Description
This paper presents a scenario for a Business Analytics (BA) teaching case that is targeted at the baseball free agent market. It is presented in a flexible manner designed to allow for its use as a multi-part case covering all phases of a data analytics project or a more restrictive case where the data collection has been completed for the students and/or only a portion of the analyses described in the case are covered. The case is primarily designed for use by undergraduate or graduate students in BA courses. However, selected portions of the case are suitable for use in a statistics course or upper division general MIS course. It is intended to support the stages of problem identification and planning and the application of analytics outputs to the business decision, as well as, supporting the technical aspects of: data collection, cleaning, and storage (ETL); application of appropriate data mining tools to produce actionable results, and development of user friendly visualizations that highlight those results.
Recommended Citation
Morgan, James Nelson and Ravindran, Sury, "Using Business Analytics to Target Baseball Free Agents: A Case Study" (2017). AMCIS 2017 Proceedings. 20.
https://aisel.aisnet.org/amcis2017/ISEducation/Presentations/20
Using Business Analytics to Target Baseball Free Agents: A Case Study
This paper presents a scenario for a Business Analytics (BA) teaching case that is targeted at the baseball free agent market. It is presented in a flexible manner designed to allow for its use as a multi-part case covering all phases of a data analytics project or a more restrictive case where the data collection has been completed for the students and/or only a portion of the analyses described in the case are covered. The case is primarily designed for use by undergraduate or graduate students in BA courses. However, selected portions of the case are suitable for use in a statistics course or upper division general MIS course. It is intended to support the stages of problem identification and planning and the application of analytics outputs to the business decision, as well as, supporting the technical aspects of: data collection, cleaning, and storage (ETL); application of appropriate data mining tools to produce actionable results, and development of user friendly visualizations that highlight those results.