Moneyball: The Analytics of Sports
Do you want to learn how to use data and quantitative methods to measure performance and make decisions to gain an advantage in sports?
The techniques and tools of business analytics are transforming many industries, including sports. This vertical has the advantage of being widely and closely followed, with large amounts of easily-accessible real-world data. Topics for study in this course include how to evaluate players, create good teams, predicting the success of decisions, schedule leagues, and enhance coaching strategies. Activities involve the use of a variety of techniques and tools, including data collection, cleaning and manipulation; basic probability theory; introductory statistical reasoning; regression analysis; optimization; and simulation. The hands-on learning laboratories will introduce participants to R, a statistical programming language. Only basic knowledge of sports (especially baseball, football, and basketball) is assumed.
Scott Nestler is an associate teaching professor in the IT, Analytics, and Operations (ITAO) department, and serves as the Academic Director of the MS in Business Analytics program. He teaches courses in Statistics, Sports Analytics, and Ethics in Business Analytics. He joined Mendoza College of Business subsequent to his retirement from the U.S.Army after more than 25 years of service. Previously, he taught at the Naval Postgraduate School and the U.S. Military Academy at West Point. He has served as an analyst and leader of analytic teams at the Pentagon and the U.S. Embassy in Baghdad, Iraq. Scott has a Ph.D. in Management Science from the University of Maryland – College Park. He is active in the Institute for Operations Research and the Management Sciences (INFORMS), and currently serves as the General Chair of the 2020 Analytics Conference, in Denver, CO. Previously, he was Chair of the Analytics Certification Board (ACB), which oversees the Certified Analytics Professional (CAP) program and Vice-Chair of the INFORMS SpORts (Operations Research in Sports) Section. He is especially interested in looking at holistic methods for measuring individual performance, and then aggregating that information when building high performing teams. His research also looks at the ethical implications of data collected from wearable devices used by athletes and others.
Seth is an assistant teaching professor in the IT, Analytics, and Operations (ITAO) department, where he teaches courses in Statistics and Unstructured Data Analytics. After receiving his Ph.D. in Applied Experimental Psychology, Seth started his journey at Notre Dame as the Survey Methodology Consultant for the Center for Social Research (CSR -- now the CSSR). During his time in the CSR, he was involved in a veritable cornucopia of research. Seeking to further refine his skills, he took a Data Scientist position in the Mendoza College of Business. While engaging in many feats of data imagineering over a few years, Seth began teaching the occasional class for ITAO. These dalliances developed into a more serious relationship, as he is now teaching for ITAO full time. As a lifelong sports fan, Seth enjoys merging his passion of research and sports. He particularly enjoys scraping sports-related data from the web.
Martin Barron is an assistant teaching professor of Information Technology, Analytics and Operations at the University of Notre Dame, where he teaches undergraduate and graduate courses in machine learning and sports analytics. Prior to joining the Mendoza faculty, Martin worked in the Dublin-based sports analytics company Kitman Labs. In this role he served as a data scientist developing machine learning pipelines, researching analytics approaches and collaborating with elite sports teams on projects in the areas of injury prevention, performance maximization, player growth and maturation, and strategy analysis. Martin received his master’s degree and Ph.D. from the University of Notre Dame in the field of Applied and Computational Mathematics and Statistics. His research focused on the development of statistical methods for the analysis of next generation single-cell RNA-sequencing data. Martin received his undergraduate degree at the University of Dublin, Trinity College, in Economics and Mathematics.