Golf and GameForge: Innovative Analytics for Recommender Systems

Abstract

The college sports industry has grown tremendously over the past decade, with NCAA athletic departments recruiting almost half-a-million students to 19,866 teams in 2019 and generating $18.9 billion of revenue the same year. Identifying and selecting the best student-athletes is critical to maintaining the power of these sports programs, aggrandizing the recruitment pipeline and necessitating the demand for novel use of existing technologies. Sports analytics is one response to these growing needs, as its primary use in junior recruitment has presented fruitful for college basketball and football teams across the nation. Golf analytics firm GameForge aims to provide the same insights to college golf coaches, streamlining the recruitment of junior golfers to U.S. universities from around the world. GameForge seeks to develop a two-sided recruiting system that provides insights to junior players and their coaches as well as strengthen its predictive models with the inclusion of new data. A systems-based approach was taken to develop data-driven machine learning models that would provide (a) a proprietary ranking system that compares junior athletes to one another; (b) a relative SWOT analysis that highlights each player's strengths and skill gaps; and (c) a recommender system that suggests potential recruits to college coaches and recommends colleges of best fit to junior players.