Bibliographic record
Abstract
Purpose This paper aims to recommend opportunities for professional sport leagues in the USA and Canada to apply the art and science of revenue management in order to minimize potential losses and maximize profits. Design/methodology/approach The evolution of current key revenue management concepts is presented from their initial stages to their current level of implementation. In addition, the literature regarding the strongest business models is reviewed and examined in the context of current successes and challenges across the major sport leagues in North America. Findings Five revenue streams in sports organizations are identified and analysed. Five key elements for revenues are highlighted as strategic tools used to maximize effectiveness in achieving revenue management goals. A series of recommendations is made to best use revenue management including careful negotiation of television contracts, the use of dynamic pricing models, maximization of partnerships and sponsorships, acceptance of new approaches to food and beverage and accessibility of sport merchandise to customers. Practical implications At the regional, national and international levels, sports organizations should review their current business practices to identify areas to improve their revenue management in light of the recommendations in this paper. Originality/value Although the use of the concept of revenue management in sectors of tourism has evolved since early 1970s, its application in professional sports is relatively new. Therefore, this paper provides value to professional sports organizations to optimize their profitability.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".