Data Analysis of the Arbitration Process in the MLB
Alex Riles
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The arbitration process in Major League Baseball is used to prevent holdouts and prolonged disputes. The ability to predict the outcome of the hearing will help the players and teams know where they stand. This could change the amount a player or team submits, so the outcome could turn in their favor. This project used Neural Network and Random Forests models to investigate whether a predictive model is worth using to predict the outcome of arbitration. Results indicated that Neural Network and Random Forests models can be used to predict the outcome of the arbitration process with a low misclassification rate.
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Modelling Baseball Players’ Likelihood of Being Inducted into the Hall of Fame
Connor Demorest
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I used logistic regression to model the probability a player will be inducted into the Hall of Fame. I found that my model has 96% accuracy when cross validating on players already in the Hall of Fame, and I discuss the implications of players who are still playing or not yet eligible. I determined what characteristics were most important to Hall of Fame voters and which were not. I identified several players who are not in the Hall of Fame yet and make a case for them to be inducted.
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