Analogous Forecasting for Predicting Sport Innovation Diffusion: From Business Analytics to Natural Language Processing

in Journal of Sport Management

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Liz Wanless Ohio University, Athens, OH, USA

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https://orcid.org/0000-0002-6896-2076 *
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Michael L. Naraine Brock University, St Catharines, ON, Canada

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https://orcid.org/0000-0002-9132-1269
DOI:
https://doi.org/10.1123/jsm.2022-0026
Keywords:
professional sport; artificial intelligence; discrete Bass model
First Published Online:
01 Feb 2023
In Print:
Volume 37: Issue 3
Page Range:
191–202
Restricted access

The purpose of this study was to analyze the diffusion of one sport innovation to forecast a second. Contextualized within the diffusion of innovations theory, this study investigated cumulative business analytics diffusion as an analog for cumulative natural language processing (NLP) diffusion in professional sport. A total of 89 teams of the 123 teams in the Big Four North American men’s professional sport leagues contributed: 21 from the National Football League, 23 from the National Basketball Association, 22 from Major League Baseball, and 23 from the National Hockey League. Utilizing an analogous forecasting approach, a discrete derivation of the Bass model was applied to cumulative BA adoption data. Parameters were then extended to predict cumulative NLP adoption. Resulting BA-estimated parameters (p = .0072, q = .3644) determined a close fit to NLP diffusion (root mean square error of approximation = 3.51, mean absolute error = 2.98), thereby validating BA to predict the takeoff and full adoption of NLP. This study illuminates an ongoing and isomorphic process for diffusion of innovations in the professional sport social system and generates a novel application of diffusion of innovations theory to the sport industry.

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