Browsing by Subject "NBA"
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Item Bayesian hierarchical parametric survival analysis for NBA career longevity(2012-05) Lakin, Richard Thomas; Scott, James (Statistician); Powers, DanielIn evaluating a prospective NBA player, one might consider past performance in the player’s previous years of competition. In doing so, a general manager may ask the following questions: Do certain characteristics of a player’s past statistics play a role in how long a player will last in the NBA? In this study, we examine the data from players who entered in the NBA in a five-‐year period (1997-‐1998 through 2001-‐2002 season) by looking at their attributes from their collegiate career to see if they have any effect on their career longevity. We will look at basic statistics take for each of these players, such as field goal percentage, points per game, rebounds per game and assists per game. We aim to use Bayesian survival methods to model these event times, while exploiting the hierarchical nature of the data. We will look at two types of models and perform model diagnostics to determine which of the two we prefer.Item Look good, play good : the world of American sports uniforms(2011-08) Pickhartz, Eric Michael; Coleman, Renita; Hunt, ThomasAs part of America’s cultural traditions, sports have become one of the most followed and widely appreciated aspects of entertainment and enjoyment for generations. The one consistent part of sports, that all fans and non-fans can understand, is the practice of team uniforms serving as identifiers and connectors to the city, franchise, and history they obtain. Look Good, Play Good: The World of American Sports Uniforms informs of the sports realm in the context of clothing. Four parts of the uniform world help describe and explain the teams and locations that wear them. They do this through historical, influential, and forward thinking distinctions.Item The Road to Sustainable Development: A Guide for Nongovernmental Organizations, PRP 120(LBJ School of Public Affairs, 1998) Magalhaes, Antonio Rocha; Schmandt, JurgenItem Shooting for Success: an Analysis of Predictive Basketball Analytics(2023-05) Geelhoed, DevinBasketball has changed greatly over recent years, thanks to the data-driven revolution in the way the game is played. Models to predict player and team performance are increasingly popular for team personnel to focus on what they are most successful at, for analysts to break down where advantages and disadvantages are had for different players or teams, and for viewers to create their own opinions on the players or teams they want to succeed or fail and inform betting decisions. This thesis seeks to define where current predictive analytics are lacking with a multimodal examination of three ways we analyze the game: equation-based prediction, machine learning prediction, and human prediction. The thesis focuses on each of these three methods of forecasting in turn, noting their strengths and weaknesses. Additionally, with equation-based prediction, the thesis provides an example model of Expected Points to project the outcome of a certain shot from a certain player. Lastly, the thesis focuses on future developments in predictive analytics and the ways they are shaping the basketball viewing experience.