In recent years, advanced metrics have dominated the game of Major League Baseball. One such metric, the Pythagorean Win-Loss Formula, is commonly used by fans, reporters, analysts and teams alike to use a team’s runs scored and runs allowed to estimate their expected winning percentage. However, this method is not perfect, and shows notable room for improvement. One such area that could be improved is its ability to be affected drastically by a single blowout game, a game in which one team significantly outscores their opponent.<br/>We hypothesize that meaningless runs scored in blowouts are harming the predictive power of Pythagorean Win-Loss and similar win expectancy statistics such as the Linear Formula for Baseball and BaseRuns. We developed a win probability-based cutoff approach that tallied the score of each game once a certain win probability threshold was passed, effectively removing those meaningless runs from a team’s season-long runs scored and runs allowed totals. These truncated totals were then inserted into the Pythagorean Win-Loss and Linear Formulas and tested against the base models.<br/>The preliminary results show that, while certain runs are more meaningful than others depending on the situation in which they are scored, the base models more accurately predicted future record than our truncated versions. For now, there is not enough evidence to either confirm or reject our hypothesis. In this paper, we suggest several potential improvement strategies for the results.<br/>At the end, we address how these results speak to the importance of responsibility and restraint when using advanced statistics within reporting.

This study utilized a literature review and an analysis of Google Trends and Google News data in order to investigate the coverage that American men’s soccer gets from the media compared to that given to other major American sports. The literature review called upon a variety of peer-reviewed, scholarly entries, as well as journalistic articles and stories, to holistically argue that soccer receives short-sighted coverage from the American media. This section discusses topics such as import substitution, stardom, and American exceptionalism. The Google analysis consisted of 30 specific comparisons in which one American soccer player was compared to another athlete playing in one of America’s major sports leagues. These comparisons allowed for concrete measurements in the difference in popularity and coverage between soccer players and their counterparts. Overall, both the literature review and Google analysis yielded firm and significant evidence that the American media’s coverage of soccer is lopsided, and that they do play a role in the sport’s difficulty to become popular in the American mainstream.

This thesis aims to develop a new way to value players for all teams in the MLB, despite the financial disparity. Displayed in the rest of this paper, is a player valuation model created around each team's salary level, focusing on the player’s offensive output. The model functions in a way that values players by their ability to help their team score runs and win games by setting parameters for salary expectations based on player performance. This allows for small market MLB teams, like the Cleveland Guardians, to build a roster of players around their specific salary limit, specifically to score the maximum runs and win games. On the contrary, the model also works for big market teams, like the Los Angeles Dodger, allowing them to project their larger salary limit to players and build their ideal roster as well.