Classification Trees and Effective Recruiting in College Sports
Abstract: Classification trees are used with a categorical response variable. The goal of a classification tree is to derive a model that predicts to which category a particular subject or individual belongs, based one or more explanatory factors. For example, we could use a classification tree to predict the level of success for college soccer players based upon information available to coaches during the recruiting of these athletes. These classification trees are displayed as a decision tree that has a start node which then branches into other nodes. Using classification and regression trees (CART), we develop the ability to fit a tree to data. Once we have formulated a CART model through pruning and impurity, we evaluate its predictive ability. We apply this methodology to data obtained from the St. Lawrence Women's Soccer team. Upon finding the best CART, we compare it against a logistic regression model to check its accuracy. If results are sufficient and measurable, we can use the model to improve future recruiting.