During the last two decades investigations into motor learning have gone beyond the traditional discrete sum-mary statistics and more and more complex process oriented movement variables are being investigated. This increase in the complexity of data entails also an increase in the complexity of the data analysis. The present paper serves as an intro-duction for sports scientists to several different analysis methods, which have produced many interesting insights in the area of motor control and motor learning over the last few years, thereby highlighting non-linear aspects of motor learn-ing. An approachable introduction to root-mean square measures, uncontrolled manifold analysis, principal component analysis, and cluster analysis is given. These analysis tools enable sports scientists to investigate motor learning from a non-linear perspective and to gain a better knowledge of the processes occurring during motor learning.