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The Effects of Roster Turnover on Demand in the National Basketball Association

Author

Listed:
  • Alan L. Morse

    (University of Northern Colorado)

  • Stephen L. Shapiro

    (University of Northern Colorado)

  • Chad D. McEvoy

    (Illinois State University)

  • Daniel A. Rascher

    (University of San Francisco)

Abstract

The purpose of this study was to examine the effects of roster turnover on demand in the National Basketball Association (NBA) over a five-year period (2000–2005) and compare these results to previous research on turnover in Major League Baseball (MLB). A censored regression equation was developed to examine the relationship between roster turnover and season attendance, while controlling for other potentially confounding variables in the model. The censored regression model was used to account for the capacity constraints by forecasting the level of demand beyond capacity using information from the uncensored observations. The regression model was found to be significant with a log-likelihood statistic of 113.631. Previous attendance, current winning percentage, previous winning percentage, number of all-star players, local major sport competition, and team history were found to be significant predictors of attendance. However, the variables measuring the effects of roster turnover were not found to be significant. There were substantial differences in the effect of roster turnover on attendance in the NBA compared with MLB. In addition, these findings provide evidence for using censored regression when dealing with constrained variables. Sellouts in the NBA appear to have an effect on all of the variables in the demand model. Future research will need to be conducted to help sport managers understand the role of roster turnover in specific professional leagues and to better understand the importance of using a censored regression model.

Suggested Citation

  • Alan L. Morse & Stephen L. Shapiro & Chad D. McEvoy & Daniel A. Rascher, 2008. "The Effects of Roster Turnover on Demand in the National Basketball Association," International Journal of Sport Finance, Fitness Information Technology, vol. 3(1), pages 8-18, February.
    • Handle: RePEc:jsf:intjsf:v:3:y:2008:i:1:p:8-18
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    2. Yiannis Nikolaidis, 2015. "Building a basketball game strategy through statistical analysis of data," Annals of Operations Research, Springer, vol. 227(1), pages 137-159, April.
    3. Drayer, Joris & Shapiro, Stephen L., 2011. "An examination into the factors that influence consumers’ perceptions of value," Sport Management Review, Elsevier, vol. 14(4), pages 389-398.
    4. Gong, Bingnan & Zhou, Changjing & Gómez, Miguel-Ángel & Buldú, J.M., 2023. "Identifiability of Chinese football teams: A complex networks approach," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

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    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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