Abstract
This report, prepared for the Worcester Polytechnic Institute, describes the development of a running log application and the development and analysis of a data-centric approach to running performance prediction. The java application incorporated common UI principles as well as a community aspect to facilitate and encourage its use. The data- centric predictive model was developed by parsing meet results to follow each individual’s performances. Simplified, predictions are created by analyzing individuals who have performed similarly to the input. As tested with 1148 male track performances and 1265 female track performances, the data-centric approach provided predictions with an average error of 3.05 percent for men and 3.63 percent for women. These errors are approximately 9 percent and 20 percent lower, respectively, than the leading “Purdy Points” model.
Description
This report details a running log application and a predictive model for running performance.