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.
{combineR} is a small package developed to easily gather and explore over 20 years of NFL Draft Combine data from Pro Football Reference. In addition to being able to pull data, the package also currently supports benchmarking tables for all NFL combine tests by position.