The primary aim of this study is to compare the ability of three commonly used clustering techniques to identify movement strategies within countermovement jumps. A secondary aim is to interpret the identified movement strategies. A hierarchical, k-means using non- and normalized subject scores and an Expectation-Maximization approach using normalized subject scores were examined. The ability to identify movement strategies was measured using the r2-value of a regression model to describe jump height. Clusters of the best clustering solution were examined for differences. Hierarchical clustering utilizing normalized subject scores to generate 4 clusters appears to be the most suitable technique. The generated clusters demonstrated clear defining characteristics.