Accurate kinematic analysis in speed skating is crucial to understand and improve skaters’ unique technique during training. Few research methods have captured joint kinematics in the past, using Inertial Measurement Units (IMUs) or manual annotation of filmed data or marker-based motion capture methods. The large motion capture volume and ice-rink environment hinders these methods to be actively adopted on rink. Thus, with growing accuracy in kinematic estimation algorithms, demand for a biomechanically accurate dataset is high. In this research, we aim to generate a virtual video dataset called ODAH-SpeedSkater, from experimental motion capture data. First, we examine the impact and accuracy of markers during motion capture. After selection of accurate inverse kinematics data, we use the SMPL-X human body model to achieve individual skater body shape and pose throughout the motion, rigging it to a skater-specific scaled OpenSim skeletal model. We render the finalized mesh sequences in an ice rink scene with skater outfits through realistic camera set-ups and configurations. Thus, we successfully create a dataset of 1,326 biomechanically annotated virtual videos of speed skating. Finally, we test our dataset on a pre-trained 3D kinematic estimation algorithm to evaluate its performance on speed skating data. In spite of limited testing, we conclude that training a network exclusively on our dataset may improve its performance, with the ultimate goal of actively implementing such networks in the rink.