With just two weeks until the opening of the 2026 World Cup in the United States, Canada, and Mexico, Goldman Sachs has released a new research report titled "The World Cup and Economics." The report uses a statistically validated historical model to simulate 50,000 complete World Cup tournaments, providing what is described as the most rigorous prediction to date for the probability of winning and the likely tournament progression.

The Foundation of the Prediction Model

Goldman Sachs' forecasting model is not arbitrary; it is built on historical data from nearly 20,000 international 'A' matches since 1978. The core logic uses a Poisson distribution to predict the number of goals in each match, a statistical method commonly used for describing the frequency of rare events, which fits the nature of scoring in football. The most critical factor determining a team's goal-scoring ability is its Elo rating. This system, originally designed for chess rankings, has become one of the most authoritative indicators for measuring national team strength. It considers not just win-loss results but dynamically adjusts points based on the strength of the opponent. At the time of the report's release, Spain held a commanding lead at the top of the Elo rankings, 52 points ahead of second-place Argentina and 84 points ahead of third-place France, forming the primary basis for Goldman's bullish outlook on Spain.

However, football is more than just numbers. Goldman enhanced its base model with five key variable categories to make predictions more realistic:

1. Scoring Talent: The model incorporates data on top scorers from each nation playing in Europe's elite leagues and continental cup competitions, as this metric has a significant positive correlation with a team's World Cup scoring efficiency. To reflect practical patterns, the model caps the number of elite attacking players counted per team at four, based on the typical threshold for core attacking personnel in a World Cup squad, not limited solely to strikers.

2. Team Momentum: Teams in strong recent form often carry that momentum into the World Cup. This is measured by a team's goals in its last 10 matches and the goals conceded by its opponents in their last 5 matches.

3. Psychological Factors: This is one of the most intriguing variables. Goldman highlights the classic "champion's curse": since 1978, no team has successfully defended its World Cup title, with defending champions often underperforming. Examples include France in 2002, Spain in 2010, Germany in 2014, and Germany in 2018 all exiting in the group stage, and Italy in 2006 bowing out in the round of 16. The sole recent exception is France, the 2018 champion, which reached the final as runner-up in 2022, making it the best-performing defending champion in over four decades. Additionally, debutant nations often play fearlessly and exceed expectations. Under similar strength conditions, European teams tend to have stronger tactical discipline and defensive resilience, making them harder to score against.

4. Geographical Factors: Home advantage remains significant in the World Cup. More importantly, altitude and temperature variations can substantially impact team performance. This is particularly crucial for the 2026 tournament, as several venues in Mexico are located over 2,000 meters above sea level, posing a major challenge for teams accustomed to lower altitudes.

5. Historical "Powerhouse" Adjustment: The model also accounts for a "football powerhouse" boost, with one notable exception: England. Historical data shows England consistently underperforms relative to its Elo rating in World Cups, and this "England curse" is factored into the model.

The Rankings for Lifting the Trophy

Using this comprehensive model, Goldman conducted 50,000 Monte Carlo simulations to determine each team's probability of winning the tournament.

Spain leads with a 25.7% chance of victory, nearly one in four. This means if the World Cup were replayed 50,000 times, Spain would win approximately 12,850 times. Spain's advantage is comprehensive: the highest Elo rating, elite scoring talent, strong pre-tournament momentum, and a relatively favorable group and path.

France ranks second with an 18.9% probability. As the 2018 champion and 2022 runner-up, France boasts arguably the world's most luxurious attacking lineup, ranking first in the scoring talent metric. However, France's path is problematic; the model predicts a likely semi-final clash with top favorite Spain, significantly reducing their chances.

Defending champion Argentina ranks third with a 14.3% probability. While Argentina has the second-highest Elo rating and benefits from a strong tournament pedigree, the "champion's curse" casts a shadow. Yet, Argentina holds a major advantage: they are projected to avoid Spain until the final, the most favorable path among the top contenders.

Brazil places fourth with a 7.6% chance. The footballing nation is always a contender, but its performance in recent major tournaments has been inconsistent, and its Elo rating now trails Spain, Argentina, and France.

The Netherlands ranks fifth at 5.2%, with England close behind at 5.0% in sixth. England's case is peculiar. Despite a consistently high Elo rating, its World Cup potential is heavily dragged down by historical underperformance, the geographical disadvantage of high-altitude matches in Mexico City, and a less-than-ideal draw.

Following are Portugal (4.8%), Germany (4.5%), Colombia (2.2%), and Croatia (1.7%). The combined probability for all other teams is less than 10%, indicating the champion will almost certainly come from the traditional football powers.

Projecting the Tournament's Path

Goldman's model also projects the most likely specific tournament progression.

In the group stage, with 48 teams in 12 groups, the top two from each group and the eight best third-placed teams advance to the 32-team knockout stage. The model predicts all traditional powers and the three host nations—the United States, Canada, and Mexico—will progress. Groups D (USA, Australia, Turkey, Paraguay) and G (Belgium, Iran, New Zealand, Egypt) are projected to be the most competitive, with all four teams closely matched, potentially leading to draws in all matches.

The round of 32 could feature notable clashes like USA vs. Iran and Argentina vs. Uruguay. However, the first heavyweight duel is forecast for the round of 16, where Germany is predicted to be eliminated by France.

The quarter-finals are projected to deliver the tournament's most thrilling matches: England vs. Brazil and Argentina vs. Portugal. The latter could be the final World Cup showdown between the legendary duo, Lionel Messi and Cristiano Ronaldo. The model predicts victories for Brazil and Argentina, sending them to the semi-finals.

The semi-final matchups are projected to be France vs. Spain and Brazil vs. Argentina. These four teams are the top four in the probability rankings, representing the pinnacle of European and South American football. Goldman predicts Spain and Argentina will win their respective matches to meet in the final on July 19th in New York.

Ultimately, the model predicts Spain will defeat Argentina to claim its second World Cup title. This final would see a 39-year-old Lionel Messi, playing in his final World Cup, face a Spanish side featuring teenage superstar Lamine Yamal, who would turn 19 just before the final. This clash is viewed as a symbolic passing of the torch, marking the end of the Messi era and the rise of a new generation.

This prediction aligns with the historical pattern of continental alternation in champions. Since Brazil's back-to-back wins in 1962, the World Cup winner has largely alternated between Europe and South America. With Argentina (South America) winning in 2022, historical trends suggest the 2026 champion is likely to be European again, fitting perfectly with Spain's projected victory.

The Limits of Prediction and Football's Unpredictable Charm

Any predictive model has limitations, and Goldman Sachs acknowledges its own.

First is the luck of the draw. Through random group simulations, Goldman found Germany to be the most unfortunate team in this draw, likely facing France in the round of 16, while Argentina is the luckiest, avoiding top favorite Spain until the final.

Second, the model cannot account for some non-attacking factors, such as the midfield and wing depth of teams like France and Portugal, or the role of an excellent goalkeeper in penalty shootouts. Injuries are a major unknown, such as whether a pre-tournament injury to Spain's key player Yamal would affect his form.

Furthermore, individual player form, fatigue from club competitions, and other soft factors like managerial skill, in-game tactical adjustments, and team chemistry are not quantified in the model. For instance, the physical toll on French internationals who played deep into the Champions League with Paris Saint-Germain is hard to measure. The ability of veterans like Messi and Ronaldo, now away from Europe's top leagues, to withstand World Cup intensity at an advanced age is uncertain.

Finally, Goldman compared its predictions with betting market odds. The comparison shows Goldman is more bullish on Spain and Argentina than the market, while its expectations for England and Portugal are below market averages.

Despite these limitations, this Goldman Sachs model has demonstrated impressive accuracy in past World Cups. Before the 2022 Qatar World Cup, it predicted Brazil (24%), Argentina (21%), and France (19%) as top contenders, closely aligning with market odds and the eventual tournament outcome. Data shows that since 1978, the model's predicted team goal differential has a 49% correlation with actual match results—a remarkably high quantitative accuracy rate for the unpredictable nature of football.

Goldman Sachs stated it will update the model after each match day during the World Cup, adjusting teams' Elo ratings and momentum variables based on actual results to provide updated winning probabilities and tournament predictions.