Chinese Super League: attendance, pricing, and team performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of team performance, price dispersion – having multiple ticket prices for a single event, and market characteristics on fan attendance. By considering the context of the Chinese Super League (CSL), this study considers multiple strategies for enhancing the demand for sport in relation to factors on- and off-the-field of play. Design/methodology/approach This study uses economic demand theory to examine consumer interest in sporting events in relation to pricing. Through employing econometric modeling, regression analysis is used to estimate results from match-level data encompassing multiple seasons. Findings The findings estimated from the linear regressions indicate that using multi-tiered pricing for sporting events does not significantly enhance demand in this context. Furthermore, it is found that consumers are responsive to matches against rival teams and strong opponents. Research limitations/implications The results run counter to prior literature on price dispersion, indicating that attendance demand may not always be influenced by the number of price points. Practical implications The findings help to develop an understanding of how team performance and pricing are important parts of meeting organizational goals in sport. From this, strategies can be formed to help stakeholders and managers in improving organizational performance. Originality/value This research is one of the first to consider the CSL, where both single and multiple price points exist for sporting events. Thus, it helps to build both theoretical and empirical knowledge in regards to the importance of pricing systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".