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Youngho Park
and
Stefan Szymanski
In this course the learner will be shown how to generate forecasts of game results in professional sports using Python. The main emphasis of the course is on teaching the method of logistic regression as a way of modeling game results, using data on team expenditures. The learner is taken through the process of modeling past results, and then using the model to forecast the outcome games not yet played. The course will show the learner how to evaluate the reliability of a model using data on betting odds. The analysis is applied first to the English Premier League, then the NBA and NHL. The course also provides an overview of the relationship between data analytics and gambling, its history and the social issues that arise in relation to sports betting, including the personal risks.
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What's inside
Syllabus
Week 1
This module introduces the regression models in dealing with the categorical outcome variables in sport contest (i.e., Win, Draw, Lose). It explains the Linear Probability Model (LPM) in terms of its theoretical foundations, computational applications, and empirical limitations. Then the module introduces and demonstrates the Logistic Regression as a better substitute of LPM for the categorical dependent variables.
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Syllabus
Week 1
This module introduces the regression models in dealing with the categorical outcome variables in sport contest (i.e., Win, Draw, Lose). It explains the Linear Probability Model (LPM) in terms of its theoretical foundations, computational applications, and empirical limitations. Then the module introduces and demonstrates the Logistic Regression as a better substitute of LPM for the categorical dependent variables.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Demystifies soccer gambling odds, bringing clarity to a complex topic
Exposes the secrets of building predictive sports analytical models
Arms learners with in-demand skills in sports analytics
Provides an overview of the historical and ethical dimensions of sports gambling
Equips learners to make informed decisions in the world of sports betting
Introduces learners to logistic regression, providing hands-on experience in the process
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Reviews summary
Forecasting sports outcomes with data models
According to students, this course offers a largely positive experience, particularly for those looking to apply logistic regression to real sports data. Learners praise the clear explanations of complex models and the practical, hands-on Python exercises using data from leagues like the EPL and NBA. A standout feature is the unique focus on evaluating model reliability against betting odds and the thought-provoking module on gambling ethics and social issues. Some reviewers noted that a prior understanding of statistics and Python is beneficial, as the pace can be quick for absolute beginners, and some coding explanations could be more detailed.
A unique module explores the social and ethical aspects of sports gambling.
"The Week 5 module on gambling ethics was a unique and thought-provoking addition, providing a well-rounded perspective."
"The ethical discussion was also very relevant given the current landscape."
"I felt the last week on gambling was a bit of a departure from the main technical focus."
"The inclusion of the gambling section is interesting but feels somewhat disconnected from the primary goal of model building."
Utilizes Python for hands-on exercises, a practical skill for data science.
"The Python examples were practical and easy to follow. I learned a lot about how to use Python for this kind of analysis."
"The balance between theory and practical coding was perfect."
"Some coding parts could use more detailed explanations for beginners, but generally, it's a valuable skill-building experience."
Offers clear, well-structured explanations of complex statistical models.
"The explanations of logistic regression were incredibly clear, especially how it applies to sports."
"The instructor's delivery is engaging, and the content builds logically."
"The explanations are rigorous yet understandable, and the practical application makes it immensely valuable."
Applies models to real sports data for highly relevant predictions.
"The hands-on approach with real EPL and NBA data. Highly recommend for anyone interested in sports analytics."
"The application of ordered logit models to real betting odds was fascinating and incredibly insightful."
"I was able to apply what I learned to my own projects almost immediately."
"The focus on specific leagues like EPL, NBA, NHL was great for real-world context."
Course is best for learners with some statistical or programming background.
"Some parts felt a bit fast-paced if you're not already comfortable with statistical concepts."
"I found some of the statistical explanations to be quite dense. It probably helps if you have a strong math background."
"As someone new to both Python for data and advanced stats, I found myself overwhelmed. Maybe this is better for intermediate learners."
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 Prediction Models with Sports Data with these
activities:
Review Concepts of Probability
Show steps
Boost your understanding of probability, a core concept for this course.
Show steps
-
Review notes or textbooks on basic probability concepts, such as random variables, probability distributions, and conditional probability.
-
Solve practice problems to reinforce your understanding.
-
Attend a refresher workshop or online tutorial on probability.
Utilize Logistic Regression Tutorials
Show steps
Enhance your proficiency in logistic regression, the primary technique used in this course for game result modeling.
Browse courses on
Logistic Regression
Show steps
-
Identify and enroll in online tutorials or courses that provide a comprehensive overview of logistic regression.
-
Follow along with the tutorials, practicing the concepts and techniques as you progress.
-
Complete the exercises and assignments associated with the tutorials to test your understanding.
Assist at a Sports Analytics Event
Show steps
Gain practical experience and expand your network by volunteering at a sports analytics event.
Browse courses on
Sports Analytics
Show steps
-
Identify upcoming sports analytics conferences, workshops, or industry events.
-
Reach out to the organizers and express your interest in volunteering.
-
Assist with various tasks such as registration, speaker support, or data collection during the event.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Forecasting Game Outcomes
Show steps
Put your knowledge of logistic regression into practice by forecasting game outcomes.
Browse courses on
Sports Analytics
Show steps
-
Gather data on historical game results and other relevant variables, such as team statistics and betting odds.
-
Apply the logistic regression model learned in the course to forecast the outcomes of upcoming games.
-
Compare your forecasts to actual outcomes to evaluate the accuracy of your model.
Develop a Betting Model Presentation
Show steps
Solidify your understanding of betting markets and their relationship with data by creating a presentation on betting models.
Show steps
-
Research different types of betting models and their applications in sports betting.
-
Develop a betting model of your own, outlining the variables considered, the methodology used, and the potential returns.
-
Create a presentation that clearly explains your model, its strengths and limitations, and its potential impact on betting strategies.
Develop a Sports Analytics Dashboard
Show steps
Showcase your data visualization and analysis skills by creating a comprehensive sports analytics dashboard.
Browse courses on
Data Visualization
Show steps
-
Identify a specific sport, league, or team to focus on.
-
Collect and prepare relevant data, including player statistics, team performance, and historical trends.
-
Design and develop a dashboard that visually represents the data, providing insights and enabling data-driven decision-making.
Review Concepts of Probability
Show steps
Boost your understanding of probability, a core concept for this course.
Show steps
Show steps
Show steps
- Review notes or textbooks on basic probability concepts, such as random variables, probability distributions, and conditional probability.
- Solve practice problems to reinforce your understanding.
- Attend a refresher workshop or online tutorial on probability.
Utilize Logistic Regression Tutorials
Show steps
Enhance your proficiency in logistic regression, the primary technique used in this course for game result modeling.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify and enroll in online tutorials or courses that provide a comprehensive overview of logistic regression.
- Follow along with the tutorials, practicing the concepts and techniques as you progress.
- Complete the exercises and assignments associated with the tutorials to test your understanding.
Assist at a Sports Analytics Event
Show steps
Gain practical experience and expand your network by volunteering at a sports analytics event.
Browse courses on
Sports Analytics
Show steps
Show steps
Show steps
- Identify upcoming sports analytics conferences, workshops, or industry events.
- Reach out to the organizers and express your interest in volunteering.
- Assist with various tasks such as registration, speaker support, or data collection during the event.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Forecasting Game Outcomes
Show steps
Put your knowledge of logistic regression into practice by forecasting game outcomes.
Browse courses on
Sports Analytics
Show steps
Show steps
Show steps
- Gather data on historical game results and other relevant variables, such as team statistics and betting odds.
- Apply the logistic regression model learned in the course to forecast the outcomes of upcoming games.
- Compare your forecasts to actual outcomes to evaluate the accuracy of your model.
Develop a Betting Model Presentation
Show steps
Solidify your understanding of betting markets and their relationship with data by creating a presentation on betting models.
Show steps
Show steps
Show steps
- Research different types of betting models and their applications in sports betting.
- Develop a betting model of your own, outlining the variables considered, the methodology used, and the potential returns.
- Create a presentation that clearly explains your model, its strengths and limitations, and its potential impact on betting strategies.
Develop a Sports Analytics Dashboard
Show steps
Showcase your data visualization and analysis skills by creating a comprehensive sports analytics dashboard.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a specific sport, league, or team to focus on.
- Collect and prepare relevant data, including player statistics, team performance, and historical trends.
- Design and develop a dashboard that visually represents the data, providing insights and enabling data-driven decision-making.
Career center
Learners who complete Prediction Models with Sports Data will develop knowledge and skills
that may be useful to these careers:
Sports Analyst
Sports Analyst
A Sports Analyst studies past game results, player performance, and other relevant data to make predictions about future outcomes. This course may be useful for aspiring Sports Analysts, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Sports Analysts.
Data Scientist
Data Scientist
Data Scientists use statistical analysis and modeling to solve business problems. This course may be useful for aspiring Data Scientists, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Data Scientists.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. This course may be useful for aspiring Statisticians, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Statisticians.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for aspiring Quantitative Analysts, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Quantitative Analysts.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course may be useful for aspiring Actuaries, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Actuaries.
Risk Analyst
Risk Analyst
Risk Analysts use statistical analysis and modeling to assess risk and uncertainty. This course may be useful for aspiring Risk Analysts, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Risk Analysts.
Financial Analyst
Financial Analyst
Financial Analysts use statistical analysis and modeling to analyze financial data and make investment decisions. This course may be useful for aspiring Financial Analysts, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Financial Analysts.
Business Analyst
Business Analyst
Business Analysts use statistical analysis and modeling to solve business problems. This course may be useful for aspiring Business Analysts, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Business Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical analysis and modeling to solve business problems. This course may be useful for aspiring Operations Research Analysts, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Operations Research Analysts.
Data Engineer
Data Engineer
Data Engineers design and build systems to store and process data. This course may be useful for aspiring Data Engineers, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Data Engineers.
Software Engineer
Software Engineer
Software Engineers design and develop software applications. This course may be useful for aspiring Software Engineers, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Software Engineers.
Computer Scientist
Computer Scientist
Computer Scientists design and develop software applications. This course may be useful for aspiring Computer Scientists, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Computer Scientists.
Data Analyst
Data Analyst
Data Analysts use statistical analysis and modeling to solve business problems. This course may be useful for aspiring Data Analysts, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Data Analysts.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. This course may be useful for aspiring Machine Learning Engineers, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Machine Learning Engineers.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. This course may be useful for aspiring Artificial Intelligence Engineers, as it provides a foundation in statistical analysis and modeling techniques that are essential for success in the field. The course also covers the relationship between data analytics and gambling, which is a key area of interest for many Artificial Intelligence Engineers.
For more career information including salaries, visit:
OpenCourser.com/course/5zuj7b/prediction
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Prediction Models with Sports Data.
By two of the instructors provides essential background knowledge for the course on the relationship between soccer and gambling, cultural and economic factors for soccer performance, and how odds and predictions can be made.
Provides a good background on prediction and forecasting, and about what makes good models from bad models, which is one of the learning objectives in this course.
Covers a wide range of statistical learning methods, including logistic regression, and useful reference for anyone interested in learning more about the statistical methods used in this course.
Covers a wide range of data science methods used in sports, including logistic regression and other statistical models for prediction.
By one of the instructors provides additional material on the economics of sports, which is important for understanding the context of sports gambling.
Save
Provides a good overview of predictive analytics, which is the general field of study that this course falls under.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5zuj7b/prediction
Share
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Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructors
Youngho Park
Stefan Szymanski
Language
English
Transcripts
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This course belongs to a
collection
called
Sports Performance Analytics. It's comprised of five courses:
- Moneyball and Beyond
- Foundations of Sports Analytics: Data, Representation, and Models in Sports
- Prediction Models with Sports Data (viewing)
- Introduction to Machine Learning in Sports Analytics
- Wearable Technologies and Sports Analytics
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