Panama vs Croatia Prediction, Tips & Odds
Panama vs Croatia
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About the Teams
Panama
Croatia
Head to Head
Last 1 Meetings (Croatia leads 1-0)In their only competitive meeting, Croatia won against Panama, with a score of 1-0 in the 2026 World Cup. This single match provides a very limited sample size, making it difficult to draw any significant conclusions about the head-to-head dynamics between these two teams. As there are no previous encounters or friendly matches to consider, the result from 2026 stands alone and does not indicate any patterns or trends in their historical matchups.
Group L Standings
| # | Team | P | W | D | L | GF | GA | GD | Pts | Form |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 |  England | 3 | 2 | 1 | 0 | 6 | 2 | 4 | 7 | WWWWD |
| 2 | Croatia | 3 | 2 | 0 | 1 | 5 | 5 | 0 | 6 | LWWL |
| 3 |  Ghana | 3 | 1 | 1 | 1 | 2 | 2 | 0 | 4 | LLDW |
| 4 | Panama | 3 | 0 | 0 | 3 | 0 | 4 | -4 | 0 | LLL |
Key Statistics
2026 SeasonPanama have won 2 of their last 5 matches at home, drawing 2 and losing 1. Their recent home fixtures include drew with Bosnia & Herzegovina 1-1 and beat Dominican Republic 4-2.
Overall, the wider run reads 5W 4D 2L from the last 11 games (45% win rate). The form line is positive, with more wins than defeats across the sample.
Croatia have won 3 of their last 5 matches on the road, drawing 1 and losing 1. Their recent away fixtures include lost to Brazil 3-1 and beat Colombia 1-2.
Overall, the wider run reads 9W 1D 2L from the last 12 games (75% win rate). The form line is positive, with more wins than defeats across the sample.
Lineups
Lineups not yet announced
National-team lineups are genuinely unknown until 1 to 2 hours before kick-off, when the starting XI is confirmed. Until then, only predicted lineups are possible.
Unavailable Players
0 playersPanama
No reported absences
Croatia
No reported absences
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The Daily Punt Analysis Team
Data-driven football predictions and betting tips
This forecast was built by the The Daily Punt Analysis Team using our in-house statistical model. We feed the model two layers of data for every fixture: each team's recent expected goals (xG and xGA) and their actual goals scored and conceded, split by home and away venue. The numbers run through a Poisson distribution that produces a probability for every possible scoreline, which we then convert into fair odds for the 1X2, correct score, BTTS, and over/under markets.
Before publishing, we cross-check our fair odds against the prices the major bookmakers are offering. Where a bookmaker is paying out more than our model says is mathematically fair, we flag that market as a value bet on this page.
