47 courses with the subject SAL, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Exploration of a topic (to be determined) not covered by the standard curriculum but of interest to faculty and students in a particular semester.
Analyzing and interpreting sport data through analytical procedures. Current advanced statistics will be introduced, discussed, and computed across a variety of professional and college sports.
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.
Utilizing Python programming Sports Analytics students will collect, clean, and visualize sports data. Students will conduct statistical analyses of collected sports datasets, interpret, and visualize results.
In-depth exploration of a problem or problems. Individual independent study based on a plan submitted by the student. Permission in advance with the consent of the department chairperson, instructor, and dean. Limited to those in good academic standing.
Introduction to elements of sport analytics, research methods and design, applied probability and statistics. Students will conduct research and construct papers using Excel and R to interpret sport statistical analyses.
Exploration of a topic (to be determined) not covered by the standard curriculum but of interest to faculty and students in a particular semester.
Advanced econometric techniques for cross-sectional, time-series and panel (i.e., cross-sectional and time-series) data are covered. Non-linear models are considered using the generalized linear model approach. Techniques will be applied to sport analytic data across sports.
An introduction to computer programing within sport analytics. An emphasis on learning to code, program, package, and use modules within sport. Database management, storage, and debugging techniques will be reviewed for sport industry data.
Introduction to sport science and athletic injury analytics. Complex sport injury cases will be examined and analyzed using scientific articles and on-line resources.
Differentiates structure within analytics departments among multiple professional sports organizations. Prepares students to compete in the baseball analytics based SABR Diamond Dollars Case Competition and attend the SABR Analytics Conference. Travel and field study course.
Analyzing and explaining sport issues through the lens of economic analysis. Theoretical models and applications will be discussed that relates to aspects of professional, collegiate, and amateur sports nationally and internationally.
Advanced analysis of various web scraping techniques with the specific focus of learning how to scrape sports data from various sport websites. Significant time will be spent problem solving and cleaning data from complicated sources.
This course analyzes legal frameworks in sports, including data governance, intellectual property, contracts, constitutional protections, antitrust, and labor relations. Students apply statistical modeling and data visualization to evaluate legal issues. Additional work required for graduate students.
This course examines baseball and its Collective Bargaining Agreement (CBA) through analytics, exploring sabermetrics, economic analysis, and predictive modeling to evaluate performance, labor markets, financial implications, and competitive balance. Additional work required for graduate students.
Analyzing and interpreting sport data through analytical procedures in the software program R. Current advanced statistics and techniques will be covered using a variety of sport databases.
This course explores professional basketball operations and analytics through the NBA Collective Bargaining Agreement, examining organizational structures, salary cap mechanisms, and contract strategies. Students apply statistical methods to real-world basketball challenges. Additional work required for graduate students.
This course explores hockey and its Collective Bargaining Agreement (CBA), applying analytics to evaluate player performance, salary cap management, roster construction, and competitive balance. Additional work required for graduate students.
This course provides hands-on experience in professional football analytics using R programming. Students will apply advanced data wrangling, visualization, regression modeling, and predictive machine learning techniques with nflverse packages. Additional work required for graduate students.
This course explores advanced soccer analytics, emphasizing expected goals modeling, passing networks, tactical formations, and player valuation. Using R and Python, students build predictive models with professional tracking data from FIFA, UEFA, and MLS. Additional work required for graduate students.
This course focuses on golf analytics methods for analyzing professional performance and strategy including world rankings, driving distance trends, weather impacts, performance streaks, putting optimization, and strokes gained methodologies. Additional work required for graduate students.
Explores the theory and practice of sports betting markets including market efficiency, line movements, momentum trading, money management, Kelly criteria, futures, parlays, and Daily Fantasy Sports across multiple sports. Additional work required by graduate students.
Students will study the global impact of sport, history, and business implications on a particular region or country. Analyzing sport from a variety of perspectives: lectures, readings, field trips, and case studies of sport organizations.
Course is the travel portion of SPM/SAL 440. Students enrolled in SPM/SAL440 must register for this zero credit course in the subsequent semester for the required travel portion of the course
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.
In-depth exploration of a problem or problems. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor or instructors and the department.
This course guides Sport Analytics students through the thesis process, from topic selection and literature review to data analysis using empirical models, culminating in written thesis with visualized results.
Second semester in a two semester course sequence for Sport Analytics majors who will complete an original research project within sport analytics.
Completion of an Honors Capstone Project under the supervision of a faculty member.
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.
This course analyzes legal frameworks in sports, including data governance, intellectual property, contracts, constitutional protections, antitrust, and labor relations. Students apply statistical modeling and data visualization to evaluate legal issues. Additional work required for graduate students.
This course examines baseball and its Collective Bargaining Agreement (CBA) through analytics, exploring sabermetrics, economic analysis, and predictive modeling to evaluate performance, labor markets, financial implications, and competitive balance. Additional work required for graduate students.
This course explores professional basketball operations and analytics through the NBA Collective Bargaining Agreement, examining organizational structures, salary cap mechanisms, and contract strategies. Students apply statistical methods to real-world basketball challenges. Additional work required for graduate students.
This course explores hockey and its Collective Bargaining Agreement (CBA), applying analytics to evaluate player performance, salary cap management, roster construction, and competitive balance. Additional work required for graduate students.
This course provides hands-on experience in professional football analytics using R programming. Students will apply advanced data wrangling, visualization, regression modeling, and predictive machine learning techniques with nflverse packages. Additional work required for graduate students.
This course explores advanced soccer analytics, emphasizing expected goals modeling, passing networks, tactical formations, and player valuation. Using R and Python, students build predictive models with professional tracking data from FIFA, UEFA, and MLS. Additional work required for graduate students.
This course focuses on golf analytics methods for analyzing professional performance and strategy including world rankings, driving distance trends, weather impacts, performance streaks, putting optimization, and strokes gained methodologies. Additional work required for graduate students.
Explores the theory and practice of sports betting markets including market efficiency, line movements, momentum trading, money management, Kelly criteria, futures, parlays, and Daily Fantasy Sports across multiple sports. Additional work required by graduate students.
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.
Source: Syracuse University's catalog, linked per course · table learning_unit · CourseShelf publish 61
