The official match ball of the 2026 FIFA World Cup (© Smart Future - stock.adobe.com)
In A Nutshell
- Spain, France, Argentina, and England are the only teams given more than a 10% chance of winning the World Cup, with Spain narrowly projected as the favorite at 15.8%.
- The expanded 48-team format still leaves traditional powerhouses dominant, though the “group of nine” now accounts for a smaller share of semifinalists and champions than in past tournaments.
- Australia and Canada can make the knockout rounds with decent odds, but their title chances are still tiny, while New Zealand is projected to go very deep only rarely.
The 2026 FIFA World Cup is one of the most-watched events of the international sports calendar, and fans from across the globe will be trying to predict how far their team will go.
I’m a data scientist and in an attempt to forecast the eventual tournament winner, semi-finalists and teams’ chances of progressing through the group stages, I built a model to predict how the World Cup may unfold.
Here’s how I did it and what my model predicted.
Lessons From Recent History
For this World Cup, the traditional 32-team tournament structure (eight groups of four) has been expanded to a bulging 48 teams (12 groups of four), with new progression rules, an extra knock-out round and a rise in total matches from 64 to 104.
The changes were designed by FIFA primarily to increase global participation, maximize revenue through more matches, and boost the popularity of soccer in new markets.
In trying to predict the 2026 event, what can recent history teach us?
Looking back to the seven 32-team tournaments since 1998, the 28 semi-final spots have been dominated by six nations who reached that stage more than once: Argentina (2), the Netherlands (3), Brazil (3), Croatia (3), France (4), and Germany (4).
If we include previous tournament winners (England, Italy, and Spain), 78.6% of the modern semi-finalists have come from nine nations.
Further, all 14 finalists were from this group – the last finalist from outside these nine came in 1962 (Czechoslovakia); the last winner was in 1950 (Uruguay).
This is an amazing degree of dominance given the number of international teams playing the game – official FIFA rankings currently list 211 nations.
More teams at the 2026 event, though, means it is harder to accurately assess the likelihood of tournament results.
For this, we need analytics, and I’ve undertaken a simulation study designed to calculate the progression chances of all 48 teams in the field.
While the obvious outcome of such a study is to assess who the likely winners are, we can also gain insight from how the new format spreads these chances across the teams and how it affects the chances of the top sides raising the trophy.
The Maths Involved
The core of any tournament simulation is its determination of outcome probabilities for individual matches. While there are many possibilities, I adopted FIFA’s Elo-based rating system.
An Elo rating system is a statistical method for measuring the skill levels of players or teams, based on match outcomes and opponent strength.
Conveniently, this allows for updating ratings after each match and provides a formula for expected results between teams with any given rating difference.
The formula I used was:
E(D) = 1 / (10–D/600 + 1)
This tells us the chance of a team winning a match (E) based on the difference between their Elo rating and their opponent’s rating (D). The result will be a number between 0 and 1, where 0 means a certain loss and 1 means a guaranteed victory.
However, to simulate the full tournament, we must contend with progression from the group stage, where goal differential is often a crucial component. To do this, I’ve leant on research by German researcher Patrick Heuer and his colleagues, who provide a goal difference distribution which can be used as a starting point for our simulations. I standardized their distribution as:
To adapt it for games between opponents with a given ratings difference, an exponential tilt is employed:
pD(d) ∝ p(d) × 100.365 × d × (D/600)
This means we take the symmetrical distribution shown above and multiply it by another factor which depends on both the goal difference (d) and the difference in Elo ratings (D).
The factor 0.365 is chosen to match as closely as possible the expected outcome based on this tilted goal difference distribution to the one determined by the Elo rating.
What did it predict?
Each team’s chances of reaching each round, based on one million simulations, are shown in the below table.
It predicts Australia is a 67.1% chance of getting out of their group, a 31.3% chance of getting past their first knock-out match, but is just a 1.0% chance of making the final and 0.3% chance of winning.
Canada’s chances are quite similar: a 78.9% chance of making out of their group (thanks to being a host nation), a 37.9% chance of getting past their first knock-out but just a 1.0% chance of making the final and 0.3% chance of winning.
New Zealand, on the other hand, has basically no chance of winning and only a 19.5% chance of making it out of their group.
Lastly, while England has the fourth-highest overall chance of winning, it is notably lower than the other three favorites. This is at least in part due to their recent drop in rating after a loss to Japan in March.
The only teams with more than a 10% chance of winning the trophy are Spain (15.8%), France (15.6%), Argentina (15.3%) and England (11.0%) – all members of the “group of nine” and the current top four-rated sides.
But the estimated proportion of semi-final spots taken by these nine nations is 54.2% – notably lower than the historical 78.6%.
Further, the estimated proportion of finalists to come from these nine nations is 63.6%, while there is a 72.6% chance the champion comes from this group, both down from the historical 100% values. Of course, this is partly due to Italy’s failure to make the World Cup.
So, FIFA’s new format does reduce the chances of the historically strong nations progressing far into the tournament but not as much as they may have hoped.
Had FIFA increased the size of the groups to six teams, instead of increasing the number of groups, the new format would have done more to spread the chances – but doing so would have required at least 136 matches.
I would like to thank Dr Chris Bilson and Noah Stern for their help in producing this article. In addition, I would like to thank a very careful reviewer for important comments.
Steven Stern is a Professor of Data Science at Bond University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
