The present study investigates the ideas of labor market discrimination within the National Basketball Association, specifically consumer discrimination through gate revenues collected at NBA games. Previous research has mainly focused on consumer-based discrimination on consumption of nationally televised games. These studies have shown a variety of results, but the majority imply that consumers discriminate against African-American players. Thus, teams with higher participation by white players enjoy increased revenues. This study will use similar techniques but will attempt to explain the determinants of gate revenues instead of television viewership. In order to accomplish this, an ordinary least squares (OLS) model will be employed, with a wide variety of explanatory variables in an attempt to best explain consumer's preferences when deciding to attend a professional basketball game. The current study has used a more recent data set than previous research. It is the goal of this study to determine if there is evidence of consumer discrimination in the unique labor market of the National Basketball Association.
The purpose of this study is to identify the primary determinants of an NBA player’s salary from his contract season performance. While some factors are outside a player’s sway, such as height and age, others such as points per game are within their control, and this study examines which factors are significant. The study examines 272 players and data from the year before they signed their new contract. A regression analysis tests the relationship between salary as the dependent variable and a number of independent variables. The analysis reveals that NBA teams value players who score points and generate wins (as measured by win shares). While teams will never forego the human aspect and evaluation present in every transaction, analyzing the statistical side should help expose some market inefficiencies currently present in the NBA.
The National Basketball Association (NBA) is one of the four largest professional sports organizations in the United States. There are currently 23 teams in the NBA that gathered over $100 million in revenue during the 2007-08 season alone. This study examines the components of total NBA franchise revenues and investigates the effect that multiple losing seasons has on total revenue performance. A fixed-effects regression analysis is used to examine the effect of multiple losing seasons on total NBA franchise revenue. All the statistics and data observed in this study are from the 10 year period of 1999 to 2008. The findings in this study provide valuable information to NBA teams as to whether losing consecutive seasons affects total revenue performance.
The National Basketball Association is one of the most popular professional sports leagues in the world. Franchise owners want the best players on their team to build an increasing fan base, win championships, and generate revenue. Therefore, players displaying key qualities are more likely to receive more money from owners. This paper investigates what productive, physical, and social attributes along with league regulations are most important in predicting a player’s annual salary. The data shows that social attributes have no effect on determining individual salary, but player productivity, Collective Bargaining Agreements, and physical capabilities are significant determinants of salary.
This study was inspired by the recent trend in the National Basketball Association (NBA) of teams signing seemingly mediocre players to abnormally large contracts. The purpose of this study was to identify if there were in fact other player characteristics that NBA teams looked for other than pure basketball ability when signing players. 284 NBA players and their salaries during the 2006-2007 were collected along with twenty other independent variables. Obviously players' salary was the single dependent variable. Data and analysis comprised a regression test to determine the relationship between these twenty independent variables and salaries. The regression test revealed a relationship between age and athleticism to salaries. A player's contract year approximate value to his team and scoring ability proved to have a strong relationship with salaries as well. Surprisingly, no player characteristic related to efficiency had any relationship to salaries.
As more money is committed to players, it is more pivotal than ever for National Basketball Teams (NBA) teams to find ways to accurately and comprehensively find young, cheaper talent in the draft better than their competitors. In this study we use all publicly available information about players that is available. The focus of this paper is to examine factors that contribute to early career success among professional players in the NBA and to better understand these quantifiable measures available on draft day that can aide in predicting players' future performance. In this study we run six separate Ordinary Least Squares (OLS) regressions with different sections of the data. One regression with all the player data, then we separate the others by one and two year players, three and four year players, guards, forwards, and big men. The independent variables used in this study are player position (point guard, shooting guard, small forward, power forward, center), the player's college win shares per year, win shares in their first year, average college win shares per year, quality of conference, NBA combine agility, combine no-step vertical leap, and dummy variables for when they came out of college. The dependent variable in all of the regressions is the average wins per year through five years of each players NBA career.