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Most often, running backs in the National Football League are compared by taking summary statistics from each play. Traditional metrics include yards-per-carry, expected points added per play (EPA), and win probability added per play (WPA). While comparing players based on these their averages is the straightforward, means can be sensitive to outlying observations and distributions with heavy tails. Unfortunately, yards, EPA, and WPA are all strongly skewed, which means that taking averages may not be an optimal approach.



I taught the following courses at Skidmore College:

  • MS 104: Intro to Statistics
  • MS 204: Statistical Methods (Github)
  • MS 210: Data Visualization and storytelling (Github)
  • MA 204: Probability (Github)
  • MS 276: Statistics and Sports (Github)
  • MS 376: Mathematical Statistics
  • MS 240: Regression

+++ # About/Biography widget. widget = “about” active = true date = “2016-04-20T00:00:00”

Order that this section will appear in.

weight = 5

List your academic interests.

[interests] interests = [ “Causal inference”, “Statistics and sports” ]

List your qualifications (such as academic degrees).

[[]] course = “PhD in Biostatistics” institution = “Brown University” year = 2014

[[]] course = “MS in Statistics” institution = “UMass Amherst” year = 2010

[[]] course = “BA in Mathematics” institution = “Bates College” year = 2004



Michael Lopez is the Director of Football Data and Analytics at the National Football League and a Lecturer of Statistics and Research Associate at Skidmore College.

At the National Football League, his work centers on how to use data to enhance and better understand the game of football. Academically, his research is split between causal inference – with a specific focus on causal inference methods for multiple exposures or multiple exposure doses – and the application of statistics to sports. He is an Associate Editor at the Journal of Quantitative Analysis in Sports, and has written for FiveThirtyEight, Deadspin, Sports Illustrated, and Hockey News.