Do Consumer Perceptions of Tanking Impact Attendance at National Basketball Association Games? A Sentiment Analysis Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of big data in sport and sport management research is increasing in popularity. Prior research generally includes one of the many characteristics of big data, such as volume or velocity. The present study presents big data in a multidimensional lens by considering the use of sentiment analysis. Specifically focusing on the phenomenon of tanking, the purposeful underperformance in sport competitions, the present study considers the impact that consumers’ sentiment regarding tanking has on game attendance in the National Basketball Association. Collecting social media posts for each National Basketball Association team, the authors create an algorithm to measure the volume and sentiment of consumer discussions related to tanking. These measures are included in a predictive model for National Basketball Association home game attendance between the 2013–2014 and 2017–2018 seasons. Our results find that the volume of discussions for the home team and sentiment toward tanking by the away team impact game attendance.
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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 | 0.001 |
| 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) | 0.003 | 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 it