World Cup 2026: Data Scientist’s Model Simulates Tournament, Reveals Team Chances

The upcoming 2026 FIFA World Cup, set to be the most expansive in history with 48 teams, has spurred keen interest in predicting its outcome. A data scientist has created a sophisticated mathematical model, running one million simulations, to forecast how the tournament might unfold and assess the progression chances for every participating nation.

The 2026 tournament marks a significant departure from previous formats. FIFA has expanded the competition from 32 to 48 teams, restructuring it into 12 groups of four. This change introduces new progression rules, an additional knockout round, and a substantial increase in the total number of matches from 64 to 104. FIFA’s stated aims for this expansion include fostering greater global participation, maximizing revenue, and boosting football’s popularity in emerging markets.

Historical Dominance and New Realities

Analyzing the past seven 32-team tournaments since 1998, a clear pattern of dominance emerges. Out of 28 semi-final spots, six nations—Argentina, the Netherlands, Brazil, Croatia, France, and Germany—accounted for multiple appearances. When including previous winners like England, Italy, and Spain, 78.6% of modern semi-finalists hailed from just nine nations. Furthermore, all 14 finalists in this period originated from this elite group, with the last finalist outside this set dating back to 1962. This level of dominance is striking given that FIFA officially ranks 211 nations.

However, the expanded 2026 format presents challenges for straightforward prediction. The increased number of teams and the altered progression pathways necessitate a more analytical approach. The simulation study undertaken by the data scientist aims to quantify the progression probabilities for all 48 teams. While identifying a likely winner is a key objective, the model also sheds light on how the new format distributes these chances across the field and influences the prospects of traditionally strong nations.

Model Predictions for Select Teams

The simulation results offer intriguing insights into the likelihood of teams advancing through the tournament stages. For instance, Australia is predicted to have a 67.1% chance of advancing from their group, a 31.3% chance of winning their first knockout match, but only a 1.0% chance of reaching the final and a 0.3% chance of winning the tournament.

Canada, as a host nation, benefits from a projected 78.9% chance of progressing from their group. They have a 37.9% chance of winning their first knockout fixture but face long odds for a deeper run, with just a 1.0% probability of making the final and a 0.3% chance of lifting the trophy. New Zealand faces a more challenging outlook, with minimal chances of winning the tournament and only a 19.5% probability of advancing from their group.

England, despite being among the top contenders, sees its chances influenced by recent form. A loss to Japan in March has reportedly lowered their standing. The model identifies Spain (15.8%), France (15.6%), Argentina (15.3%), and England (11.0%) as the only teams with more than a 10% chance of winning the trophy. These four nations are all part of the historical “group of nine” and are currently the top four rated sides.

Impact of the New Format

The simulation suggests that the new 48-team format does reduce the dominance of historically strong nations, though perhaps not as dramatically as some might have expected. The estimated proportion of semi-final spots taken by the traditional nine powerhouses is 54.2%, a notable decrease from the historical 78.6%. Similarly, the estimated proportion of finalists from this group falls to 63.6%, and the probability of the champion emerging from this cohort is 72.6%. These figures are down from the historical 100% in both categories. The absence of Italy from the tournament also plays a role in these adjusted probabilities.

The data scientist notes that a different format change, such as increasing group sizes to six teams instead of adding more groups, would have more significantly spread the chances of progression. However, such a change would have necessitated at least 136 matches.

Key facts
| Team | Group Stage Advance (%) | First Knockout Win (%) | Final Appearance (%) | Tournament Win (%) |
|—————–|————————-|————————|———————-|——————–|
| Australia | 67.1 | 31.3 | 1.0 | 0.3 |
| Canada | 78.9 | 37.9 | 1.0 | 0.3 |
| New Zealand | 19.5 | N/A | N/A | Minimal |
| Spain | N/A | N/A | N/A | 15.8 |
| France | N/A | N/A | N/A | 15.6 |
| Argentina | N/A | N/A | N/A | 15.3 |
| England | N/A | N/A | N/A | 11.0 |

The development of such data-driven models provides football fans and analysts with a quantitative perspective on the unpredictable nature of the World Cup. As the tournament approaches, these simulations offer a valuable, albeit probabilistic, glimpse into potential outcomes, highlighting how the expanded format reshapes traditional expectations.

Source: https://theconversation.com/i-built-a-maths-model-to-simulate-the-world-cup-a-million-times-find-out-your-teams-chances-276386

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