MétaCan
Menu
Back to cohort
Record W4416093601 · doi:10.32731/ijsr142.052019.03

Does Star Power Boost Soccer Match Attendance? Empirical Evidence from the Chinese Soccer League

2019· article· en· W4416093601 on OpenAlex
Bo Li, Yuanyang Liu, Jerred Junqi Wang, Olan Scott, Sarah Stokowski

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Sport Finance · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsBrock University
Fundersnot available
KeywordsLeagueAttendanceEmpirical evidenceTicketStar (game theory)Profit (economics)Power (physics)Stadium

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine the impact of star power on game attendance. The essential aspect of demand for sport events is fan interest, which can be shown through match attendance, watching contests, buying team-related products, and following a team on media. To conduct this study all attendance data from the 2015, 2016, and 2017 seasons of the Chinese Super League were used to understand which factors impacted spectator attendance. Results of this study found that high profile teams, traditional rivalries or derbies, and famous foreign players can positively impact attendance at both home and away games, while members of the Chinese National Team had a negative impact on attendance. These results will give team owners a better understanding of Chinese soccer fans’ interests, ultimately leading to maximizing profit generation in ticket and merchandise sales. Implications for both sport managers and scholars are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.027
GPT teacher head0.274
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it