Sports in America is big business. It has become a substantial contributor to the national economy. Big profits are earned and big salaries are paid. As a result, performance in the field and in the front office has become a significant managerial concern. The difference between winning and losing is no longer just a matter of athletic ability and personal pride, but it can mean huge differences in revenue and profit. As such, in recent decades, more and more sports organizations have reached out to the application of advanced management methods, in particular statistical, data analysis and operations research/management science techniques. The use of data, and now Big Data, has become entrenched in the business of sport. The analysis of that data has taken on new dimensions and has become as sophisticated as that of any other endeavor.
This course is an examination of the most advanced applications of those techniques. The structure of the course is to examine the use of them to four main areas of interest: player performance measurement, in-game decision-making, player selection/team building, and general administration such as marketing, pricing, contracts, stadium management etc. Emphasis will be place on not only how the application of Analytics has improved each of these situations, but how those decisions relate to business decisions in any other field of commerce. For example all businesses have to evaluate employees, make tactical and strategic decisions about how they operate, must maintain a good portfolio of assets in particular recruit and retain quality employees, and have to be good at administering the overall business.
Each class will examine one or more of these topics in one or more sport. Students will be responsible in each class for readings which will the basis of class discussions. There will be homework assignments using the Analytic techniques discussed. Lastly there will be a group project due at the end of the semester.
Several classes will have guest speakers with practical experience in the field of Sports Analytics.
Students should be familiar with the use of Excel, Solver, basic statistical data analysis/mining techniques, and at least one simulation software program such as Crystal Ball, Risk Solver Platform, Matlab, or equivalent.