BaseballDataScience.com leverages a variety of analytical techniques to explore America’s pastime. Baseball is a ripe subject for in-depth statistical analysis, including machine learning, for a variety of reasons. First, the game represents a semi-controlled system: rules dictate play, and laws of nature govern a range of plausible performance. Some outcomes, therefore, can theoretically be predicted with some level of accuracy. Second, a vast wealth of baseball data exists and is (mostly) freely available, an almost ideal condition for conducting analysis.

Baseball Machine Learning – Beyond Statistics

Many baseball fans have seen the outstanding movie Moneyball. The analysis in Moneyball is largely descriptive, focusing on accurately valuing players, an incredibly worthwhile pursuit. However, machine learning can be employed to answer some potentially more challenging questions: Will a pitcher surrender a run? Who will enter the Hall of Fame? Which teams will win today? Being able to answer these questions with some degree of accuracy has major implications for teams, fans, and fantasy baseball.

Baseball Data Analysis – Finding Patterns

Machine learning can be incredibly informative when applied to baseball data, though less complex forms of analysis are also highly valuable. Finding similarities among players and teams, detecting anomalies, and uncovering unexpected discrepancies in performance can enhance our understanding of the game’s many nuances.

Baseball Data Science – Striving to Enhance Understanding

The purpose of BaseballDataScience.com is to contribute to the large body of work in both sabermetrics and applied data science. Employing a variety of statistical methodologies to focused data sets can help us, broadly, better understand their application and America’s greatest game.