Case Article—Converting NFL Point Spreads into Probabilities: A Case Study for Teaching Business Analytics

Eric Huggins (), Matt Bailey () and Ivan Guardiola ()
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Eric Huggins: School of Business Administration, Fort Lewis College, Durango, Colorado 81301
Matt Bailey: Freeman College of Management, Bucknell University, Lewisburg, Pennsylvania 17837
Ivan Guardiola: School of Business Administration, Fort Lewis College, Durango, Colorado 81301

INFORMS Transactions on Education, 2020, vol. 21, issue 1, 57-60

Abstract: In this case study, students determine the relationship between point spreads and the probability of winning a game using data from the National Football League (NFL); although the data comes from the NFL, the models and insights are accessible to students who are unfamiliar with football. They model the relationship first with a linear fit and then with a logistic curve. The analysis requires a combination of several key Microsoft Excel functions, including PivotTables, trendlines, and Solver. The case is designed to be tailored to the needs of the instructor, the students, and the course—the basic case can be relatively short but several additional options add depth and reinforcement; similarly, it can be assigned as an unstructured or highly structured assignment. The case itself introduces the idea that with enough data on previous point spreads and game results, we can calculate the empirical probability that a team favored by a given point spread will win the game. The case Teaching Note provides detailed instructions for every step of the case, with hints for both instructors and students. This case article discusses the positive pedagogical aspects.

Keywords: Microsoft Excel; sports analytics; PivotTables; Solver; data cleaning; logistic regression (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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