The Nexus of Sports Fandom and Sports Wagering: A Scoping Review
Bibliographic record
Abstract
This scoping review examines the intersection of sports fandom and sports betting, with a particular focus on the marketing considerations involved in this relationship. Sports fandom, driven by strong emotional investments and team identification, plays a critical role in shaping betting behaviors. As this relationship becomes increasingly complex, it is essential to explore the underlying motivations and behaviors of sports fans who engage in betting, the ethical implications of marketing practices targeting this group, and the methodologies used to study this intersection. To address these relationships, this review examines four key areas: motivations for sports fans to engage in betting, responsible marketing practices, the methodological approaches used in current studies, and future research directions. This review highlights that fans are motivated to bet not only by potential financial gain but also by socialization, excitement, and loyalty to their teams. However, this emotional engagement raises ethical concerns about the normalization of betting in marketing, especially in targeting vulnerable populations. The review also identifies a reliance on quantitative methods in the existing literature and calls for more qualitative and longitudinal research. Finally, the study highlights the need for further exploration of diverse demographics and the impact of new technologies on the relationship between fandom and betting. This review offers insights for researchers, marketers, and policymakers to better understand and navigate the evolving landscape of sports betting and sports fan behavior.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".