Developing a Recommendation System for Collegiate Golf Recruiting

摘要

In the world of college sports, the process of recruiting players is one of the most important tasks a coach must tackle. With only 6% of the 8 million high school athletes earning spots on NCAA teams, finding and selecting the right players can be incredibly challenging even with the availability of widespread data. Some sports, like football and basketball, have found great success using predictive analytics to estimate success in college. These efforts, however, have not yet been extended to other sports, such as golf. Given the vast amount of data available to the public on junior golfers, there is clear potential to bring analytics to college golf recruiting. We partnered with GameForge, a leading golf analytics company, to create a recommendation tool for college coaches, one that leverages the already existing data on high school and collegiate golfers and a variety of predictive models to display athletes we believe would best fit in a certain college program. A systems analysis approach was taken to find the factors that most accurately predict a high school player’s success in college golf. This was done with a variety of models including the forecasting of probability of a high school athlete being a top ranked college golfer, the finding of players with a similar performance to another desired player, and the predicting of a junior golfer's scoring performance and development during the remainder of their high school career and during college. Using these models, we identified several factors that are predictive of player similarity and performance. The research team iteratively developed these models to be used in conjunction with each other in order to provide meaningful, and understandable recommendations to a college coach on which players they should recruit to maximize success.