Competitive balance within CONCACAF: a longitudinal and comparative descriptive review of the seasons 2002/2003–2017/2018
Bibliographic record
Abstract
Rationale/Purpose: This article examines the trend in competitive balance and its association with end-of-year FIFA rankings among CONCACAF football associates.Design/methodology/approach: Secondary data were collected from the football domestic league tables for the seasons 2002/2003–2017/2018 of Costa Rica, Mexico, USA, Panama, Jamaica, Honduras, Trinidad and Tobago and Canada. Competitive balance was assessed using the Five-Club Concentration Ratio Index of Competitive Balance (C5ICB), Herfindahl Index of Competitive Balance (HICB) and Lorenz Seasonal Balance Curve. Linear regression modeling was used to assess the relationship between end-of-year FIFA ranking and competitive balance.Findings: The most competitive league was the USA, Honduras and Mexico, while the least competitive leagues were Trinidad and Tobago, Canada and Panama. For the 2017/2018 season within CONCACAF it was seen that the football leagues of the Jamaica, USA, Mexico and Panama were the most competitive balance leagues. The HICB and C5ICB were both significant predictors of a change in CONCACAF countries end-of-year FIFA rankings.Practical Implications: Competitive balance continues to be a vital component in assessing the viability and competitiveness of a football league which may have direct impacts on league authorities, marketing revenue streams and spectator attractions.Research Contribution: This is the first study to describe competitive balance in CONCACAF.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".