Prediction of on-Base Percentage (OBP) of Baseball Players Using Neural Networks

Abstract

This research paper presents a detailed study on the prediction of On-Base Percentage (OBP) of baseball players utilizing neural networks. OBP is a crucial statistic in baseball, measuring how frequently a batter reaches base through hits, walks, and hit-by-pitches, excluding errors, fielder's choices, and dropped catches. Traditional methods of predicting OBP have relied heavily on historical statistics and linear models. This study explores the application of neural networks to improve prediction accuracy. We discuss the existing systems, propose a novel neural network-based approach, and evaluate the performance of the model using real-world data. The results demonstrate significant improvements over traditional methods, highlighting the potential of neural networks in sports analytics. Keywords: Baseball, Neural Networks , Analytics, ML.