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- All Subjects: Economics
- All Subjects: Baseball
- Creators: Department of Information Systems
- Resource Type: Text
- Status: Published
In the 21st century economy, life moves pretty fast, and change is happening all around us. For example, it was common to drive to shopping malls with your friends or family and spend the whole afternoon browsing through hundreds of items until you found the perfect purchase. Or, only a few months ago, the entire world was put on lockdown to stop the spread of COVID-19, which caused a recession when consumers stopped spending as much to start saving. Americans also used to enjoy their loud, gas-guzzling cars and trucks to get them from place to place. Now what changed, and why? The study of economics justifies how we, as human, fundamentally live and make choices every day. As we notice the results of our choices, we may continue to do the same the next day, temporarily go another route, or alter our behavior permanently. This framework presents the concept of innovation. By applying this logic to the business world, I will attempt to analyze and defend why the innovations of e-commerce, COVID-19 vaccines, and electric vehicles were the natural cause of society changing perspective to move forward toward a better tomorrow.
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.