Football Rating Systems for Top-Level Competition: A Critical Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Comprehensive rankings of football teams have become an important, and occasionally controversial, feature of many football codes. The rationale behind these systems needs to be understood. The historical evolution of the current eight codes is briefly traced from just two: association football and rugby football, played under various forms in the mid 19th century. Based on current rules, the eight codes fall into four groups: first, Australian Rules football and Gaelic football; second, American college football, American professional football and Canadian football; third, rugby union and rugby league; and fourth, soccer. Comprehensive rating systems exist for three codes. For American college football, the Bowl Championship Series or BCS system places top US college football teams into a national championship game and other important "bowl games". That system combines two normally incompatible components, an objective adjustive computer component and a subjective human-poll component. The composite has been controversial in four of the nine years of service, when the computer component differed from the human component resulting in major changes that favored the human component each time. For rugby union, the International Rugby Board or IRB system employs a predictor/corrector adjustment in which defeating a weak team provides less gain than defeating a strong team while losing to a weak team elicits a much larger negative adjustment than losing to a strong team, arguably a fair and efficient methods for rating competitors. For soccer, FIFA have improved the previous rating systems with a new and simpler system which takes into account strength of opponents and game importance; however, all losses are treated as equal regardless of the opponents, and home advantage is ignored. An Elo based system, employing many of features of the IRB system, appears to have advantages over the FIFA system.
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.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it