This article introduces an innovative information system for analyzing and visualizing sports and human activity data. Our system employs clustering techniques to categorize data. We discuss and assess the challenges posed by the diverse preprocessing and clustering methods available: This study evaluates a range of data preprocessing and clustering methods to identify the most effective strategies. In particular, we describe an evaluation based on a recently collected real-world data set of German athletes. To achieve personalized athlete assessment our system can assign individual athletes to specific clusters based on their unique characteristic measurements and key performance indicators. To provide an intuitive way to evaluate how well an athlete fits within a specific cluster, a configurable web app is presented, allowing for easy comparison between an individual’s performance and their assigned cluster.