No abstract

Major League Baseball, a professional baseball league in the US and Canada, is one of the most popular sports leagues in North America. Partially because of its popularity and the wide availability of data from games, baseball has become the subject of significant statistical and mathematical analysis. Pitch analysis is especially useful for helping a team better understand the pitch behavior it may face during a game, allowing the team to develop a corresponding batting strategy to combat the predicted pitch behavior. We apply several common machine learning classification methods to PITCH f/x data to classify pitches by type. We then extend the classification task to prediction by utilizing features only known before a pitch is thrown. By performing significant feature analysis and introducing a novel approach for feature selection, moderate improvement over former results is achieved. Count (number of balls and strikes in the current at bat) is often cited as a basis for decisive strategy.