In 2017 alone Major League Baseball organizations spent a combined 492 million dollars on acquiring new, young, talented players through the draft or signing international free agents. The effectiveness of an MLB organization’s scouting department is pertinent to ensuring future success. Due to the highly volatile nature of professional sports, identifying a predictive and measurable statistic(s) associated with success among players would be valuable. Furthermore, a team’s ability to quickly and correctly identify players on their Minor League affiliates that will have a positive impact at the Major League level can give them an advantage over other teams. For this project I wanted to build a Neural Network trained on Minor League batting statistics from the AA level that predicts success in the MLB.
Gow, Alexander, "Using Machine Learning to predict MLB success Based on MILB performance" (2019). IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound. Paper 18.
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