Modeling Athlete Performance Using Clustering Techniques

This paper focused on using clustering techniques to analyze sport physiological data collected during incremental tests to support the planning of training sessions, to provide a tool for athlete self-evaluation. Modeling athlete performance to analyze the progress of a test session, automatically assign the tested athlete to a group of athletes which are similar to him with respect to physical parameters and development of the test,, and evaluate these groups with respect to two quality indexes of the performance of the athlete, whose real value is known only at the end of the test. It provides a continuous characterization of the progress of the test. Index Terms—data mining, clustering techniques, athlete performance, physiological data