Predicting Team Advancement in Major League Baseball Postseason Using Borda Count

Abstract

The prediction of sports competition outcomes has long been a topic of interest in academia and among sports enthusiasts. This study focuses on Major League Baseball (MLB) as its research subject, encompassing the years 2020, 2021, and 2022. By employing a set of established evaluation criteria, comprising five pitching and five hitting indicators from previous literature, the regular-season performance of the 30 MLB teams across both leagues (National League and American League) over the three-year period was compiled. Subsequently, a data normalization technique combined with the Borda count concept was proposed to develop a model for forecasting team advancement in the postseason. The predictive accuracy of the model presented in this study for determining MLB postseason qualifiers from 2020 to 2022 fell within the range of 55.6% to 66.7%, akin to models utilizing extensive datasets. Notably, the proposed model is more comprehensible and user-friendly, offering ease of understanding and application for sports enthusiasts and facilitating its potential utilization and dissemination in the sporting community.