Sport Analytics, CAS

Contact:

Rodney J. Paul, Sport Analytics Program Director

300 MacNaughton Hall

315.443.2630

rpaul01@syr.edu

Program Description

The Certificate of Advanced Study (CAS) in sport analytics program requires 12 credit hours covering specific focus areas of sport analytics statistics and methods, visualization tools, programming in R and Python, use of databases and SQL, linear regression and econometric techniques, and machine learning tools. The courses use data and examples exclusively from the sports industry, both on the player/team performance and business aspects of the discipline. It comprises 4 core courses (12 credits) that all students are required to complete.

Admissions

For admission, an applicant must hold a baccalaureate degree from a regionally accredited U.S. institution or a tertiary degree that is deemed to be comparable to a 4-year’s bachelor’s degree from a regionally accredited U.S. institution.

Qualified applicants should have successfully completed an undergraduate or graduate-level course in statistics or show significant experience using statistics in a professional capacity.

Applications will be submitted online and will include:

Official transcripts from each institution attended.
GPA and Test Scores - A GPA of 3.0 or higher on a 4.0 scale in undergraduate studies is required. GRE or GMAT are not required but could be submitted (if completed) on the student’s behalf.

Eligibility requirement: To be awarded a Certificate of Advanced Study (CAS), a student must be matriculated and active in the certificate program for at least one semester prior to graduating. Matriculation may not be backdated.

Transfer credit

The Certificate of Advanced Study in Sport Analytics does not accept transfer credit.

Part-Time Study

The CAS in Sport Analytics is a part-time program. Students complete 12 sequential credits over the course of two semesters. Given the sequential nature of the 4 courses, delaying a sequence is highly discouraged and students must seek approval from the program director.

Satisfactory progress

Graduate students must earn a minimum average of 3.000 for work comprising the program for the degree or certificate.

Total Credits Required

12 credits

Student Learning Outcomes

Upon completion of the program, students will be able to:

1. Explain the different types of statistics used for analysis of player/team performance and business performance across different sports and leagues.

2. Demonstrate proficiency in the visualization of sport analytics data on both the player/team performance-side and business-side of the industry.

3. Integrate the knowledge and skills of effectively coding in R and Python to scrape, clean, and analyze data for sport organizations.

4. Develop critical thinking and model building skills in both econometrics and machine learning for careers in sport analytics.

Required Courses (12 credits)

Courses must be completed sequentially and must be passed with a 3.0 (B) grade or higher in order to move onto the next course. If a student misses a course, they must take that course, the next time it is offered, before taking any subsequent courses in the program.

SAL 601 - Introduction to Sport Analytics and Visualization 3 credit(s)
SAL 602 - Introduction to R for Sport Analytics 3 credit(s)
SAL 603 - Introduction to Databases and Python for Sport Analytics 3 credit(s)
SAL 604 - Linear Regression and Econometrics for Sport Analytics 3 credit(s)