The Historical Development and Marketing of Fantasy Sports Leagues
Bibliographic record
Abstract
By 2013, fantasy sports leagues (and games) were entered into and played by more than an estimated 32-million people (Dwyer, 2013) across American and Canada, and produced a projected economic impact of $3.1-billion dollars annually (Fantasy Sports Trade Association, 2012). This work seeks to explain both the historical growth and cultural transitions of in-home sport gaming in the United States as well as the technological evolution that drove the appeal of modern ‘fantasy sports’ and continued enhancement of professional league avidity by developing stronger brand allegiances for leagues such as Major League Baseball (MLB) or the National Football League (NFL). From a historical standpoint, the term ‘Fantasy League’ didn’t garner popularity until the 1980s, but the concept of fantasy sports gaming can be traced back to the mid-1860s with a simple wooden tabletop contraption that simulated outcome elements of a baseball game (Cooper, 1995). Over the next 150 years, technological and social developments in the areas of in-home re-creation and networked gaming have caused fantasy sports to re-shape sports media coverage, sports marketing (particularly of sports data), fan avidity, technology engagement and general sport discussion. The impact of these fan-based developments might prove significant and increasingly leagues like the NFL have been forced to ask whether the at-home experience of following professional football (including the owning and managing of fantasy football league teams) is reaching a point where it could replace attending NFL games in person. A sea-change such as that (decreases in NFL game attendance, failure to sell out NFL games, etc.) would potentially threaten lucrative team and league sponsorships, stadium merchandise and concessions (food, beverage, parking, etc.) and possibly even TV network broadcast contracts (by way of diminished ratings and advertising demand).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".