EURO 2024 Round 16 Predictions

Predictions for the EUROPEAN FOOTBALL CHAMPIONSHIP 2024 based on Statistical Analytical Football Models

AUEB & Trieste Sports Analytics Research Group,

Athens University of Economics and Business and University of Trieste

This article was edited and co-authored by Ioannis Ntzoufras, Professor of Statistics at AUEB, and Argyro Damoulaki, PhD Candidate in the same department. The article is based on the analysis of the collaborating team of Trieste (Professors Leonardo Egidi and Nicola Torelli, PhD candidate Roberto Macrì Demartino, and data science master student Giulio Fantuzzi) with the assistance of V. Palaskas (OpenBet, application development) D. Karlis (AUEB Statistics, analysis consultant). The final result is a cooperation between the research teams of the two universities on Sports Analytics.

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The group stage ended with several surprises, and we are entering the knockout round. As “classic” favorites to win we would characterize Germany against Georgia, which however is a tough team and will not be an easy opponent. Also of great interest is how England, the Netherlands, Portugal and France will present themselves, who did not let us with good impression in the first round. So, we start with a brief review of our predictions for the group stage and then we present our "predictions" for the round of 16 where we believe that the favorites will start to show their potential and whether they can reach the trophy. 

Reminder for friends of Statistics  

The use of statistical techniques to predict football matches first appeared in the scientific literature in 1968 with the pioneering scientific publication of Reep & Benjamin. The next real innovations appear in the 80s (with the work of Michael Maher) and the 90s (with the work of Lee in 1997). However, the first important publications in the field, introducing models on which models are based and which we still use today, were the works of Dixon & Coles in 1997 and the bivariate Poisson model of Karlis and Ntzoufra in 2003 (two of the authors of this analysis). These two models formed the basis of modern models for predicting football match outcomes.

In this analysis we use the model of Karlis and Ntzoufras through the package "footbayes" in the statistical programming language R developed by Professor Leonardo Egidi from the University of Trieste with the assistance of Vasilis Palaskas (Analyst at Open Bet and active member of AUEB Sports Analytics Group). The model also includes the estimation of parameters that estimate the performance of each group that change over time. To learn the model, all international matches of the 2020-2024 period were used. The main explanatory variable is the difference between the two teams in the Coca-Cola/FIFA ranking.  The model, first proposed by Karlis & Ntzoufras in 2003, extends the usual two-variate Poisson model.  Details of the statistical and machine learning model used can be found at the end of this article.

Review of matchday 3 and group round.

The results of Matchday 3 find our model moving in shallow water as the favorites (Germany, England, France, Netherlands, Belgium and Portugal) failed to win. The biggest surprise was Georgia's victory over Portugal, which had a probability of appearing just 6% based on the model. It should be noted that matchday 3 is traditionally the most difficult in terms of predictions as many teams play expediency football in order to get the draw for the qualification or even some teams that have secured the qualification do not use their key players. 

Overall, the model in all group stage matches did relatively well as it caught or visualized the flow of the match at a rate of 56%.

Table 1: Table with the odds of the outcome of the matches for Matchday 3 of the European Championship 2024.

Predictions for the Round of 16

From Table 2 with the possible results, the following teams stand out as favorites:

1. Spain with a 73% chance of winning over Georgia
2. England with a 71% chance of winning against Slovakia
3. Portugal with a 65% chance of winning against Slovenia
4. The Netherlands with a 62% chance of winning against Romania

Of these four favorites, Spain, based on their performances at the EURO, does indeed seem to be the undisputed favorite. The model seems to overestimate England who have done the job so far, but not in a particularly impressive way. Portugal also seems to be able to beat Slovenia relatively easily based on their pre-match performance against Georgia. The last race put us in some doubts. Finally, the Netherlands has disappointed so far and probably the 62% probability overestimates the current state of the race (remember that the model also uses races from previous races).

Finally, the remaining four races (half of them!) are closer but with a slight lead of one of the two teams. In these matches we consider that the teams are relatively close and may even draw due to tactics and strategy. In particular, we have

1. Austria (55%) beating Turkey (22%) but with an increased probability of a draw (23%)
2. Italy (44%) prevailing over Switzerland (29%)
3. Germany (45%) prevailing over Denmark (29%)
4. France (41%) beat Belgium (29%) but with an increased likelihood of a draw (30%)

From these matches, Switzerland has shown itself very strong as an opponent and it seems based on the matches that it will make it much more difficult for Italy than the model predicts.

Table 2: Table with the odds of the outcome of the matches for the round of 16 of the European Championship 2024.

Figure 1 gives in more detail the odds for each score for each of the 8 matches of the round of 16.

Bibliography for reading fans

• Dixon, M.J. and Coles, S.G. (1997), Modelling Association Football Scores and Inefficiencies in the Football Betting Market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 46, 265-280.
• Karlis, D. and Ntzoufras, I. (2003), Analysis of sports data by using bivariate Poisson models. Journal of the Royal Statistical Society: Series D (The Statistician), 52, 381-393. 
• Lee A.J. (1997). Modeling Scores in the Premier League: Is Manchester United Really the Best?  Chance, 10, 15-19.
• Maher, M.J. (1982), Modelling association football scores. Statistica Neerlandica, 36, 109-118.
• Reep, C., & Benjamin, B. (1968). Skill and Chance in Association Football. Journal of the Royal Statistical Society. Series A (General), 131, 581-585.