Bibliographic record
Abstract
This paper discusses the application of Moneyball management to the hockey industry. Following a review of Moneyball and sabermetrics in other sports, attempts to apply similar practices in hockey are reviewed. Moneyball in the National Hockey League is then examined, where adoption is limited by several factors: 1) the statistics available and their usefulness in evaluating player contributions to team performance; 2) the nature of the cooperation of players to produce outputs; and 3) the willingness of league insiders to embrace Moneyball. The statistical issue may be partially addressed by the introduction of new tracking technologies that can obtain new data, while teams may be more willing to explore Moneyball as teams who do so are successful. The development of new statistical measures are now helping to break apart the contributions that each player makes to team wins; however, the acceptance of these new measures will be limited by the speed by which teams adopt the new metrics. Finally, the recent imposition of a salary cap for NHL players will increase the value of using Moneyball analyses in hockey, as general managers and other team administrators are faced with a much smaller margin of error vis-à-vis evaluating and valuing player performance in hockey.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".