Abstract
Introduction
The National Collegiate Athletic Association (NCAA) golf recruiting process is tedious, disorganized, difficult, and costly for both players and coaches. The technical portion of this project works to alleviate some of the difficulties in NCAA golf recruiting by working with Gameforge, a leading golf analytics company, to create a recommender system that matches female junior golfers and NCAA college coaches. The STS research identifies and suggests solutions to inequalities in NCAA golf recruiting by race and socioeconomic status with the ultimate goal of creating a more fair recruiting process for junior players. The motivation to investigate existing inequities in NCAA golf recruiting arose from modernizing current golf recruiting tools on the technical project. Both the tightly coupled technical project and STS research paper offer improvements to the NCAA golf recruiting system.
STS Research and Technical Project
My STS research identifies how biases within the current U.S. golf system have established a lack of representation of players from lower socioeconomic classes and African American players on NCAA golf teams. Using Critical Race Theory (CRT), my research analyzes how institutional racism manifests itself within the U.S. golf system. My STS research also examines how the organization of the NCAA recruiting process and the factors that affect junior golf exposure have established a homogenous culture amongst NCAA golf teams. Lastly, my STS research paper evaluates the technical project’s potential to diversify NCAA golf teams. The technical project team partnered with Gameforge to produce a recommender system for NCAA golf coaches that simplifies the recruiting process, not only making recruiting more efficient, but also granting coaches more confidence in their recruits. This recommendation tool leverages already existing data on high school and collegiate golfers and a variety of predictive models to display athletes that would best fit for certain college program. The technical project team used a systems analysis approach to find the factors that most accurately predict a junior player’s success in college golf. The team then used these predictive factors to create a variety of models, including forecasting the probability of a junior athlete being a top ranked college golfer, forecasting the probability of a junior golfer joining a Division I team, finding players with similar performance to desired players, and predicting a junior golfer's scoring performance and development during the remainder of her high school career and during college. The technical project team’s final deliverable was a recruiting dashboard for college coaches that incorporates the four models produced to provide multiple statistical viewpoints on potential recruits.
Conclusion
Completing the STS and technical projects simultaneously allowed for a deeper understanding of both projects. Specifically, getting firsthand exposure to specific data sources on the technical project allowed me to understand how biases within these sources create unequal opportunity in NCAA golf recruiting. Through my STS research, I uncovered that institutional racism within the U.S. golf system creates unequal opportunities for different players that ultimately contributes to the underrepresentation of African American players on NCAA golf teams. Ultimately, by pairing these two research projects, I was able to form both descriptive and data-driven recommendations on how to increase diversity on NCAA golf teams.
Acknowledgements
I would like to thank our Gameforge clients, Brian Bailie and Mark Sweeney, for their support throughout our technical project. Additionally, I’d like to thank my STS advisor, Richard Jacques, and my technical advisors, William Scherer and Stephen Adams, for their guidance; our team could not have done this project without your help. Lastly, I’d like to thank my technical project team members for their teamwork and efforts on our project.
