The aims of this study were i) to determine if performing regression models with high-risk datasets compared to full datasets could help to understand better which biomechanical variables could really be considered `at-risk` and/or `better for performance` and ii) to determine the effect of anticipation on `at-risk` biomechanical variables. Basketball players (n=33) completed 6 changes of direction in anticipated or unanticipated conditions. Kinematics, dynamics and performance were measured with motion capture and force plates. A lower number of predictors were found for high-risk dataset compared to full dataset. Hip adduction and trunk lateral lean could be both considered `at-risk` and `better for performance`. Moreover, anticipation impacts the "at-risk" technique, so training instructions should differ between anticipated and unanticipated sidestepping tasks.
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