This course will cover some of the basic tools for introductory sports performance analysis. Topics will include an introduction to data science, collaboration and version control, development operations, and web applications and dashboards, all with sport-specific examples, applications, and case studies.
Learning Outcomes
By the end of this course students should be able to:
- Understand and apply the fundamentals of sports performance analysis, including: player evaluation, prospect assessment, trade analysis, roster construction, asset valuation, and on-field strategy.
|
- Conceptualize the components of the data science workflow and related techniques, as well as the problems to which they apply in amateur and professional sport.
|
- Develop sports data solutions while accounting for practical significance, technical contributions, and data constraints.
|
- Appreciate sports analytics research in action via a number of case studies, such as in player acquisition strategy, personnel decision making, roster value optimization, and on-field performance.
|
- Apply correct programming habits such as extensively commented, well-organized, and thoroughly tested code, utilizing appropriate software libraries.
|