Western Hockey League (WHL) Regularized Adjusted Plus-Minus
Bibliographic record
Abstract
There is a complete lack of resources, tools and stats available for analyzing hockey players outside of the National Hockey League (NHL). Find any prospect breakdown and you will likely only find 2 or 3 stats available, that is, goals, assists, and sometimes +/-. This lack of information can lead to bad decision-making and makes it more difficult to narrow down and compare skill levels without extensive scouting. Goals and assists only show a subsection of a player's offensive game and does not encompass a player's defensive ability. +/- has flaws as well though, from not accounting for the quality of teammates and competition the player is put in place with, to the counting of power-play goals against and short-handed goals for, which unfairly treats players with few shorthanded minutes and many power-play minutes. Regularized adjusted plus-minus is a solution to these issues though, by looking at a goal by goal basis, we can analyze the impact of every player on the ice, and we can ensure we only count even-strength play for this metric to level out the playing field on players playing special teams.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.023 |
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".