2026-05-23T04:16:53Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1328572024-12-18T20:58:30Zoai_pmh:alloai_pmh:repo_items132857
https://hdl.handle.net/2286/R.I.52437
http://rightsstatements.org/vocab/InC/1.0/
2019-05
34 pages
eng
Blinkoff, Joshua Ian
Voeller, Michael
Wilson, Jeffrey
Graham, Scottie
Dean, W.P. Carey School of Business
Department of Information Systems
Department of Management and Entrepreneurship
Barrett, The Honors College
Text
Predictive analytics have been used in a wide variety of settings, including healthcare, <br/>sports, banking, and other disciplines. We use predictive analytics and modeling to <br/>determine the impact of certain factors that increase the probability of a successful <br/>fourth down conversion in the Power 5 conferences. The logistic regression models <br/>predict the likelihood of going for fourth down with a 64% or more probability based on <br/>2015-17 data obtained from ESPN’s college football API. Offense type though important <br/>but non-measurable was incorporated as a random effect. We found that distance to go, <br/>play type, field position, and week of the season were key leading covariates in <br/>predictability. On average, our model performed as much as 14% better than coaches <br/>in 2018.
Data Analytics
Fourth Down
College Football
Predictive Modeling
Probability
Logistic Regression
Predictive Modeling of 4th Down Selection in Power 5 Conference: Data Analytics