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Baseball Data Science – Exploring America's Pastime through Algorithms, Visualizations, and Game Theory

Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Scraping, Cleaning, and Predicting Pitches. March 28, 2017. Are you interested in using Python to scrape, clean, and analyze baseball data? Some of the most interesting baseball data, in my opinion, [.]. Predicting Tomorrow’s Outcome. February 26, 2017. Predicting the outcome of future baseball games is notoriously difficult. Some common methodologies are slightly better than a coin flip. If you always [.]. February 6, 2017.

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Baseball Data Science – Exploring America's Pastime through Algorithms, Visualizations, and Game Theory | baseballdatascience.com Reviews

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Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Scraping, Cleaning, and Predicting Pitches. March 28, 2017. Are you interested in using Python to scrape, clean, and analyze baseball data? Some of the most interesting baseball data, in my opinion, [.]. Predicting Tomorrow’s Outcome. February 26, 2017. Predicting the outcome of future baseball games is notoriously difficult. Some common methodologies are slightly better than a coin flip. If you always [.]. February 6, 2017.

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Baseball Data Science – Exploring America's Pastime through Algorithms, Visualizations, and Game Theory | baseballdatascience.com Reviews

https://baseballdatascience.com

Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Scraping, Cleaning, and Predicting Pitches. March 28, 2017. Are you interested in using Python to scrape, clean, and analyze baseball data? Some of the most interesting baseball data, in my opinion, [.]. Predicting Tomorrow’s Outcome. February 26, 2017. Predicting the outcome of future baseball games is notoriously difficult. Some common methodologies are slightly better than a coin flip. If you always [.]. February 6, 2017.

INTERNAL PAGES

baseballdatascience.com

1

Which Teams are Most Similar Offensively? – Baseball Data Science

http://www.baseballdatascience.com/which-teams-are-most-similar-offensively

Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Which Teams are Most Similar Offensively? February 6, 2017. February 6, 2017. This is a short post about finding similarities among the offenses of MLB teams. To accomplish this aim, I used two statistical methodologies that work on distance measures. As always, code can be found on GitHub. Exploring 2016 Pitching Data. Predicting Tomorrow’s Outcome. Leave a Reply Cancel reply. Your email address will not be published.

2

March 2017 – Baseball Data Science

http://www.baseballdatascience.com/2017/03

Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Scraping, Cleaning, and Predicting Pitches. March 28, 2017. Are you interested in using Python to scrape, clean, and analyze baseball data? Some of the most interesting baseball data, in my opinion, [.]. Scraping, Cleaning, and Predicting Pitches. Predicting Tomorrow’s Outcome. Which Teams are Most Similar Offensively? Exploring 2016 Pitching Data. Can Latent Class Analysis Identify Chris Sale’s Pitches?

3

July 2016 – Baseball Data Science

http://www.baseballdatascience.com/2016/07

Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Inaugural Blog: Eric Hosmer’s Statcast Data. July 22, 2016. July 22, 2016. Welcome to baseballdatascience.com! This site combines two of my passions: baseball and data science. I look forward to (hopefully) bringing you fresh, quantitative-based viewpoints [.]. Scraping, Cleaning, and Predicting Pitches. Predicting Tomorrow’s Outcome. Which Teams are Most Similar Offensively? Exploring 2016 Pitching Data.

4

Predicting Tomorrow’s Outcome – Baseball Data Science

http://www.baseballdatascience.com/predicting-tomorrows-outcome

Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Predicting Tomorrow’s Outcome. February 26, 2017. Predicting the outcome of future baseball games is notoriously difficult. Some common methodologies are slightly better than a coin flip. If you always pick the home team as the winner, you’ll be correct about 53% of the time. The Vegas betting predictions are slightly better, with about 57% accuracy. Opponent pitcher throwing arm. Current game opponent: White Sox, Reds, Brew...

5

September 2016 – Baseball Data Science

http://www.baseballdatascience.com/2016/09

Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Exploring 20 Years of Baseball Data (and Predicting Attendance). September 25, 2016. Welcome back, readers! Are you ready for some more baseball data science fun? This blog has two main components: 1) exploring aggregated data [.]. Anomaly Detection on Offensive Statistics. September 11, 2016. September 11, 2016. Clustering (Almost) Every MLB Team…Ever. September 2, 2016. September 2, 2016. Welcome to blog number three!

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Baseball Data Science – Exploring America's Pastime through Algorithms, Visualizations, and Game Theory

Exploring America's Pastime through Algorithms, Visualizations, and Game Theory. Scraping, Cleaning, and Predicting Pitches. March 28, 2017. Are you interested in using Python to scrape, clean, and analyze baseball data? Some of the most interesting baseball data, in my opinion, [.]. Predicting Tomorrow’s Outcome. February 26, 2017. Predicting the outcome of future baseball games is notoriously difficult. Some common methodologies are slightly better than a coin flip. If you always [.]. February 6, 2017.

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