Analyzing College Football Conference Realignment using Python: A Data Exploration | Giovanni Malloy | August 2023

Analyzing College Football Conference Realignment using Python: A Data Exploration | Giovanni Malloy | August 2023

Introduction:

Are you ready for some college football? As fall approaches, it’s time to gear up for the excitement and thrill of college sports. Whether you’re a fan of the Big Ten, SEC, or any other conference, there’s nothing quite like the rush of watching your favorite teams battle it out on the gridiron.

But college football isn’t just about the action on the field. It’s also about the off-season buzz and speculation that surrounds the sport. From NIL (Name, Image, and Likeness) debates to transfer portal news, and conference realignment rumors, there’s always something to talk about.

In this four-part blog series, we’ll delve into the world of college football conference realignment, taking a data-driven approach to propose some exciting changes. Using exploratory data analysis, regression, clustering, and node2vec techniques, we’ll analyze various factors such as program performance, rivalries, stadium size, and more to create a compelling realignment plan.

To ensure accuracy and reliability, I’ve compiled a comprehensive dataset from reliable sources across the web. This dataset includes information on each FBS program, historical performance, appearances in the AP top 25 polls, NFL draft picks, program revenue, and more.

Get ready for an exciting journey filled with data and analysis, as we attempt to bring some order and logic to the chaotic world of college football conference realignment. Join me as we explore new data science tools and uncover hidden insights that could reshape the landscape of college sports forever. Stay tuned for the first part of this series, where we’ll dive into exploratory data analysis using Python.

Full Article: Analyzing College Football Conference Realignment using Python: A Data Exploration | Giovanni Malloy | August 2023

Fall is here, and with it comes the excitement of college football season. As a fan of college sports, I have always enjoyed watching the games and following the teams. In recent years, analytics has become more prevalent in sports, and college football is no exception.

The last couple of off-seasons have been filled with news about NIL (Name, Image, Likeness) rights for college athletes, the transfer portal, and conference realignment. It seems like there is always something new happening in the world of college football. One commercial that captures the chaos and excitement of these changes is Dr. Pepper’s “Chaos Comes to Fansville.”

When it comes to conference realignment, there is often a lot of speculation and guesswork involved. Many fans believe that there is a mastermind behind the scenes, crunching numbers and making decisions about which teams should be added to which conferences. While I haven’t had the chance to meet this mysterious figure, I thought it would be fun to propose a data-driven approach to conference realignment.

In this four-part blog series, I will explore different data science tools and techniques that can be used for conference realignment:

1. College Football Conference Realignment – Exploratory Data Analysis in Python: In this first part, I will provide an overview of exploratory data analysis and demonstrate how it can be applied to the field of college football conference realignment.

2. College Football Conference Realignment – Regression: In the second part, I will delve into regression analysis and explain how it can be used to predict and model different factors related to conference realignment.

3. College Football Conference Realignment – Clustering: The third part will focus on clustering techniques and how they can help group teams based on different characteristics, such as performance history and stadium size.

4. College Football Conference Realignment – node2vec: In the final part of the series, I will introduce the node2vec algorithm and show how it can be used to analyze network data and make recommendations for conference realignment.

To conduct this analysis, I have compiled my own dataset from various sources on the web. The dataset includes information about each FBS program, college football rivalries, stadium sizes, historical performance, appearances in AP top 25 polls, institutional affiliations, NFL draft picks, and program revenue.

By using data science tools and techniques, we can gain valuable insights into the world of college football and potentially propose new conference realignment scenarios. Stay tuned for the next part of the series, where I will dive into exploratory data analysis using Python!

(Photo by David Ireland on Unsplash)

Summary: Analyzing College Football Conference Realignment using Python: A Data Exploration | Giovanni Malloy | August 2023

Fall is here, and with it comes college football season. As a fan of college sports, I am excited for the kickoff. Lately, there has been a lot of buzz surrounding topics like NIL, transfer portal, and conference realignment in college sports. It seems like everyone is speculating and relying on gut feelings to predict the outcomes of these changes. But what if we could use data-driven insights to make more informed decisions? In this four-part blog series, I will explore different data science tools to propose a data-driven conference realignment in college football. This post will focus on exploratory data analysis, using a dataset that I created by compiling information from various sources. Stay tuned for more exciting insights in the upcoming posts.

Frequently Asked Questions:

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d) Model building and feature selection: Developing statistical or machine learning models using appropriate algorithms and selecting relevant features for prediction or classification.
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Answer: Ethical considerations are of utmost importance in Data Science due to the potential impact on individuals and society. Some key ethical considerations in Data Science include:
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