Comprehensive evaluation model for athletes based on PageRank and complex networks
Bibliographic record
Abstract
A necessary condition for the accurate evaluation of the comprehensive strength and greatness of athletes in sports is the provision of objective and quantifiable criteria, which can reduce subjective bias and increase persuasiveness. This paper builds a model based on the characteristics of individual sports competitions, from which 'The Greatest Athlete of All Time' (The G.O.A.T.) is selected. Boxing was chosen as the object of study, and the model first collected relevant data on the BoxRec website, and built a complex network among boxers based on the relationship between opponents' fights against each other. With the support of a large amount of data, this paper uses the PageRank algorithm to score and rank the players according to the objectivity and practicality of the data, and obtain the 'greatest athlete of all time' in boxing. In order to extend the evaluation model to all individual sports, this paper subdivided the individual sports into direct and indirect athletics, and implemented differentiated evaluation. For indirect athletics, the indicators like 'relative score' and 'record keeping time', are added. The aim is to select the 'greatest athletes of all time' through comprehensive analysis.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".