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 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".