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Introduction to Machine Learning in Sports Analytics

In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs). By the end of the course students will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.

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What's inside

Syllabus

Machine Learning Concepts

This week will introduce the concept of machine learning and describe the four major areas of places it can be used in sports analytics. The machine learning pipeline will be discussed, as well as some common issues one runs into when using machine learning for sports analytics.

Support Vector Machines

In this week students will learn how Support Vector Machines (SVM) work, and will experience these models when looking at both baseball and wearable data. Coming out of the week students will have experience building SVMs with real data and will be able to apply them to problems of their own.

Decision Trees

This week will focus on interpretable methods for machine learning with a particular focus on decision trees. Students will learn how these models work in general, and see special uses of decision trees in combination with regression methods. In this week students will come to better understand how the python sklearn toolkit can be used for a breadth of supervised learning tasks.

Ensembles & Beyond

In this week of the course students will learn how many different models can be used together through ensembles, including the random forest method as a common use, as well as more general methods available in sklearn such as stacking and bagging. By the end of this week students will have a broad understanding of how methods such as SVMs, decision trees, and logistic regression can be used together to solve a problem with increasing performance.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops expertise with the Python scikit-learn (sklearn) toolset, which is standard in data science and machine learning industries

Emphasizes how to predict athletic outcomes, which is very relevant to sports analytics

Emphasizes how to apply methods like support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners

Taught by Christopher Brooks, who has experience in athletic data analytics and expertise with the Python scikit-learn toolkit

Examines data from professional sports leagues like the NHL and MLB, which is highly relevant to sports analytics

Examines data from wearable devices like the Apple Watch and inertial measurement units (IMUs), which is highly relevant to sports analytics

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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 Introduction to Machine Learning in Sports Analytics with these activities:

Review NFL sports data visualizations

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Refamiliarize yourself with data visualization for sports data to ensure there are no knowledge gaps when the course begins.

Browse courses on Data Visualization

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Find several ESPN or NFL.com articles containing data visualizations
Analyze the visualizations to identify axes, scales, and key metrics
Practice interpreting the data presented in the visualizations

Practice Machine Learning Concepts with SKLearn

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Solidify understanding of core machine learning concepts and gain practical experience applying them in Python.

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Review course lectures on machine learning concepts.
Install SKLearn library and set up a Python environment.
Implement various machine learning algorithms using SKLearn (e.g., SVM, decision trees, regression).
Analyze results, evaluate model performance, and identify areas for improvement.

Join a study group to discuss course concepts

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Enhance comprehension through collaborative learning and knowledge exchange with peers.

Browse courses on Machine Learning

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Find or form a study group with other course participants
Schedule regular meetings to discuss course materials
Take turns presenting concepts, leading discussions, and answering questions
Actively listen to others' perspectives and share your own insights

Seven other activities

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Simulate SVM, decision tree models in Python

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Reinforce theoretical concepts by practicing simulating SVM and decision tree models using Python.

Browse courses on Support Vector Machine

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Use a Python library like scikit-learn to simulate SVM models
Experiment with different parameters and kernels to observe how they affect model performance
Simulate decision tree models and explore their interpretability
Compare the performance of SVM and decision tree models on different datasets

Build a Machine Learning Model for Predicting NHL Outcomes

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Apply supervised learning techniques to real-world sports data, enhancing understanding of model building and evaluation.

Browse courses on Machine Learning Applications

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Gather and clean NHL data.
Select appropriate features for model building.
Train and evaluate various machine learning models (e.g., SVM, random forest).
Interpret model results and make predictions on future NHL outcomes.

Follow a tutorial on random forest ensemble methods

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Expand knowledge of ensemble methods by following a tutorial that delves deeper into random forest.

Browse courses on Random Forest

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Find a tutorial that provides a comprehensive overview of random forest
Follow the tutorial step-by-step to understand the concepts and implementation
Apply the learned concepts to practice using random forest for classification or regression tasks

Start a personal project using course concepts

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Reinforce learning by applying course concepts to a self-directed project.

Browse courses on Machine Learning

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Identify a sports analytics problem or topic of interest
Research and gather relevant data
Apply machine learning techniques to analyze the data and develop insights
Present your findings in a clear and concise manner
Reflect on your project and identify areas for improvement

Develop a presentation on sports analytics use cases

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Solidify understanding of sports analytics by researching and presenting on its applications.

Browse courses on Sports Analytics

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Research various use cases of sports analytics in different sports
Identify key metrics and techniques used in each use case
Develop a presentation that clearly explains the use cases and their impact
Practice delivering the presentation to improve communication skills

Contribute to an open-source sports analytics project

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Gain practical experience and deepen understanding by contributing to real-world sports analytics projects.

Browse courses on Sports Analytics

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Identify an open-source sports analytics project on platforms like GitHub
Review the project's documentation and codebase
Identify areas where you can make meaningful contributions
Fork the project, make your changes, and submit a pull request
Collaborate with the project maintainers to refine your contributions

Develop a machine learning model to predict player performance

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Apply course concepts to a practical project, enhancing problem-solving and critical thinking skills.

Browse courses on Machine Learning

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Gather and clean player performance data from various sources
Explore and analyze the data to identify key features and relationships
Select and train a machine learning model to predict player performance
Evaluate the model's performance using metrics like accuracy and ROC AUC
Document the project, including the data, model, and evaluation results

Career center

Learners who complete Introduction to Machine Learning in Sports Analytics will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their understanding of machine learning and statistical analysis to build predictive models that can be used to make decisions and solve problems. Those with an interest in pursuing a career as a Data Scientist may find this course particularly applicable because the course will not only teach students how to apply machine learning techniques to athletic data but how to understand the limitations of these techniques and how to best evaluate their results.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers build and maintain the machine learning models that power many of the products and services we use today. This course can help build a foundation for hopeful Machine Learning Engineers by teaching them the basics of machine learning, as well as how to apply these techniques to real-world data.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians analyze data to help make decisions and solve problems. In recent years, machine learning has become an increasingly important tool for statisticians, so much so that many statisticians are now working as data scientists. This course is particularly helpful for those interested in entering the field of statistics because it provides a strong foundation in the basics of machine learning.

See salaries and explore the career path for Statistician

Sports Analyst

Machine learning is becoming increasingly important in the field of sports analytics, as it can be used to predict player performance, team success, and other outcomes. Those interested in becoming Sports Analysts must learn to apply machine learning techniques to real-world athletic data in order to become effective.

See salaries and explore the career path for Sports Analyst

Sports Data Analyst

Sports Data Analysts use machine learning and other statistical techniques to analyze sports data to understand player performance, team tactics, and other aspects of the game. In this course, students will learn how to apply these techniques to real-world data, including data from professional sports leagues such as the NHL and MLB. Those who aspire to work as Sports Data Analysts, or in a similar role, should take this course.

See salaries and explore the career path for Sports Data Analyst

Data Analyst

Data Analysts use data to solve business problems. Machine learning is a powerful tool that can be used to automate many tasks that are traditionally done by hand, and it is increasingly becoming a required skill for Data Analysts. The course will teach students the basics of machine learning and how to apply these techniques to real-world data. It is particularly valuable for those interested in a career as a data analyst, as it will help them build a strong foundation in the skills needed to be successful in this field.

See salaries and explore the career path for Data Analyst

Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and techniques. This course can help build a foundation for those hoping to enter this field, as it provides a strong introduction to the basics of machine learning.

See salaries and explore the career path for Machine Learning Researcher

Data Engineer

Data Engineers build and maintain the infrastructure that allows data scientists and other data professionals to access and analyze data. Machine learning is increasingly important in this field, as it can be used to automate many tasks that are traditionally done by hand. This course can help aspiring Data Engineers build a foundation in machine learning, as well as how to apply these techniques to real-world data.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. Machine learning is increasingly becoming an important tool for Quantitative Analysts, as it can be used to automate many tasks that are traditionally done by hand. This course can help build a foundation in machine learning, as well as how to apply these techniques to real-world data, which will be valuable for those hoping to enter this field.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve business problems. Machine learning is increasingly becoming an important tool for Operations Research Analysts, as it can be used to automate many tasks that are traditionally done by hand. This course can help provide a foundation in machine learning, as well as how to apply these techniques to real-world data, which will prove helpful for those hoping to enter the field.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use financial data to make investment decisions. Machine learning is increasingly becoming an important tool for Financial Analysts, as it can be used to automate many tasks that are traditionally done by hand. This course can help build a foundation in machine learning, as well as how to apply these techniques to real-world financial data, which will be helpful for those hoping to enter this field.

See salaries and explore the career path for Financial Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. Machine learning is becoming increasingly important in this field because it can be used to improve the performance of software applications. This course can help build a foundation in machine learning for those hoping to work as Software Engineers, giving them the skills needed to develop more effective software applications.

See salaries and explore the career path for Software Engineer

Reading list

We've selected nine 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 Introduction to Machine Learning in Sports Analytics.

Football Analytics with Python and R

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Practical guide to predictive analytics in sports, and is well-suited for professionals and practitioners. As a reference text it provides many case studies and examples. It covers a wide range of topics, including data collection and cleaning, feature engineering, and model selection.

Introduction to Machine Learning in Sports Analytics

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What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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