The Midwest Big Data Hub (MBDH) is a growing network of partners investing in data and data sciences to address grand challenges for society and science. Anyone is welcome to join these efforts. Announcements on new opportunities are listed below and additional activities are listed on our News & Events page.
Upcoming Talks, Webinars, and Events:
MIDAS Seminar Series
Friday, January 19th, 3:00 – 4:00pm CT
Presenter: Robert Goldstone | Indiana University
Title: “Baseball Umpire Calls As A Naturally Occurring Data Source For Revealing Principles Of Bias And Learning In Perceptual Judgments”
Learn more here.
Abstract: The very expertise with which experimental psychologists wield their tools for achieving laboratory control may have had the unwelcome effect of blinding us to the possibilities of discovering principles of behavior not by conducting experiments but rather by analyzing naturally occurring data sets. Uncovering principles of psychology by analyzing naturally occurring data is an exciting endeavor because of 1) the rise of well curated and large data sets involving collections of tagged images, text corpora, Wikipedia edit histories, trends in Twitter tag usage, demographics, consumer product sales, patent use and dependencies, sporting event outcomes, scientific citations, etc., 2) novel analytic methods for inferring causal relations from observational data, 3) the data often come from strongly motivated decisions and life-changing behaviors of social importance, and 4) the data sets allow us to explore the interplay between internal psychological processes and external environments, artifacts, and social institutions.
As a case study of harvesting naturally occurring data to reveal psychological principles, I will describe my collaboration with Brian Mills to study home plate umpire calls of strikes and balls. Major League Baseball home plate umpires have collectively made millions of professional pitch calls, and these calls can be compared to trajectory information recorded since 2008 for each pitch using tracking technology that is accurate to within 1 MPH and 1 inch. Furthermore, pitch, umpire, and game data are publicly available and relatively easily scraped using modern analysis tools. Using this data, we characterize how umpires’ perceptual judgments are influenced by situational factors and their own experience making calls. We fit a parametric model to account for variation in judgment policies in terms of a strike zone’s horizontal and vertical center, shape, and sharpness, and the umpire’s guessing probability and bias.
See other upcoming events here.