Sport Analytics, MS

Contact:

Rodney J. Paul
Director of the Sport Analytics Program
300 MacNaughton Hall #321
315.443.8165
paul01@syr.edu

Program Description

The Master of Science degree in Sport Analytics is a 36-credit online degree program that provides training in the area of sport analytics. Each course in the degree is 8-weeks in length and delivered online in an asynchronous format.

The degree requires the completion of 10 required core courses, which cover basic and advanced visualization skills, coding in R and Python, database and SQL usage, linear regression, and machine learning techniques using data directly related to either the team/player or business side of the sport industry. In addition to the required courses, students choose two elective courses which concentrate on analytics applications related to collective bargaining agreements for various sports.

Admissions

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 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, and have at least three years of sport industry experience.

Applications will be submitted online and will include:
1) Official transcripts from each institution attended.
2) GPA and Test Scores - A GPA of 3.00 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.

Transfer Credit

Transfer credit is not accepted for this program.

Part-time Study

Given the sequencing of courses in the degree, part-time study is not available.

Satisfactory Progress

Each course must be passed to move along to the next course in the sequence. A minimum semester GPA of 2.5 must be maintained. An overall 3.0 cumulative GPA is required for graduation.

Learning Outcomes

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

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

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

3. Demonstrate 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.

5. Explain the use of analytical tools to specific aspects of the sport industry including finance, law, communications, management, and sport science.

Required Courses - 30 credits:

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)
SAL 605 - R for Sport Analytics II 3 credit(s)
SAL 606 - Applications of Machine Learning for Sport Analytics using Python 3 credit(s)
SAL 607 - Econometrics for Sport Analytics II 3 credit(s)
SAL 608 - Applications of Machine Learning for Sport Analytics Using R 3 credit(s)
SAL 611 - Sport Law and Analytics 3 credit(s)
SAL 621 - Sport Gambling and Analytics 3 credit(s)

Elective Courses - 6 credits required - choose 2 courses from the list below:

SAL 612 - Baseball’s Collective Bargaining Agreement Analytics Applications 3 credit(s)
SAL 613 - Football Analytics Applications 3 credit(s)
SAL 614 - Basketball and Analytics Applications 3 credit(s)
SAL 615 - Hockey and Analytics Applications 3 credit(s)