Season Ticketing as a Risk Management Tool in Professional Team Sports: A Pricing Analysis of German Soccer and Basketball
Bibliographic record
Abstract
Ticket sales remain a significant source of revenue in professional team sports. However, season ticket revenue, as an effective risk-reducing instrument, is rarely analyzed in the literature. This study aims to determine, from a price and product perspective, the extent to which different factors affect season ticket prices. Using three different professional German sports leagues, a ticket-pricing model was developed as the empirical model. Consistent with other pricing studies, an ordinary least-squares (OLS) model and a Tobit model were fit. The results indicate that different season ticket rights, type of season ticket, club league membership, fan club membership, club stadium utilization rate, club sporting performance, and club market size have significant negative or positive impacts on season ticket price. Whereas, for example, a reserved seat in the stadium has a positive impact, the population of the club’s city has a negative impact. Based on the results, club managers should consider all traditional season ticket rights and season ticket discounts when calculating season ticket pricing. These and further implications are discussed with respect to the risk management issues of season ticket pricing in light of the COVID-19 pandemic and differences in local market constellations of professional team sports clubs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".