The diffusion of natural language processing in professional sport

ABSTRACT Framed by the diffusion of innovations theory, this paper explored the adoption of natural language processing (NLP) in professional sport. NLP, the ability for computer algorithms to be trained for pattern recognition in text data, is of key interest given the surge in text data available for sport business use. Ninety-one teams (73.98%) from the “Big Four” North American professional sports leagues: the National Football League (NFL; 68.75%), the National Basketball Association (NBA; 76.67%), Major League Baseball (MLB; 73.33%), and the National Hockey League (NHL; 77.42%) participated. A multiple methods approach utilizing a discrete derivative of the Bass model, integrative literature review and qualitative description uncovered the mechanisms, timing and key influences surrounding NLP diffusion. The findings highlight NLP diffusion at near peak adoption for the professional sport industry, reveal the organizational influences catalyzing the adoption timing, and create the context for academics and practitioners to embrace NLP. HIGHLIGHTS The discrete Bass model findings highlight natural language processing (NLP) diffusion at near peak adoption. Full NLP adoption for the professional sport sample is predicted for 2031. Key NLP adoption influences were multi-faceted from business processes to personnel.