Sports Analytics

1.5 credits · Undergraduate

Sports Analytics uses data and quantitative methods to measure performance and make decisions to gain advantage in the competitive sports arena. Powerful quantitative methods such as linear regression, constrained optimization, and simulation can give operations a competitive advantage. In this course, students gain further experience in applying quantitative methods to problems in the context of sports. Typical questions in sports analytics include: How to predict future performance of players or teams? How to rank players or teams? Which decision is more likely to lead to a win? We will cover several sports, but to be able to ‘go deep’ we will focus significantly on baseball, football, and soccer. In class, we will analyze real sports data with powerful quantitative methods in Excel. As a result, the course will sharpen students’ analytics skills. These skills are equally useful in many areas of business and beyond. Prerequisite: One of BME 2400, DS 1000, DS 2100, ECON 1500, ECON 1510, MATH 1011, MATH 2810, MATH 2820, PSY 2100, PSY-PC 2110, or SOC 2100. [1.5]