During the National Basketball Association’s (NBA) 2011-12 season, Jeremy Lin experienced an entirely unexpected rise to stardom—one so exceptional that he appeared on the cover of Time Magazine with the subtitle "Linsanity!". The proliferation of analytics in sports has made talent evaluation practices very efficient, yet, as Lin proved, not perfect due to the inherent randomness of sporting performances. This collection of essays looks to explore the relationship between random performances and player evaluation by assessing why players like Lin emerge and addressing how they impact the talent identification process. First, we analyze the extent to which "sleepers" exist in the NBA by using injuries as a natural experiment to study a team’s performance with replacement players. Employing a combination of one-way fixed effects regression and hierarchical Bayesian state-space models, we find that, while rare, a team may actually improve and outperform expectations following an injury. This is more likely for teams that rely heavily on a single player, which implies a crowding out effect in which a salient high performer limits the use and evaluation of their teammates. Second, we turn to Major League Baseball (MLB) to examine the nature of brief hot streaks, using winners of the Player of the Month Award as our subgroup of interest. Employing a Bayesian hierarchical generalized additive model (BHGAM), we find that award-winning months are generally large overperformances, and the career trajectories of those who win the award a single time are starkly different from those who win it multiple times. Then, using Ordinary Least Squares regression we find evidence that players are compensated with larger salaries in the year following a Player of the Month Award win, despite the chances that it was earned due to randomness.