Given the proliferation of data regarding an athlete’s physical, physiological, psychological, and tactical state, Sports Scientists in the applied setting are increasingly being required to provide statistics, both descriptive and inferential, to coaches and other support staff to provide decision-making insight. However, these data sets can be imbalanced (i.e., more/less data on some athletes), contain many variables, include small sample sizes, and display only small individual/group differences. While these properties are not unique to sports science, these properties are often a barrier to performing robust statistical analysis. Therefore, the overall aim of this thesis is to provide Sports Scientists with access to applications of statistical methods that will expand their statistical toolkit to accommodate data sets regularly seen in a sports science context. Namely, the thesis explores mixed models for repeated measures and imbalanced data sets, Pareto frontiers when multiple variables need to be considered in tandem, and Bayesian inference to provide probabilistic statements when handling small samples and effect sizes. [...]