Today, statistical analysis can be used for a variety of different reasons. In sports, more particularly baseball, there is an increasing necessity to have better up to date analysis of players and their performance as they attempt to make it to the Major League. Athletes are constantly moving around within one or more organizations. Since they are moving around so often, clubs spend an ample amount of time determining whether or not it is for their benefit and betterment of the organization as a whole. The objective of this thesis is to utilize previous baseball statistics in StataSE to determine performance levels of players who played at the major league level. From these, regression-based performance models will be used to predict whether or not Major League Baseball organizations effectively and efficiently move players around from their farm systems to the big leagues. From this, teams will be able to see whether or not they in fact make the right decisions during the season. Several tasks were accomplished to achieve this outcome: 1. First, data was obtained from the Baseball-Reference statistics database and sorted in google sheets in order for me to perform analysis anywhere. 2. Next, all 1,354 players that entered the major leagues in the year 2016, were assessed as to whether or not they started in a given league and stayed, got promoted from the minor leagues to the majors, or demoted from the majors to the minor leagues. 3. Based off of prior baseball knowledge and offensive performance quantifications only, players' abilities were evaluated and only those who were called up or sent down were included in the overall analysis. 4. The statistical analysis software application, StataSE, was used to create a further analyze if any of the four major regression assumptions were violated. It was determined that logistic regression models would produce better results than that of a standard, linear OLS model. After testing multiple models, and slightly refining my hypothesis, the adjustments made developed a more accurate analysis of whether organizations were making an efficient move sending a player down to promote another player up. After producing the model, I decided to investigate at what level a player was deemed to be no longer able to perform at a Major League Baseball level.

In this work we analyze just what makes the topic of third party voting so intriguing to voters and why it is different than voting for one of the major parties in American politics. First, we will discuss briefly the history of politics in America and what makes it exciting. Next, we will outline some of the works by other political and economic professionals such as Hotelling, Lichtman and Rietz. Finally, using the framework described beforehand this paper will analyze the different stances that voters, candidates, and others involved in the political process of voting have regarding the topic of third party voting.

Our research encompassed the prospect draft in baseball and looked at what type of player teams drafted to maximize value. We wanted to know which position returned the best value to the team that drafted them, and which level is safer to draft players from, college or high school. We decided to look at draft data from 2006-2010 for the first ten rounds of players selected. Because there is only a monetary cap on players drafted in the first ten rounds we restricted our data to these players. Once we set up the parameters we compiled a spreadsheet of these players with both their signing bonuses and their wins above replacement (WAR). This allowed us to see how much a team was spending per win at the major league level. After the data was compiled we made pivot tables and graphs to visually represent our data and better understand the numbers. We found that the worst position that MLB teams could draft would be high school second baseman. They returned the lowest WAR of any player that we looked at. In general though high school players were more costly to sign and had lower WARs than their college counterparts making them, on average, a worse pick value wise. The best position you could pick was college shortstops. They had the trifecta of the best signability of all players, along with one of the highest WARs and lowest signing bonuses. These were three of the main factors that you want with your draft pick and they ranked near the top in all three categories. This research can help give guidelines to Major League teams as they go to select players in the draft. While there are always going to be exceptions to trends, by following the enclosed research teams can minimize risk in the draft.

The NFL is one of largest and most influential industries in the world. In America there are few companies that have a stronger hold on the American culture and create such a phenomena from year to year. In this project aimed to develop a strategy that helps an NFL team be as successful as possible by defining which positions are most important to a team's success. Data from fifteen years of NFL games was collected and information on every player in the league was analyzed. First there needed to be a benchmark which describes a team as being average and then every player in the NFL must be compared to that average. Based on properties of linear regression using ordinary least squares this project aims to define such a model that shows each position's importance. Finally, once such a model had been established then the focus turned to the NFL draft in which the goal was to find a strategy of where each position needs to be drafted so that it is most likely to give the best payoff based on the results of the regression in part one.

This paper explores the history of sovereign debt default in developing economies and attempts to highlight the mistakes and accomplishments toward achieving debt sustainability. In the past century, developing economies have received considerable investment due to higher returns and a degree of disregard for the risks accompanying these investments. As the former Citibank chairman, Walter Wriston articulated, "Countries don't go bust" (This Time is Different, 51). Still, unexpected negative externalities have shattered this idea as the majority of developing economies follow a cyclical pattern of default. As coined by Reinhart and Rogoff, sovereign governments that fall into this continuous cycle have become known as serial defaulters. Most developed markets have not defaulted since World War II, thus escaping this persistent trap. Still, there have been developing economies that have been able to transition out of serial defaulting. These economies are able to leverage debt to compound growth without incurring the protracted consequences of a default. Although the cases are few, we argue that developing markets such as Chile, Mexico, Russia, and Uruguay have been able to escape this vicious cycle. Thus, our research indicates that collaborative debt restructurings coupled with long term economic policies are imperative to transitioning out of debt intolerance and into a sustainable debt position. Successful economies are able to leverage debt to create strong foundational growth rather than gambling with debt in the hopes of achieving rapid catch- up growth.