Sports analytics is the use of data and statistical analysis to improve sports performance and decision-making. Online courses in sports analytics can provide students with the knowledge and skills needed to work in this field and use data to analyze sports performance and make informed decisions.
Some of the topics that may be covered in a sports analytics course include:
- Data collection and management in sports
- Statistical analysis and data visualization techniques
- Performance metrics and their interpretation
- Predictive modeling and machine learning in sports
- Sports analytics software and tools
- The use of sports analytics in decision-making
- Ethics and professionalism in sports analytics
Sports analytics courses may be suitable for individuals who are interested in pursuing a career in sports analytics, or for professionals working in the sports industry who want to expand their knowledge in this area. Before enrolling in a sports analytics course, it’s a good idea to research the course content and instructor to make sure that it aligns with your goals and interests. You may also want to consider whether the course is accredited by a professional organization, such as the Sports Analytics Association (SAA). Completing an accredited course may help you meet the requirements for certification or licensure in your region.
1. Sports Performance Analytics Specialization
Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball. Analysis of team and player performance data has continued to revolutionize the sports industry on the field, court, and ice as well as in living rooms among fantasy sports players and online sports gambling.
This introduction to the field of sports analytics is designed for sports managers, coaches, physical therapists, as well as sports fans who want to understand the science behind athlete performance and game prediction.
Course Highlights:
Course Duration: 6 hours/week
2. Sports Analytics: Data-Driven Decision Making
Analytics have revolutionized the sport, providing a competitive advantage to organizational decision-making both on and off the field. This course introduces best practices utilized in sports business analytics. The course touches on data collection, fact-finding, visualization, and metrics that guide strategic decision-making in the sports industry. This course will touch on many aspects of the sports industry, including professional sports.
Course Highlights:
- Identify concepts in sports analytics.
- Understand the benefits and objectives of sports analytics.
- Discuss the impact of analytics on sports.
- Demonstrate insight into the strategies and concepts of sports analytics.
Course Duration: 4 weeks (3-5 hours/week)
3. Data Science for Sports – Sports Analytics and Visualization
This course provides insights and knowledge into how you can perform analysis on sports data and then, visualize it using Python. We will start the course by looking at the games in the 2018 NFL season. Then, we will move on to look at the player statistics in order to understand the players in the season.
After completing this course, you will be able to play around with the various available datasets and visualize them in different ways. This course contains hands-on exercises at the end of each lecture and the knowledge you gain through this course can be extended to any other domain of sports.
Course Highlights:
- Learn how to perform analysis of different kinds of sports data using the 2018 NFL season data.
- Learn how to visualize sports statistics.
- Learn how to create a sports field and visualize players on top of it.
- Learn how to standardize sports data.
Course Duration: 1 hour
4. How to Analyze a Football Match
In this course, we will look at football and break it down to give anyone a comprehensive understanding of the game. This course is perfect for someone who is thinking about going into coaching, management, or analytics. This course is also perfect for football lovers who want to be better informed and have a better understanding of the game! Soccer or football is the greatest game and understanding tactics developing so quickly it is important to have a good foundation.
Course Highlights:
- Identify and understand the phases of play in a match
- Identify and understand the thirds of the field
- Identify and understand the five vertical corridors
- Understand how teams develop a coherent way of playing
Course Duration: 3 hours
5. Sports Management: The Essentials Course
As the sports industry has gotten more professional, the need for effective sports management has grown. Sports have become more competitive than ever, and the margins for success are normally slim. This means that effective management is more important than ever, as it can make the difference between success and failure at the highest levels of sports. In the future, as the impact of technology and management practices grows, executives with a deep understanding of sports management will be in high demand. By learning about how the principles of management relate to the sports industry, you’ll be preparing yourself for a successful career in the world of sports.
Course Highlights:
- · The principles of management
- The media’s importance in the sports industry
- Why financial management and financial reports are so important
- Where sports financing comes from and the risk management that comes with it
- The importance of data analytics in modern sport
Course Duration: 4.5 hours
6. The Complete Course on Sports Sponsorship
As the sports industry has rapidly expanded in the last few years, so have sports sponsorships. The arrival of new technologies such as the radio, tv and then the internet have all brought fans closer to the sport and given sports marketers new ways to reach fans. Sports have gotten saturated by marketing over time, and brands need to recognise that there is more to sponsorships than just buying views.
A smarter approach to sponsorships needs to be taken to ensure that deals are being used effectively. Activating a sponsorship has become just as important as the sponsorship itself and brands that carefully plan out their sponsorships are much more likely to get a better return on their investment.
Course Highlights:
- The process of sports merchandising and how brands can deal with counterfeiting
- How can a brand conduct market research the improve the effectiveness of their sponsorship?
- What factors can lead to a sponsorship being unsuccessful?
- How technology is helping to improve sponsorships
- In what ways is globalisation changing the sports industry and affecting the way sponsorships work?
- How social media is affecting the world of sports sponsorship
Course Duration: 3 hours
7. Foundations of Sports Analytics
This course outlines the fundamental principles and key methodologies relevant to sports analytics problems. The course charts the significance of workflow and methodological structure in data analysis. Then, the importance of complementing human insight with quantitative methods is covered. We will delve into the quantitative aspects by exploring the nature of descriptive and predictive analytics. Last, we will discuss the role and importance of analytics as an entrepreneurial tool. Students will learn to identify critical parameters of different analytics questions, understand how to analyze and interpret patterns using various measurement techniques, conduct statistical analyses, and quantify objective relationships present in data.
Course Highlights:
- The ability to demonstrate a basic understanding of analytic techniques, ranging from descriptive analytics to predictive and prescriptive techniques employed by advanced sports analytics professionals.
- The ability to describe the advantages and weaknesses of the use of quantitative analytic techniques vs. qualitative analysis.
- The ability to identify best practices for integrating analytic information into larger decision-making processes.
- The ability to design and conduct research projects using data and modeling techniques to improve on-field and off-field performance.
Course Duration: Self Paced
8. Wearable Technologies and Sports Analytics
Sports analytics now include massive datasets from athletes and teams that quantify both training and competition efforts. This course is an introduction to wearable technology devices and their use in training and competition as part of the larger field of sport sciences. It includes an introduction to the physiological principles that are relevant to exercise training and sport performance and how wearable devices can be used to help characterize both training and performance. It includes access to some large sport team datasets and uses programming in python to explore concepts related to training, recovery and performance.
Course Highlights:
Course Duration: 28 hours
9. Foundations of Sports Analytics: Data, Representation, and Models in Sports
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. 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.
Course Duration: 49 hours
10. 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.
Course Highlights:
Course Duration: 12 hours
