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.

Legal issues facing the sports industry related to contracts, antitrust, arbitration, etc., and the various dilemmas they create are explored. Students will research and use analytics, offer analysis, and discuss possible resolutions to specific dilemmas.

The sport of baseball and its Collective Bargaining Agreement (CBA), which describes the rules and regulations governing both the business behind and playing of the sport, are explored with analytics applications related to key concepts.

The sport of football and its Collective Bargaining Agreement (CBA), which describes the rules and regulations governing both the business behind and playing of the sport, are explored with analytics applications related to key concepts.

The sport of basketball and its Collective Bargaining Agreement (CBA), which describes the rules and regulations governing both the business behind and playing of the sport, are explored with analytics applications related to key concepts.

The sport of hockey and its Collective Bargaining Agreement (CBA), which describes the rules and regulations governing both the business behind and playing of the sport, are explored with analytics applications related to key concepts.

The theory and practice of the sports betting and daily fantasy marketplace are studied. Topics include market efficiency, risk assessment, line movements, portfolio analysis, and momentum trading. Analytical applications are presented and explored throughout.

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.