By William J. Battle-McDonald
This paper examines how the quantity and quality of admissions applications to Division 1 colleges and universities were affected by two non-academic factors: (1) performance of a school’s men’s basketball and football teams; and (2) scandals associated with these athletic programs. Admissions data from 2001 – 2017 were compared to team performance during their football and basketball seasons in order to understand how these non-academic factors contribute to an individual’s decisions to apply for admission. A multivariate linear regression model with school and year fixed effects supported the hypothesis that athletic success positively affects the quantity of applications, increasing them by up to 3% in basketball and 11% in football in the following application period. Seasonal football success was also shown to have negative impacts on the distribution of standardized testing scores of future applicant classes, however these scores were shown to increase when a team played their best season in five or more years. Additional analysis of the effects of athletic program scandals reveals a significant negative effect on the number of applications received, although a deep dive into a few of the most prominent scandals suggests that the benefits associated with violating NCAA rules may, under the right circumstances, be well worth the risk.
Advisor: Dr. James Roberts | JEL Codes: I23, J24, L82, L83, Z2
By Dylan Newman
This paper examines factors that affect the transfer value of players transferred into the English Premier League from 2009–2015. The analysis begins by examining what factors are significant in determining a player’s projected transfer fee based on the website Transfermarkt.com as well as the actual fee that the player was sold for. The paper goes on to find that competition level and a player’s form are not statistically significant in models built to determine a player’s transfer value. Quantile regression is then used to illustrate that there is a superstar effect with a forward’s goal’s scored in the transfer market.
Advisor: Kent Kimbrough, Peter Arcidiacon | JEL Codes: L83, Z21 | Tagged: English Premier League, Quantile Regression, Soccer Transfer Fee
By Joshua Rosen
NBA teams have the opportunity each offseason to sign free agents to alter their rosters. Using only regular season per game statistics, I examine the best method of calculating a player’s appropriate salary value based upon his contribution to a team’s regular season win percentage. I first determine which statistics most accurately predict team regular season win percentage, and then use regression analysis to predict the values of these metrics for individual players. Finally, relying upon predicted statistics, I assign salary values to free agents for their upcoming season on specific teams. My results advise teams to rely heavily on Player Impact Estimate (“PIE”) when predicting their teams’ win percentage, and to seek players whose appropriate salaries would be significantly more than their actual season–long salaries if the free agents were to sign.
Advisor: Kent Kimbrough, Peter Arcidiacon | JEL Codes: C30, Z2, Z22 | Tagged: Free Agents, Salaries, NBA
The New Landscape of the NBA: The 2011 Collective Bargaining Agreement’s Impact on Competitive Balance and Players’ Salaries
By Nicholas Yam
The National Basketball Association (NBA) passed a new Collective Bargaining Agreement (CBA) in 2011 that introduced many changes to the structure of the league. The purpose of those changes was to improve competitive balance among the league, allowing smaller market teams to better compete with larger market teams. Many of the changes targeted the league’s salary cap and teams’ ability to pay players. This paper aims to determine whether competitive balance in the NBA improved under the 2011 CBA. The paper also determines which types of players’ salaries were affected the most. The results showed that competitive balance did not improve under the 2011 CBA. However, the results showed that higher performing players were paid proportionally more money than lower performing players following 2011 CBA.
Advisor: Peter Arcidiacon | JEL Codes: Z2, Z20, Z22