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Baseball is a very strategic and abstract game, but the baseball world is strangely obsessed with statistics. Modern mainstream statisticians often study offensive data, such as batting average or on-base percentage, in order to evaluate player performance. However, this project observes the game from the opposite perspective: the defensive side of the game. In hopes of analyzing the game from a more concrete perspective, countless mathemeticians - most famously, Bill James - have developed numerous statistical models based on real life data of Major League Baseball (MLB) players. Large numbers of metrics go into these models, but what this project attempts to quantify specifically how positioning of defensive players in baseball ultimately affects the success of those players. This consults the use of multivariate probability distributions and models that allow us to study how good players really are based on data.
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Hawke, Christopher John Jr., "Quantifying the Effect of The Shift in Major League Baseball" (2017). Senior Projects Spring 2017. 191.
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