The NBA Draft has become one of the most exciting and unique events in sports. Draft decisions are so monumental; so crucial to be right, so disastrous to be wrong. The purpose of this project is to build a model that would help teams to predict which types of players perform at a high level upon entering the league. By using regression analysis to predict the rookie year PER (performance efficiency rating) as a dependent variable, teams would have some idea of whether their rookies were underperforming, excelling, or performing at a level they could expect. The independent variables and their statistical significance could help answer a host of questions that front offices have about players: If a player came from a worse conference, can we expect them to have a harder time adjusting? Will their shorter wingspan have a negative effect on their play in the NBA? Do guards or forwards tend to have higher PERs upon entering the league? To answer these questions, I've gathered data on every first round NBA draft pick from 2001-2014 who played at least one season of Division 1 NCAA basketball. The data consist of anthropometric measurements (height, wingspan, standing reach, etc.), NBA draft combine results (agility drills, sprint times, etc.) and their college statistics per 40 minutes in their final season of college basketball (points, rebounds, assist-to-turnover ratio, etc.). I then separated the data into seven different sets: aggregate, backcourt, frontcourt, guard, wing, forward, and big. For each of these data sets, I built a predictive model for rookie PER. In doing so, I aimed to gain both a broad understanding of what factors lead to translation of college basketball play to professional play, and also a precise understanding of how those factors change for each distinct position.