Analysis of Brain Diagnoses and the Incidence of Chronic Traumatic Encephalopathy (CTE)
by Arjun Lakhanpal
Abstract
Chronic traumatic encephalopathy (CTE) has become a significant area of scientific inquiry in relation to various sports with contact exposure, specifically boxing and professional football, resulting from many individuals who participated in these sports being diagnosed with CTE neuropathology after death. This paper contributes to the CTE literature by analyzing the various predictors of the progression of neurodegenerative disorders, including CTE, that are associated with a history of head impact exposure. In addition, it analyzes how manner of death shifts depending on an individual’s clinical brain diagnosis, which is a decision based upon the clinical record and case review of a patient.
Through data from the NIH NeuroBioBank, the VA-BU-CLF Brain Bank, and data self-collected from living individuals with symptoms associated with CTE, this paper explores an analysis of various brain diagnoses through a large control population and small subset of athletes and veterans. Logistic regression models are established to analyze explanatory variables of clinical brain diagnosis, manner of death, and CTE presence and severity.
These logistic regression models confirm previous research surrounding the potential racial influence present in Black populations with schizophrenia related diagnoses and illustrate the degree to which neurodegenerative disorders, specifically Parkinson’s Disease, are influenced by increased age. Specific to CTE, the analysis conducted through the sample population illustrates the influence of an extra year of football played at the professional level, while counteracting existing literature regarding the association between position and CTE.
Professor Jason Luck,Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: I10, Z20, J15
For Love of the Game: A Study of Tournament Theory and Intrinsic Motivation in Dota 2
By YAO Shengjie
This paper studies the effect of intrinsic motivation on the extrinsic incentives specified by tournament structure in tournament theory in the context of e-sports. It incorporates tournament theory and motivation crowding theory in the same framework, something that past literature have hinted towards but never formally done so. It also uses an e-sports dataset, a type of dataset that few academics in the past have dealt with, but one that offers many interesting potentials. Results weakly show that crowding-in occurs in e-sports, but the effects of tournament structure on performance are inconclusive in the context of this paper. Implications of this paper lie mainly in the possibility for future academics to utilise e-sports data for research.
Advisors: Professor Grace Kim | JEL Codes: J31, J33, J41, M51, M52, Z20
Team Payroll Versus Performance in Professional Sports: Is Increased Spending Associated with Greater Success?
by Grant Shorin
Abstract
Professional sports are a billion-dollar industry, with player salaries accounting for the largest expenditure. Comparing results between the four major North American leagues (MLB, NBA, NHL, and NFL) and examining data from 1995 through 2015, this paper seeks to answer the following question: do teams that have higher payrolls achieve greater success, as measured by their regular season, postseason, and financial performance? Multiple data visualizations highlight unique relationships across the three dimensions and between each sport, while subsequent empirical analysis supports these findings. After standardizing payroll values and using a fixed effects model to control for team-specific factors, this paper finds that higher payroll spending is associated with an increase in regular season winning percentage in all sports (but is less meaningful in the NFL), a substantial rise in the likelihood of winning the championship in the NBA and NHL, and a lower operating income in all sports.
Professor Peter Arcidiacono, Faculty Advisor
Professor Kent Kimbrough, Faculty Advisor
JEL Codes: Z2, Z20, Z23, J3
Mixed Strategy Equilibrium in Tennis Serves
by Joel Wiles
Abstract
A mixed strategy is a random choice among available strategies, with each strategy being chosen a set percentage of the time. In many games that require unpredictable play, game theory predicts that a mixed strategy equilibrium, a situation where each player uses an optimal mixed strategy, will result. Economists have tested whether people play according to the mixed strategy equilibrium in laboratory experiments with two player zero-sum games—subjects in these experiments generally do not play in accordance with game theory’s predictions. Recently, economists have published papers examining mixed strategy equilibrium play using professional sports as a natural experiment. This paper builds upon Walker and Wooders (2001), which examines mixed strategy play in the locations of serves in professional tennis matches. Walker and Wooders (2001) find that professional tennis players are closer to game theory’s predictions than subjects in laboratory settings, but still “switch their serves up” more than is consistent with game theory’s predictions. My hypothesis is that this result can be explained by a short-term timing effect where a serve that has just been hit is, ceteris paribus, less effective on the next point. I construct a model incorporating this timing effect and work out the theoretical implications of my model. I then estimate the magnitude of this timing effect and determine if optimal play under this model is consistent with the results obtained by Walker and Wooders. My conclusion is that the model accounts for a little under half of the deviation from game theory’s predictions found in the data from professional tennis matches. This suggests that professional tennis players play closer to game theory’s predictions when tested using a model designed to account for more of the complexities of tennis than the Walker and Wooders model, but they still do not play in complete accordance with those predictions.
Professor Curtis Taylor, Faculty Advisor
JEL Codes: Z20,
