STSCI 4881

Sports Analytics

Course Description

Course information provided by the 2025-2026 Catalog.

This course introduces statistical thinking and data analysis in the context of sports. Students learn to explore, visualize, and model sports data to evaluate performance, predict outcomes, and support strategic decisions. Emphasis is placed on the distinctive features of sports data, including player metrics, event outcomes, and temporal or spatial patterns, as well as on applying statistical and computational tools to real-world problems. Topics include exploratory data analysis, simulation, confidence intervals, hypothesis testing, resampling methods, regression, and introductory machine learning techniques such as decision trees, random forests, and clustering. By the end of the course, students will be able to interpret models, assess performance, and communicate data-driven insights in the sports domain. Students are expected to have proficiency in at least one computer language or software package capable of statistical analysis (e.g., R, Python, Stata, or MATLAB). A working understanding of basic probability, statistics, and a familiarity with linear regression, properties of the normal distribution, and common types of errors.

Prerequisites CS 1110, CS 1112 or STSCI 2110.

Last 4 Terms Offered (None)

Learning Outcomes

• Analyze sports datasets using statistical and computational methods to identify patterns, relationships, and factors influencing performance and outcomes.
• Evaluate and compare statistical and machine learning models, interpreting their assumptions, accuracy, and practical implications for decision-making in sports contexts.
• Create clear, data-driven visualizations and written analyses that effectively communicate insights and support evidence-based recommendations in sports analytics.

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