Single-Entity MLS vs. The English Premier League
Bibliographic record
Abstract
Soccer, otherwise known as football to the rest of the world, was and is a dominating sport, not only in popularity but in organization and individual profit as well. The Fédération Internationale de Football Association [hereinafter “FIFA”] alone had a net profit of around $3.54 billion during the 2018 World Cup. This figure is only the tip of the iceberg in regard to the positive impact that football can have on the organizations that control it. Not only do FIFA’s numbers speak to the profitability of football, but England’s Premier League in 2019 alone grossed around $4.577 billion. Comparably, the U.S.’s MLS teams produced “$1.1 billion in revenue . . . La Liga powerhouse FC Barcelona alone reported $1.1 billion in revenue during its 2018-19 campaign.” MLS has overcome much hesitation when it comes to playing on a world-wide level. MLS, as it stands, is currently ranked around the 10th best soccer league in the world, both in quality and popularity. Although the highest level of soccer played in both the US and Canada, it is not nearly as high as it could be. While the likes of La Liga and the English Premier League are top tiers, “MLS is mostly compared with second divisions in top European leagues.” So why is there a disparity in revenue between the European football world and that of the United States?\nThis post was originally published on the Cardozo International & Comparative Law Review on February 23, 2022. The original post can be accessed via the Archived Link button above.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".