Who Will Stay and Who Will Go? Related agglomeration and the mortality of professional sports leagues in the United States and Canada, 1871–1997
Bibliographic record
Abstract
Professional sports leagues play a major role in our society, but little attention has been given to organizational factors related to league survival. We address this issue by examining the effects of related agglomeration (the extent to which league teams are located near teams from other sports that share the same broad professional sport identity), sport age, market heterogeneity (high variance in the number of teams from other sports in its teams’ cities), and within-sport league competition (high niche overlap) on league mortality. Related agglomeration may lead to intensified competition but may also lead to benefits by producing agglomeration economies and by driving the development of regional identities. We propose that the effects of related agglomeration vary over a focal population’s life cycle. We also argue that leagues with high market heterogeneity have higher chances of failure, particularly under conditions of high competition. We test our ideas using event history analysis to examine mortality in the entire population history of professional sports leagues in the United States and Canada (which are fundamentally different from leagues in other parts of the world) from the first league founding in 1871 until 1997. We find that leagues in young sports whose teams tend to be located in cities with large numbers of other sports (high related agglomeration) suffer from higher mortality rates while leagues that are in more established sports are less likely to fail under these circumstances. Consistent with prior research, leagues are more likely to fail when they experience higher competition (higher niche overlap) with other leagues in their sport, and the effects of competition are exacerbated by high variance in the number of other sports across the leagues’ cities (high market heterogeneity). We end by discussing the implications of our results for more common multi-unit organizational forms such as franchises and by considering promising avenues for future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".