Chad Readey

Chad Readey is a self-described “nerd-athlete” currently pursuing a dual degree in Data Science and Computer Science at Northwestern University. As a pitcher on the university’s baseball team, Chad combines the precision of athletics with the analytical depth of data science. His passion lies in transforming complex datasets—like MLB statistics—into meaningful insights. With a strong foundation in logistic regression, machine learning, and data visualization, Chad exemplifies a rare balance of individual focus and team spirit.

Chad Readey - Pursuing a dual degree in Data Science
Chad Readey - Explores the strategic side of baseball

Merging Sports and Data

Baseball Through the Lens of Data

Chad explores the strategic side of baseball by analysing stats, pitch patterns, and player performance.

Making Models Mean Something

Through projects like MLB pitch prediction, Chad turns models into meaningful tools.

 From Fieldwork to Frameworks

Chad is a communicator as much as an analyst.

Vision, Values & The Road Ahead

For Chad, data is more than numbers—it's a powerful tool for driving meaningful impact. He is committed to applying his skills to projects that create tangible value, whether that means optimizing athletic performance, enhancing business strategies, or solving real-world challenges. His goal is to work on initiatives where data can inform smarter decisions, fuel innovation, and ultimately make a difference in people’s lives.

Frequently Asked Questions

Q1: What is Chad Readey studying?

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A: Chad Readey is pursuing a dual bachelor’s degree in Data Science and Computer Science at Northwestern University.

Q2: What is his role on the baseball team?

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A: He is a pitcher for the Northwestern University Division I baseball team.

Q3: What is Chad’s most notable academic project?

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A: Chad built a pitch prediction model using logistic regression to analyze MLB data, achieving impressive classification accuracy.

Q4: How does Chad merge sports and analytics?

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A: He applies statistical and machine learning methods to analyze baseball performance, uncover trends, and predict outcomes with precision.

Q5: What are Chad’s core technical strengths?

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A: His skill set includes logistic regression, data visualization, machine learning, predictive modeling, and Python programming.

Q6: What are Chad’s career goals post-graduation?

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A: He aims to pursue a career that blends data science, sports strategy, and business innovation, ideally in a role that emphasizes purpose and impact.

Q7: What makes Chad stand out?

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A: Chad brings a rare combination of athletic discipline, analytical intelligence, and a team-first mindset—making him equally comfortable on the field, in the lab, or in a boardroom.

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