A Forecasting Model of Success at the Euro Tournaments: The Role of Team’s Performance at Qualifying Games
Bibliographic record
Abstract
The European Football Championship (Euro) is different from any other soccer competition in the world in that it has a long and homogeneous qualifying period for all teams. Using data from every tournament that has taken place in history, a step logit model is estimated to quantify the role of team’s earlier qualifying performance in the likelihood of success at the final stage. Considering only the information available at the date preceding each of the last three Euros, we test the model’s ability to forecast the winner at future tournaments. The model correctly predicted Spain to win it in 2008 and 2012, as well as Portugal to take the Cup in 2016. Teams’ efficacy during qualification is found to be a key contributor to the model’s forecasting performance. Our results have strong implications about the current FIFA ranking system as a way to gauge teams’ relative strength, as well as about which information a sophisticated bettor should process in order to beat the odds and make a profit out of the betting market. In that regard, we conclude that the betting market is possibly not efficient when pricing teams at the Euros.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".