We discuss simple yet impactful adjustments to two popular tools of network science. In community detection, Normalized Mutual Information is often used as a similarity measure to assess the quality of an inferred community structure against "ground truth" communities. We describe two drawbacks of this measure: bias towards excessive groups and bias in normalization and propose remedies to both. In ranking, the Bradley-Terry model (commonly known as the basis for Elo scores in chess) is widely applied to infer rankings in many contexts including sports and animal and social interactions. We propose a generalization of this model which can infer the role of luck and the depth of competition in these various hierarchies.