Competitive profile differences between the best-ranked European football championships
Bibliographic record
Abstract
Purpose.The aim of the study was to compare the competitive profiles of the best-ranked European football championships.Methods.The final rankings (n = 30) and the final scores of the matches played in the 2012, 2013, 2014, 2015, and 2016 seasons (n = 10,465) of the best-ranked European football championships (Spanish LaLiga, English Premier League, German bundesliga, Portuguese Liga Portugal, French Ligue 1, and Italian Serie A) were analysed.The instrument used was the battery of Indicators for the Assessment of Competitive Profile of a Championship.The competitive profile was analysed in three dimensions and their correspondent measurement indices: ( 1) degree of excellence: International Achievement and Classification Dominance indices; (2) equality of teams: Classification Compactness, Performance Sustainability, and Home Advantage indices; (3) type of matches: Match Openness, Match Equality, and result Uncertainty indices.ANOVA one-way analysis of variance and bonferroni post-hoc adjustment were used.Results.Significant differences were shown among the main European championships in the International Achievement (p = 0.003), Classification Dominance (p = 0.001), Classification Compactness (p = 0.009), Match Openness (p = 0.000), and Match Equality (p = 0.008) indices. Conclusions.LaLiga Spanish championship stands out as the only instance of a quality competitive profile.Consequently, LaLiga occupies the highest position in the indices related with the international prestige and the competitive quality of the teams (p < 0.001).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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 source (direct Gemma or distilled Codex), 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".