Do Consumer Perceptions of Tanking Impact Attendance at National Basketball Association Games? A Sentiment Analysis Approach

in Journal of Sport Management

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Hua Gong Rice University

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Nicholas M. Watanabe University of South Carolina

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Brian P. Soebbing University of Alberta

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Matthew T. Brown University of South Carolina

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Mark S. Nagel University of South Carolina

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DOI:
https://doi.org/10.1123/jsm.2020-0274
Keywords:
big data; demand for sport; machine learning
First Published Online:
08 Apr 2021
In Print:
Volume 35: Issue 3
Page Range:
254–265
Restricted access

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.

Gong is with the Department of Sport Management, Rice University, Houston, TX, USA. Watanabe, Brown, and Nagel are with the Department of Sport and Entertainment Management, University of South Carolina, Columbia, SC, USA. Soebbing is with the Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, Alberta, Canada.

Gong (hg37@rice.edu) is corresponding author.
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