Building Tomorrow’s Sports Fans: Strategic Initiatives in Youth Engagement
Bibliographic record
Abstract
National Hockey League fans in the United States are aging, presenting a challenge for the League’s future growth. The Toronto Maple Leafs (the Leafs), the National Hockey League’s most valuable franchise, recognize that younger generations engage with sports differently than traditional fans and often perceive hockey as less appealing compared to other major sports. Despite their loyal and historic fan base, the Leafs understand that sustaining long-term growth and cultural relevance requires attracting and engaging younger audiences. To address this, the Leafs launched the Next Gen initiative, transforming select home games into youth-focused experiences and leveraging digital strategies tailored to Gen Z. This case presents a critical question: How can National Hockey League teams—and other sports leagues and teams more broadly—build sustainable, younger fan bases essential for their future? Through this case study, students will analyze demographic trends in sports fandom, identify the challenges teams face in expanding their fan base, evaluate targeted marketing strategies, and propose innovative solutions to help marketing managers effectively engage and retain younger audiences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".