The popularity of data driven decision-making in sports has risen significantly since the success of the 2002 Oakland Athletics baseball team. Based on an objective, analytical approach, the franchise improved upon their 2001 record and made the playoffs despite a limited payroll and a lack of star players.
In the National Basketball Association (NBA), analytics have caused offenses to prioritize 3-point shooting over 2-pointers. All teams employ analytics experts in the hope of creating a competitive advantage and recently “player tracking” (measuring the movements of the basketball and of every player on the court multiple times per second) has introduced the era of big data in basketball. Evaluating teams and players, however, is not straightforward, even with the large amount of available data and multiple methods to quantify team and player performance. With only five players per team, the interaction between players is vital. As are coaching and player matchups. The main aim of this thesis is to gain insight into the NBA dynamics from a statistical point of view. To this end, principal component analysis (PCA) is performed as a descriptive tool.
An interesting result is found when comparing DeAndre Jordan and Andrew Bogut. Bogut was supposed to be the next star Center of the Dallas Mavericks after the team failed to sign All-NBA Center DeAndre Jordan in 2015. Despite high hopes, Bogut turned out to be a bad fit for the team and they missed the playoffs in 2016. A principal component analysis shows that Bogut and Jordan have similar playing styles and qualities. This appears to suggest that Jordan would also have been a bad fit for the Dallas Mavericks. The principal component method could help scouting departments to identify players of interest and coaches with the creation of their game plans.