Analyzing the Evolution of Novel Metrics and Statistics in Professional Athletics

Like many Americans, I have always been interested in and fascinated by watching professional sports. Sports like basketball, football and baseball have huge markets in the U.S., with millions of fans tuning in to watch the games. In addition to the spectacle of the games themselves, I have also always enjoyed following the analytical nature of professional sports. Following which teams are changing their organizational structure, game plan or recruiting ideology has always interested me as much as the games themselves. In this era of advancing technology, computing power and algorithms, I wanted to examine how these new technologies and statistics are being utilized by professional sports organizations. The focus of my thesis research was to analyze how grading and predicting performances of professional athletes has changed in different sports leagues, specifically with regards to how novel analytics and metrics play a role in this process. This research sought to determine if newer, more advanced models are actually better at evaluating professional athletes and predicting their future success. Analytics have continually changed throughout the history of professional sports, and team executives are always eager to have any advantage when it comes to identifying talent for their organization. However, it can be difficult to determine if a newer model will actually prove more effective. If not, it could severely hurt an organization by either failing to acquire much-needed talent or by putting their faith in a player whose talents were misreported. My thesis research was conducted to answer three key questions: 1) What are the newer predictive models being used in professional sports today? 2) How effective are these newer models? 3) How willing are sports executives to accept and utilize these advancements?