The purpose of this study was to create a parametric running model in the AnyBody Modeling System (AMS) based on a large amount of running trials, with the possibility of adding more trials to further develop the model. 149 kinematic running trials were obtained from running customers at the company, Kaiser Sport & Orthopedics (KSO), and was used to drive a full-body musculoskeletal running model, developed in AMS. Mixed terms of Fourier series represented anatomical joint degrees-of-freedom throughout a running cycle. Fourier coefficients were stored in a matrix, from where principal component analysis was performed to interpret variation of the data and correlations between anthropometrics and all anatomical degrees-of-freedom. Eigenvectors acted as measures of the parameters’ influence on each principal component (PC). 90 % of the total variance were described by the first 39 PCs and 50 % were described by the first 10 PCs. A parametric running model driven by Fourier coefficients was successfully created. Pseudo-generated models with standard deviation of +/-3 along each PC were compared along with the parameters with the largest eigenvalues to understand the relationship between the top ten PCs and running techniques as interpreted in the clinical practice of KSO.