Who is the GOAT: Jordan, Bryant, or King James? An Inference Based on Data Crunching of the Surface Web
Bibliographic record
Abstract
Objectives: Michael Jordan, Kobe Bryant or LeBron James; each one of those top athletes is undoubtedly considered to be the best of his own basketball era. However, many professional athletes and sports critics still do consider Michael Jeffery Jordan to be the greatest basketball player of all time. The purpose of this study derive a statistical inference based on data derived from the surface web in relation to the most popular basketball player, the GOAT. Materials and methods: This study is based on data crunching of; Google Trends database (1) and the surface web (2), literature databases (3), grey literature (4), sports websites (5), in addition to media networks (5). An internet snapshot was taken for the trends database with a subsequent retrospective analysis of data retrieved from the past five years (2012-2017). Results: There was sharp (acute and intermittent) rise in the attentiveness of web users towards each of the three players. However, those were more noticeable for LeBron James. These were correlated with milestone events for each athlete career including; Kobe Bryant final NBA game, and LeBron opting out from his contract with Miami Heat. There was a significant difference in between LeBron’s popularity over both Jordan and Bryant (p-value<0.001). Furthermore, geo-mapping of data revealed that the top countries of highest attentiveness were; the Philippines, Dominican Republic, US, Canada and Hong Kong. Conclusion: For the past half-decade, it seems that the attentiveness of surface web users was more focused towards LeBron James than to either of Kobe Bryant or Michaela Jordan. Accordingly, LeBron is the GOAT since 2012. However, the data analysis did not go back in time prior to 2012, in which it is expected that the relative popularity of the three competitive players might be different.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".