The Importance of Total Salaries in Determining Team Success: An Econometric Analysis of the NHL Salary Cap
Bibliographic record
Abstract
Among the popular conversation topics for Canadians, few evoke emotions as strong as hockey and defending one’s favorite team. Despite its prevalence, there remains little conclusive proof to settle many of the common debates between fans. One of the most popular and divisive issues in the current state of the National Hockey League (NHL) is the effect of teams’ salaries on their chances of winning. As the game has evolved over the last century, revenue and salaries have both grown steadily, but more recently, have exploded due to increased corporate interest. Since revenues were not spread equally among the thirty teams, this created a competitive imbalance where the richer teams could afford to offer higher salaries to attract most of the top players. To combat this, the NHL introduced a salary cap prior the 2005-2006 season, in an attempt to level the playing field between teams with different relative revenues. Since NHL managers have had several seasons to adjust to the new system, there is now an opportunity to study how strong the link between team salaries and team success was, both before and after its introduction to understand its effects.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".