This course covers a basic statistics review, visualization techniques in Tableau, a discussion of the Moneyball hypothesis, and an overview of the current state of player/team analytics in different sports.

This course serves as an introduction to R and covers basic coding, data frames, data cleaning and editing, visualization techniques, and basic modeling of data in R. These techniques are taught using sports data.

This course serves as an introduction to Python. Sports data are used in conjunction with NumPy, Pandas, management, cleaning, wrangling, and aggregation. Key strategies of effective use of Python for sports data are discussed.

The course covers linear regression, modeling techniques, interpretation of regression results, diagnostic tests and corrections for econometric issues, logistic regression, and key sport economic insights.

Continued training in coding, webscraping, creating interactive graphics, using dashboards, and combining databases and SQL with R for Sport Analytics. Techniques used include nearest neighbors, classification, trees, and cluster analysis.

Applications of machine learning for sport analytics using Python.  Topics include supervised vs. unsupervised models, clustering, Bayesian networks, component analysis, and neural networks using sports data.

Continued application of econometrics in Sport Analytics, including additional tests for violations of assumptions of CLRM.  Other topics include nonlinear regression, qualitative response models, panel data, and simultaneous equation models and methods.

Applications of Machine Learning for Sport Analytics Using R. Elements of both supervised and unsupervised learning. Key topics include classifier models (KNN, Naïve Bayes), decision trees, clustering, cross validation, bagging, and neural networks.

This course analyzes legal frameworks in sports, including data governance, intellectual property, contracts, constitutional protections, antitrust, and labor relations. Students apply statistical modeling and data visualization to evaluate legal issues. Additional work required for graduate students.

This course examines baseball and its Collective Bargaining Agreement (CBA) through analytics, exploring sabermetrics, economic analysis, and predictive modeling to evaluate performance, labor markets, financial implications, and competitive balance. Additional work required for graduate students.

This course explores professional basketball operations and analytics through the NBA Collective Bargaining Agreement, examining organizational structures, salary cap mechanisms, and contract strategies. Students apply statistical methods to real-world basketball challenges. Additional work required for graduate students.

This course explores hockey and its Collective Bargaining Agreement (CBA), applying analytics to evaluate player performance, salary cap management, roster construction, and competitive balance. Additional work required for graduate students.

This course provides hands-on experience in professional football analytics using R programming. Students will apply advanced data wrangling, visualization, regression modeling, and predictive machine learning techniques with nflverse packages. Additional work required for graduate students.

This course explores advanced soccer analytics, emphasizing expected goals modeling, passing networks, tactical formations, and player valuation. Using R and Python, students build predictive models with professional tracking data from FIFA, UEFA, and MLS. Additional work required for graduate students.

This course focuses on golf analytics methods for analyzing professional performance and strategy including world rankings, driving distance trends, weather impacts, performance streaks, putting optimization, and strokes gained methodologies. Additional work required for graduate students.

Explores the theory and practice of sports betting markets including market efficiency, line movements, momentum trading, money management, Kelly criteria, futures, parlays, and Daily Fantasy Sports across multiple sports. Additional work required by graduate students.

Participation in a discipline or subject related experience. Student must be evaluated by written or oral reports or an examination. Permission in advance with the consent of the department chairperson, instructor, and dean. Limited to those in good academic standing.

Exploration of a problem, or problems, in depth. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor(s) and the department.