It is hard to believe, but it is already time for another Euros Football Tournament. So, who are the favorites and dark horses heading into Euro 2024? We performed a statistical simulation to find out. France leads the way as a strong favorite, followed by Germany and England. Portugal and Spain round out the top 5.
Check out our interactive visualization and explore every team’s chances of advancing past the group stage and eventually winning the tournament. We will update the probabilities after each game.
The Euros have been dominated by Germany, Spain and France who have half of the tournaments to date. This year portends to be no different, as our model gives > 50% that one of those three countries will lift the trophy in Berlin on July 14. Still, there are a number of intriguing storylines. Can England capitalize on their strong team to finally win a Euros? Can Cristiano Ronaldo win one more tournament in what is likely his final Euros? Can a team such as the Netherlands or Belgium spring a surprise?
Who is most likely to win the Euros?
According to our forecasting model, France is a big favorite with a 26% chance of winning and a 40% chance of reaching the final. Still, France’s path in the later stages may be tricky. If our projections hold, they will face a Belgian team that has transitioned away from a number of their core players from 2016-2022 that reached two quarterfinals and the semifinals of the 2018 World Cup. If they get past Belgium, a very strong England is expected to await them in the semifinals.
| Chance of reaching round | ||||||
|---|---|---|---|---|---|---|
| # | Team | 1/16 | 1/8 | 1/4 | Final | Win ▼ |
| 1 |  | 97% | 80% | 59% | 40% | 26% |
| 2 |  | 95% | 69% | 46% | 28% | 16% |
| 3 |  | 96% | 71% | 46% | 27% | 15% |
| 4 |  | 97% | 63% | 40% | 21% | 11% |
| 5 |  | 91% | 66% | 37% | 20% | 10% |
| 6 |  | 85% | 57% | 31% | 15% | 6% |
| 7 |  | 94% | 56% | 26% | 12% | 6% |
| 8 |  | 79% | 47% | 22% | 9% | 3% |
Germany have a 16% chance of winning. While Germany’s form suffered after the 2022 World Cup, they still have beaten favorite France twice in the past year. Moreover, they hope to capitalize on the strong home support. Our model includes a home advantage for Germany. Perhaps our model is too aggressive in this regard, as including the advantage increases Germany’s chance of winning by 10 percentage points. They are arguably on the harder side of the knockout draw, with Spain, Portugal, and the Netherlands.
England enter the tournament hoping to capture their first tournament title since their 1966 World Cup victory. They are loaded with attacking talent with Phil Foden having won EPL Player of the Season, Harry Kane leading the Bundesliga in scoring, and Jude Bellingham playing a key role in Real Madrid winning LaLiga and the Champions League. The question is whether the defense can hold up. Reaching the semifinals will likely require overcoming Italy, who beat them in the finals of Euro 2020.
Portugal has an 11% chance of winning. They feature a team with the experience of Ronaldo, Pepe, Bruno Fernandes, and Bernando Silva. They are complemented by prime-age players (Ruben Dias, Rafael Leao, Diogo Jota) and emerging talents such as Goncalo Ramos and Nuno Mendes.
Finally, Spain has a 10% chance of winning. They won the Nations League last year but are in a tricky group with Croatia and Italy. They are also less experienced with only Alvaro Morato, Rodri, and the 38-year-old Jesus Navas being capped at least 50 times.
Beyond these teams, we project the tournament to be top-heavy this year. Only Belgium (6%), the Netherlands (6%), Italy (3%) and Croatia (2%) have a greater than 1% chance of winning according to our simulations.
How our predictions work
At the core of our forecasts are three rating systems that estimate a team’s strength:
- First, we compute an Elo rating from recent international match results. These results are weighted for the recentness of the game, the importance of the competition and the strength of the opponent. We are more aggressive in our updates than most traditional Elo rating systems. Therefore, our ratings reflect a team’s recent form, rather than its longer-term evolution.
- Second, we assign individual offensive and defensive ratings to each team based on a team’s goals scored and conceded in recent games. Although offense and defense aren’t easy to separate in a fluid sport such as soccer, there are some useful reasons to handle things in this way. First, an important factor in soccer games is the playing style of both teams and the balance between offense and defense. This difference in playing style might be an important factor in deciding the game’s final outcome. For example, a game between two teams known to rely on a very strong defense might have a higher probability of ending up in a draw than a game between two teams that are known to play very offensively. Second, defensive ratings tend to have a little more predictive power than offensive ratings in games against elite competition, which will be the case for most key matches that will be played at the EUROs.
- Finally, we include the cumulative market value of each nation’s starting XI as estimated by the website Transfermarkt. Because most teams still have to make their final list of players official, we relied on recent matches and some of our soccer knowledge to come up with the most likely lineups for these teams. We preprocessed the data to overcome issues such as market inflation or missing values.
Below we show these ratings for each team. Because all of these rating systems are on different scales, we’ve standardized them to make them comparable to one another. Positive scores are associated with above-average teams according to the rating system in question. For instance, a score of +1 represents a team that’s one standard deviation better than the mean. Negative scores are associated with below-average teams.
| Team ratings | |||||
|---|---|---|---|---|---|
| # | Team | ELO | Off | Def | MV |
| 1 | | 2.09 | 2.01 | 1.27 | 2.01 |
| 2 | | 0.88 | 1.31 | 0.67 | 0.95 |
| 3 | | 1.48 | 1.35 | 1.16 | 2.72 |
| 4 | | 1.37 | 1.44 | 1.18 | 1.46 |
| 5 | | 1.36 | 1.45 | 1.15 | 1.15 |
| 6 | | 0.93 | 0.88 | 1.08 | 0.59 |
| 7 | | 0.98 | 1.18 | 1.08 | 0.18 |
| 8 | | 0.68 | 0.47 | 0.65 | 0.52 |
| 9 |  | 0.33 | -0.18 | 0.93 | -0.36 |
| 10 |  | -0.11 | -0.73 | -0.48 | -0.21 |
| 11 |  | 0.06 | -0.23 | -0.32 | -0.66 |
| 12 |  | -0.29 | -0.52 | -0.47 | -0.70 |
| 13 |  | -0.30 | 0.02 | 0.03 | -0.57 |
| 14 |  | -0.46 | -0.26 | -1.14 | -0.47 |
| 15 |  | -0.47 | -0.56 | 0.30 | -0.10 |
| 16 |  | -0.40 | -0.10 | 0.27 | -0.55 |
| 17 |  | -0.61 | -0.47 | -0.09 | -0.45 |
| 18 |  | -0.76 | -0.89 | -0.12 | -0.75 |
| 19 |  | -0.93 | -1.40 | -0.81 | -0.90 |
| 20 |  | -0.99 | -1.00 | -0.10 | -1.01 |
| 21 |  | -1.01 | -0.83 | -1.19 | -0.74 |
| 22 |  | -1.16 | -0.91 | -2.00 | -0.75 |
| 23 |  | -1.12 | -1.08 | -1.01 | -0.71 |
| 24 |  | -1.54 | -0.96 | -2.03 | -0.63 |
Usually, the three ratings are strongly correlated with one another. But there are some exceptions. England, for example, can line up the most expensive starting XI, but their Elo ranking has fallen during the qualifiers for the Euros following draws against Ukraine and North Macedonia.
Given the ratings of two teams, we then use an ordered logistic regression model to estimate the distribution of win/draw/loss outcomes between the two teams.
