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Record W3164529193 · doi:10.1123/jsm.2020-0322

Stadium Giveaway Promotions: How Many Items to Give and the Impact on Ticket Sales in Live Sports

2021· article· en· W3164529193 on OpenAlexaff
Jeffrey Cisyk, Pascal Courty

Bibliographic record

VenueJournal of Sport Management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsStadiumAttendancePromotion (chess)LeagueTicketAdvertisingMarketingBusinessLawPolitical scienceComputer scienceComputer securityMathematics

Abstract

fetched live from OpenAlex

Although stadium giveaways are the most common type of promotion used in Major League Baseball to increase demand, most teams supply fewer giveaway items than there are tickets sold. This study argues that giveaway availability is a major component of teams’ promotion strategies and has been largely overlooked in the literature. The authors document the choice of giveaway availability across all Major League Baseball teams over an 8-year period and demonstrate that attendance increases with giveaway availability up to the point where there are enough giveaway items to serve 40% of a stadium’s capacity. Roughly two thirds of teams set giveaway availability in a fashion that is consistent with the standard price discrimination rationale for promotions found in the economic and marketing literatures. The remaining teams exhibit levels of high availability, indicating an additional investment into fan lifetime value, which is corroborated by these teams’ unique fan relationships.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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