“When you allow kids to collect data on the football field or basketball court, you don’t know who will jump the highest.” John Drazan - STEM Director, 4th Family, Inc., Albany, NY
Career opportunities in sports data science are rapidly growing as sports organizations rely on data-driven decision-making to gain a competitive edge. To prepare for a career in this field, you can start laying the foundation during high school by focusing on certain subjects and skills.
Here's a step-by-step guide:
High School Level Preparation:
College Level Education:
Career Opportunities in Sports Data Science:
Sports data science is highly competitive, so continuous learning and staying updated with the latest tools and techniques are essential for success. Building a strong educational foundation and gaining practical experience will be key to securing opportunities in this exciting and evolving industry.
Curricula include courses such as Innovation Through New Technologies, Project Cost-Benefit Analysis, and an Evaluation of Quantitative Methods. Some courses will focus on strategic management and consultation topics within the sports analytics field and electives, including expanding on qualitative methods, health care, and evaluation theories.
Example: Sports Performance Analytics Specialization (University of Michigan)
Foundations of Sports Analytics: Data, Representation, and Models in Sports
Use 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 also enables the learner to apply them to sports datasets of interest so that they can generate their results rather than relying on the data processing performed by others. As a consequence, the learners will be empowered to explore their ideas about sports team performance, test them out using the data, and so become a producer of sports analytics rather than a consumer.
Moneyball and Beyond
The book Moneyball triggered a revolution in the analysis of performance statistics in professional sports by showing that data analytics could be used to increase team winning percentages. This course shows how to program data using Python to test the claims that lie behind the Moneyball story and to examine the evolution of Moneyball statistics since the book was published. The learner is led through calculating baseball performance statistics from publicly available datasets. The course progresses from the analysis of on-base percentage and slugging percentage to more advanced measures derived using the run expectancy matrix, such as wins above replacement (WAR). By the end of this course, the learner will be able to use these statistics to conduct their own team and player analyses.
Prediction Models with Sports Data
Learn 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 modeling past results and then using the model to forecast the outcome of 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 with sports betting, including the personal risks.
Wearable Technologies and Sports Analytics
Sports analytics now include massive datasets from athletes and teams that quantify training and competition efforts. Wearable technology devices are being worn by athletes every day and provide considerable opportunities for an in-depth look at the stress and recovery of athletes across entire seasons. The capturing of these large datasets has led to new hypotheses and strategies regarding injury prevention and detailed feedback for athletes to try and optimize training and recovery.
This course introduces wearable technology devices and their use in training and competition as part of the larger field of sports sciences. It includes an introduction to the physiological principles that are relevant to exercise training and sports performance and how wearable devices can be used to help characterize both training and performance. It includes access to some large sports team datasets and uses programming in Python to explore concepts related to training, recovery, and performance.
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.