9 min
Machine Learning on Opening Day Data-Based Projections
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- Technology
In time for opening day of the 2022 Major League Baseball (MLB) season, I discuss the initial results of my Baseball Data Analysis Challenge.
See the extended show notes for links to my input data, my results as a Microsoft Excel file, and my SQL scripts on GitHub.
I used logistic regression machine learning classification models to calculate win probabilities for the Boston Red Sox across nine (9) game metrics, and a Naïve Bayes machine learning classification model to predict individual game wins and losses with an associated probability.
Think you can best my model? Game on! The baseball data analysis challenge continues. Play ball!
Extended Show Notes: ocdqblog.com/dbp
Follow Jim Harris on Twitter: @ocdqblog
Email Jim Harris: ocdqblog.com/contact
Other ways to listen: bit.ly/listen-dbp
In time for opening day of the 2022 Major League Baseball (MLB) season, I discuss the initial results of my Baseball Data Analysis Challenge.
See the extended show notes for links to my input data, my results as a Microsoft Excel file, and my SQL scripts on GitHub.
I used logistic regression machine learning classification models to calculate win probabilities for the Boston Red Sox across nine (9) game metrics, and a Naïve Bayes machine learning classification model to predict individual game wins and losses with an associated probability.
Think you can best my model? Game on! The baseball data analysis challenge continues. Play ball!
Extended Show Notes: ocdqblog.com/dbp
Follow Jim Harris on Twitter: @ocdqblog
Email Jim Harris: ocdqblog.com/contact
Other ways to listen: bit.ly/listen-dbp
9 min