Developing a demand model to estimate attendance at an individual NBA game from related-game attributes

Kenneth K. Chen, James J. Zhang, Brenda G. Pitts, Thomas A. Baker, Kevin K. Byon

    Research output: Chapter in book/report/conference proceedingChapterpeer-review


    The purpose of this study was to build and test a demand model from game-related attributes to estimate fluctuations in attendance on a game-specific basis by controlling for the home team and examining those attributes solely associated with the visiting teams. Four major factors were selected to be the main focus of the current model, namely game schedule, star power, team performance, and uncertainty of outcome. The proposed model was tested using an empirical data set of a total of 1,189 NBA games played by six teams over five seasons. The resulted prediction models may be useful for estimating the demand of attendance because this study confirmed the strong effect of star power on demand, clarified the three-level day-of-week variable, added and verified the effect of the game availability variable, incorporated and validated new and more accurate variables to capture team performance, and discovered the potential moderating effect of the home team’s quality on the relationship between uncertainty of outcome and game demand.
    Original languageEnglish
    Title of host publicationGlobal Sport Business
    Subtitle of host publicationManaging Resources and Opportunities
    EditorsBrenda G. Pitts, James J. Zhang
    Place of PublicationNew York and London
    Number of pages26
    ISBN (Electronic)9780429025662
    ISBN (Print)9780367671594, 9780367132880
    Publication statusPublished - 4 Mar 2019

    Publication series

    NameWorld Association for Sport Management Series


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