Prediction of Postseason Appearance in Major League Baseball by Statistical Analysis and Machine Learning

• Main Authors: Yen-Chieh Wang, 王彥傑
• Other Authors: 鄭士康
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/75332361762159445648

碩士 === 國立臺灣大學 === 電信工程學研究所 === 104 === Major League Baseball (MLB) gathers the top baseball players around the world. It’s the most popular professional baseball league that its fans are worldwide. Every season, the 30 teams of MLB enhance their power to make them qualify the postseason games in October. Moreover, they all hope to win the World Series Championship. Baseball fans and teams would like to know what attributes makes a team go to the postseason games. In the thesis, we first introduce the baseball statistics and the history of MLB postseason system. We adopt the factor analysis, the decision tree, and the support vector machine to analyze what attributes the postseason teams are with. The teams’ statistics from season 1995 to 2015 and whether they made postseason appearances or not are used in these analyses. Result shows that the accuracy of the prediction by these method can reach at least 70%. Fans can use the analysis in the thesis to predict which teams will make postseason appearance in the new baseball season.