Why should sports scientist be familiar with mixed effects (multi-level) modeling and effect statistics… Skill in analyzing longitudinal data is important for a number of practical reasons including; accounting for the dependencies created by repeated measures (athletes being measured over time), dealing with missing or unbalanced data (common occurrence in athlete monitoring practices), differentiating between-athlete from within-athlete variability, accounting for time-varying (e.