Prediction Models with Sports Data from Coursera

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.

Week 2

This module explores the relationship between probability and betting markets. It explains the concept of odds, and the relationship between betting odds and probabilities. It then develops a measure of the accuracy of betting odds using sports examples, and assesses the meaning of efficiency in betting markets.

Week 3

This module shows how to forecast the outcome of EPL soccer games using an ordered logit model and publicly available information. It assesses the accuracy of these forecasts against the betting odds and shows that they are remarkably accurate.

Week 4

This module assesses the efficacy of the EPL forecasting model covered in the previous week by replicating the model in the context of three North American team sports leagues (i.e., NHL, NBA, MLB). Specifically, this module shows how to forecast the outcome of NHL, NBA, MLB regular season games using an ordered logit model and publicly available information. It assesses the accuracy of these forecasts against the betting odds.

Week 5

In this module we examine the historical and social consequences of gambling, and the relationship between gambling and statistics. Gambling is explored from the perspective of different ethical and religious systems. Issues of problem gambling are explored and assessed.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Practical sports data modeling

According to students, this course is a solid foundation for learning basic modeling techniques in Python and Pandas. Although there are mixed opinions on the difficulty of the course, students appreciate the practical and engaging assignments. The course covers the basics of logistic regression and is recommended for those new to data science coding as well as those with a background in mathematics and statistics.

Practical and engaging assignments

"Excellent course"

"Very interesting course, even though some of the data prep is kind of weird it's nice to see things done a bit differently "

"This course covers basics of modeling in form of logistic regression.The course is worth for those who want hands-on experience/beginning in pandas/python data science coding and those who are already familiar with mathematics & statistics of regression"

Mixed opinions on difficulty

"I found the material from weeks 2 and 4 very interesting!"

"IN GENERAL TERMS I LIKE IT ALL, WITH THE EXCEPT THAT I COULD NOT FINISH THE SPECIALIZED PROGRAM BECAUSE I DID NOT UNDERSTAND THE QUESTIONS OF COURSE NUMBER 5, THE TEACHER ASKS THINGS THAT HE DOESN'T EXPLAIN, AND WHAT IT EXPLAINES DOES NOT DO IT WITH CLARITY !!!"

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

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Boost your understanding of probability, a core concept for this course.

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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

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Enhance your proficiency in logistic regression, the primary technique used in this course for game result modeling.

Browse courses on Logistic Regression

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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

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Gain practical experience and expand your network by volunteering at a sports analytics event.

Browse courses on Sports Analytics

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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

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Practice Forecasting Game Outcomes

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Put your knowledge of logistic regression into practice by forecasting game outcomes.

Browse courses on Sports Analytics

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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

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Solidify your understanding of betting markets and their relationship with data by creating a presentation on betting models.

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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

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Showcase your data visualization and analysis skills by creating a comprehensive sports analytics dashboard.

Browse courses on Data Visualization

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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:

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Business 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.

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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.

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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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Data 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.

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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.

See salaries and explore the career path for Risk Analyst

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.

See salaries and explore the career path for Data Engineer

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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Sports Analyst