Developing a Profitability Model for Professional Sport Leagues: The Case of the National Hockey League
Bibliographic record
Abstract
Escalating costs in professional sport, increased competition from entertainment alternatives, and a recent labor dispute in the National Hockey League (NHL) provide the impetus to study the underlying structure of team profitability. The current study takes advantage of this opportunity by developing and testing a profitability model for NHL teams based on the underlying premise that there are multiple determinants to franchise profitability. An extensive data set of more than 40 variables was extracted from the 2001-02, 2002-03, and 2003-04 NHL seasons to explore the complex nature of franchise profitability. The number of variables is reduced using principal components analysis and the model interactions are tested using a regression analysis. The results demonstrate that having a winning team is an important feature but it is not the only factor related to profitability. Indeed, winning is not directly related to profits but indirectly influences profits through the level of market support. The resulting model implies that profitability is directly determined by market support and player investment while a variety of other influences on profitability are enabled through the direct considerations. These indirect determinants include improved performance; team playing style; team composition; historical performance; market competition; arena location; and level of sponsorship. Regional and local television, the intent of ownership, and market characteristics are additional considerations that should not be completely dismissed from the list of profit determinants. The model has implications for both theory and practice and contributes towards the development of a profitability model for all professional sport leagues.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".