Foundations of Sports Analytics: Data, Representation, and Models in Sports from Coursera

This course provides an introduction to using Python to analyze team performance in sports. Learners will discover a variety of techniques that can be used to represent sports data and how to extract narratives based on these analytical techniques. The main focus of the introduction will be on the use of regression analysis to analyze team and player performance data, using examples drawn from the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier LEague (EPL, soccer) and the Indian Premier League (IPL, cricket).

This course does not simply explain methods and techniques, it enables the learner to apply them to sports datasets of interest so that they can generate their own results, rather than relying on the data processing performed by others. As a consequence the learning will be empowered to explore their own ideas about sports team performance, test them out using the data, and so become a producer of sports analytics rather than a consumer.

While the course materials have been developed using Python, code has also been produced to derive all of the results in R, for those who prefer that environment.

What's inside

Syllabus

Introduction to Sports Performance and Data

This week introduces a simple example of sports analytics in practice - the calculation of the Pythagorean expectation to model winning in team sports. This can also be used for the purposes of prediction. Examples are developed for five different sports leagues, Major League Baseball (MLB), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier League (EPL-soccer) and the Indian Premier League (IPL-cricket).

Introduction to Data Sources

This week will use NBA data to introduce basic and important Python codes to conduct data cleaning and data preparation. This week also discusses summary and descriptive analyses with statistics and graphs to understand the distribution of data, the characteristics and pattern of variables as well as the relationship between two variables. At the end of this week, we will introduce correlation coefficients to summarize the linear relationship between two variables.

Introduction to Sports Data and Plots in Python

This module introduces some ways of representing data using examples from MLB, the NBA and Indian Premier League. MLB data is used to analyze the spatial distribution of different hits. NBA data is used to generate heatmaps to illustrate the different ways in which players contribute. IPL data is used to show how team performances can be compared graphically.

Introduction to Sports Data and Regression Using Python

This week introduces the fundamentals of regression analysis. We will discuss how to perform regression analysis using Python and how to interpret regression output. We will use NHL data to estimate multiple regression models to identify the team level performance factors that affect the team's winning percentage. We will also use cricket data from the Indian Premier League to run regression analyses to examine whether player performance impacts player salary.

Well-received sports analytics foundations

Learners say this course is a great foundational course, providing a great introduction and hands-on experience with sports analytics. Students largely enjoyed the engaging and informative lectures and assignments, as well as the opportunity to work with real-world sports datasets. While some reviewers mentioned that the course content needs updating and that assignments sometimes have incomplete instructions, overall, this course is well received by learners.

The course offers learners a chance to apply their knowledge through assignments.

"Great introduction, really make you want to know more"

"I've never been more excited of doing a regression model in my life!"

"The lectures focus on the hands-on application of analytics techniques"

Learners enjoyed the engaging lectures, assignments, and real-world datasets.

"Great foundational course!"

"Really great and informative course, loved the material and the assignments!"

"An excellent way to get hands-on experience exploring sports data in Python/R"

The course content needs updating and some assignments have incomplete instructions.

"The course content was intriguing. However, it definitely needs updating."

"There are times where assignment instructions are incomplete"

"I didn't get a lot out of some of the weeks, as it felt like the instructor was just reading word for word what was written in the notebooks"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Foundations of Sports Analytics: Data, Representation, and Models in Sports with these activities:

Join a study group with other students in the course

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This activity will help ensure that you keep up in class.

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Find a few other students who are taking this course
Meet regularly to discuss the course material
Help each other with homework and assignments

Introduction to using Python for sports analytics

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Familiarize with the basics of Python programming by following a tutorial.

Browse courses on Python

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Find a beginner-friendly tutorial on Python programming
Follow the tutorial step-by-step
Practice writing simple Python programs

Practice converting sports data into useful insights

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This activity will reinforce your skills in importing, cleaning, and exploring sports data using Python.

Browse courses on Data Analysis

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Acquire a dataset of your choice
Import the data into a Python environment
Clean the data by removing duplicates and outliers
Explore the data using descriptive statistics and visualizations

Four other activities

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Show all seven activities

Review 'Predictive Analytics for Sports'

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Gain advanced knowledge in predictive analytics and modeling techniques used in sports.

View An Introductory Handbook of Bayesian Thinking on Amazon

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Read the book
Take notes and highlight important concepts
Apply the concepts to a real-world sports analytics project

Write a blog post on a sports analytics topic

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This activity will allow you to synthesize your knowledge of sports analytics and communicate it effectively.

Browse courses on Sports Analytics

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Choose a specific topic within sports analytics that interests you
Research the topic thoroughly
Write a blog post that explains the topic clearly and engagingly
Share your blog post with others

Participate in a sports analytics hackathon

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This activity will provide you with hands-on experience in applying your skills to a real-world problem.

Browse courses on Data Science

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Find a sports analytics hackathon that interests you
Form a team or work individually
Develop a solution to the hackathon's challenge
Present your solution to a panel of judges

Develop a sports analytics dashboard

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Expand your skills in data visualization and reporting by creating a dashboard that showcases your insights.

Browse courses on Data Visualization

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Identify the key metrics and insights you want to track
Choose a data visualization tool
Develop the dashboard
Deploy the dashboard

Career center

Learners who complete Foundations of Sports Analytics: Data, Representation, and Models in Sports will develop knowledge and skills that may be useful to these careers:

Sports Performance Analyst

Sports Performance Analysts use data to evaluate and improve the performance of athletes and teams. This course provides a comprehensive introduction to using Python for sports data analysis, including data representation, modeling, and regression analysis techniques. Aspiring Sports Performance Analysts will gain valuable hands-on experience in applying these methods to real-world sports data, enabling them to make informed recommendations for improving performance and achieving success.

See salaries and explore the career path for Sports Performance Analyst

Sports Analyst

Sports Analysts use data to evaluate and understand the performance of athletes and teams. This course provides a comprehensive introduction to using Python for sports data analysis, including data representation, modeling, and regression analysis techniques. Aspiring Sports Analysts will gain valuable hands-on experience in applying these methods to real-world sports data, enabling them to make informed decisions and contribute to the improvement of sports performance.

See salaries and explore the career path for Sports Analyst

Statistician

Statisticians collect, analyze, interpret, and present data to help organizations make informed decisions. Sports Analytics is a specialized field of Statistics that focuses on the analysis of sports data to improve performance and outcomes. This course provides a solid foundation in using Python for data analysis, as well as specific techniques for representing and modeling sports data using regression analysis. Statisticians interested in specializing in Sports Analytics will find this course particularly valuable.

See salaries and explore the career path for Statistician

Data Scientist

A Data Scientist collects and analyzes large amounts of data to extract meaningful insights, patterns, and trends. Sports Analytics is a specialized field of Data Science that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Data Scientists can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts collect, clean, and analyze data to help organizations make better decisions. Sports Analytics is a specialized field of Data Analysis that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Data Analysts can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models to solve complex problems. Sports Analytics is a specialized field of Machine Learning that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Machine Learning Engineers can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Sports Analytics is a specialized field of Quantitative Analysis that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Quantitative Analysts can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to help organizations make better decisions. Sports Analytics is a specialized field of Operations Research that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Operations Research Analysts can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Operations Research Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines to ensure that data is available for analysis. Sports Analytics is a specialized field of Data Engineering that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Data Engineers can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts use data to help organizations make better decisions. Sports Analytics is a specialized field of Business Analysis that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Business Analysts can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts use data to help organizations make better financial decisions. Sports Analytics is a specialized field of Financial Analysis that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Financial Analysts can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Financial Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. Sports Analytics is a specialized field of Software Engineering that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Software Engineers can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

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Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. Sports Analytics is a specialized field of Actuarial Science that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Actuaries can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

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Computer Scientist

Computer Scientists design, develop, and analyze computer systems. Sports Analytics is a specialized field of Computer Science that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Computer Scientists can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

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Economist

Economists use data to understand how economies work. Sports Analytics is a specialized field of Economics that applies these techniques to the world of sports. This course provides a solid foundation in the use of Python, a popular programming language for data analysis, and teaches learners how to represent and model sports data using regression analysis. With this knowledge, aspiring Economists can confidently enter this growing field and contribute to the advancement of sports performance analysis and prediction.

See salaries and explore the career path for Economist