
1-1▼
40%
26%
34%
xG 1.4 – 1.2O2.5 48%BTTS 52%
오스트리아
1-1▼
39%
26%
35%
xG 1.3 – 1.3O2.5 48%BTTS 53%
벨기에
1-1▼
38%
26%
36%
xG 1.3 – 1.3O2.5 50%BTTS 53%
브라질
1-1▼
38%
25%
37%
xG 1.4 – 1.4O2.5 51%BTTS 55%
세네갈
1-1▼
37%
26%
36%
xG 1.3 – 1.3O2.5 49%BTTS 53%
네덜란드
1-1▼
37%
26%
37%
xG 1.4 – 1.4O2.5 51%BTTS 56%
튀르키예
1-1▼
37%
26%
37%
xG 1.3 – 1.3O2.5 49%BTTS 54%
우루과이
1-1▼
37%
26%
37%
xG 1.3 – 1.3O2.5 47%BTTS 53%
아르헨티나
1-1▼
36%
26%
39%
xG 1.3 – 1.4O2.5 51%BTTS 56%
스페인
1-1▼
35%
25%
39%
xG 1.3 – 1.4O2.5 51%BTTS 55%
독일
1-1▼
35%
26%
39%
xG 1.3 – 1.4O2.5 51%BTTS 55%
포르투갈
1-1▼
34%
26%
40%
xG 1.2 – 1.4O2.5 48%BTTS 53%
잉글랜드
1-1▼
34%
26%
40%
xG 1.3 – 1.4O2.5 50%BTTS 54%
프랑스
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
