Hello, my name is Curtis Schaefer. I'm a sophomore at the University of Wisconsin-Madison, triple-majoring in Mathematics, Economics, and Data Science. My passion lies in utilizing analytics for predictive insights. This began in high school when I developed a game-theoretic model to predict changes in MLB pitcher behavior due to new rules. In college, I've continued solo data projects, including using a random forest model to predict baseball MVP voting and a set cover algorithm to address a challenge in the game 'Immaculate Grid.' These projects have not only honed my analytical skills but also affirmed my belief in the power of data to forecast outcomes in sports and beyond. I'm eager to apply this analytical acumen and passion for sports analytics in a role where data-driven insights shape strategic decisions. Check out my twitter for baseball analytics!