Group
Finish probabilities
1st 0%
2nd 100.0%
3rd place 0% Qualified from 3rd 0% Eliminated in 3rd 0%
4th 0%
Group L
Croatia
A nation of four million people that keeps reaching World Cup semifinals through sheer tactical intelligence and tournament experience. Modrić will be 41, which is either a liability or the most interesting subplot of the tournament. The midfield that defined a generation is aging in real time but nobody has told them they are supposed to stop.
This profile summarizes Croatia's World Cup 2026 simulation path from the latest current-state snapshot: group outlook, finish distribution, possible knockout routes, opponent pools, squad context, and title chances.
Champion 0%
Group qualification 100%
Squad market €385.7m
Avg age 28.3
Avg height 1.853m
Manager Zlatko Dalić
Saved forecast
Forecast as of Updated 2026-07-15 10:47 UTC, based on 10k current-state simulations. Tournament-wide official results included at that time stay fixed. These are saved model forecasts, not the team's actual final finish.
Tournament-wide result cutoff: France 0-2 Spain
Guide
How to read this team profile
Group finish and stage reach values are model outputs from the latest current-state team profile snapshot. Path sections show how Croatia's route changes by group finish, while opponent pools summarize the teams most often appearing in each round.
Other forecast pages may show a different saved date. The interactive replay simulator is separate: changing Team Strength settings creates alternative scenarios without changing this saved forecast.
Tournament
Stage reach probabilities
R32 100%
R16 0%
QF 0%
SF 0%
Final 0%
Champion 0%
Conditional path
Path by group finish
| Finish | Sample | R32 | R16 | QF | SF | Final | Champion |
|---|---|---|---|---|---|---|---|
| 1st | No longer possible | — | — | — | — | — | — |
| 2nd | 100.0% | 100% | 0% | 0% | 0% | 0% | 0% |
| 3rd | No longer possible | — | — | — | — | — | — |
Overall opponent pool
Most common opponents by round
If reached shows the opponent split among this team's matches in that round. All sims shows how often the matchup happens across the latest current-state snapshot.
R32
Portugal If reached: 100.0% All sims: 100.0% Matchup win rate: 0% R16
No opponent samples.
QF
No opponent samples.
SF
No opponent samples.
Final
No opponent samples.
Finish 1st
Croatia opponent pool if finishing 1st
For group-finish paths, If reached is measured within that finish path and round. Path sims shows how often the matchup happens among simulations where the team finished in that position.
R32
No longer possible
R16
No longer possible
QF
No longer possible
SF
No longer possible
Final
No longer possible
Finish 2nd
Croatia opponent pool if finishing 2nd
For group-finish paths, If reached is measured within that finish path and round. Path sims shows how often the matchup happens among simulations where the team finished in that position.
R32
Portugal If reached: 100.0% Path sims: 100.0% Matchup win rate: 0% R16
Not enough samples.
QF
Not enough samples.
SF
Not enough samples.
Final
Not enough samples.
Finish 3rd
Croatia opponent pool if finishing 3rd
For group-finish paths, If reached is measured within that finish path and round. Path sims shows how often the matchup happens among simulations where the team finished in that position.
R32
No longer possible
R16
No longer possible
QF
No longer possible
SF
No longer possible
Final
No longer possible
Squad
Current squad
| Pos | Player | Age | Height | Club | Market |
|---|---|---|---|---|---|
| GK | Dominik Kotarski | 26 | 1,89m | FC Copenhagen | €5.0m |
| GK | Ivor Pandur | 26 | 1,87m | Hull City | €4.5m |
| GK | Dominik Livakovic | 31 | 1,88m | GNK Dinamo Zagreb | €4.0m |
| CB | Joško Gvardiol | 23 | 1,85m | Manchester City | €70.0m |
| CB | Luka Vuskovic | 19 | 1,93m | Hamburger SV | €60.0m |
| CB | Josip Sutalo | 26 | 1,90m | Ajax Amsterdam | €12.0m |
| CB | Marin Pongracic | 28 | 1,93m | ACF Fiorentina | €6.5m |
| CB | Martin Erlić | 28 | 1,93m | FC Midtjylland | €4.0m |
| CB | Duje Caleta-Car | 29 | 1,93m | Real Sociedad | €3.0m |
| RB | Josip Stanisic | 26 | 1,87m | Bayern Munich | €40.0m |
| DM | Nikola Moro | 28 | 1,84m | Bologna FC 1909 | €7.0m |
| DM | Kristijan Jakic | 29 | 1,84m | FC Augsburg | €6.0m |
| CM | Petar Sučić | 22 | 1,83m | Inter Milan | €40.0m |
| CM | Mateo Kovacic | 32 | 1,82m | Manchester City | €12.0m |
| CM | Luka Sucic | 23 | 1,85m | Real Sociedad | €10.0m |
| CM | Mario Pašalić | 31 | 1,88m | Atalanta BC | €6.0m |
| CM | Luka Modrić | 40 | 1,72m | AC Milan | €3.5m |
| AM | Martin Baturina | 23 | 1,72m | Como 1907 | €30.0m |
| AM | Nikola Vlašić | 29 | 1,79m | Torino FC | €10.0m |
| AM | Toni Fruk | 25 | 1,78m | HNK Rijeka | €9.0m |
| AM | Andrej Kramaric | 34 | 1,77m | TSG 1899 Hoffenheim | €2.5m |
| LW | Ivan Perišić | 37 | 1,86m | PSV Eindhoven | €1.2m |
| RW | Marco Pašalić | 25 | 1,77m | Orlando City SC | €6.5m |
| CF | Igor Matanovic | 23 | 1,94m | SC Freiburg | €22.0m |
| CF | Petar Musa | 28 | 1,88m | FC Dallas | €8.0m |
| CF | Ante Budimir | 34 | 1,90m | CA Osasuna | €3.0m |
Important note: This simulator and its probability outputs are for entertainment and informational purposes only. They are not investment advice, financial advice, or a recommendation to buy, sell, trade, or participate in any prediction market. No real-money contest is offered or accepted on baplab.