Full metadata
Title
Predictive Modeling of 4th Down Selection in Power 5 Conference: Data Analytics
Description
Predictive analytics have been used in a wide variety of settings, including healthcare, sports, banking, and other disciplines. We use predictive analytics and modeling to determine the impact of certain factors that increase the probability of a successful fourth down conversion in the Power 5 conferences. The logistic regression models predict the likelihood of going for fourth down with a 64% or more probability based on 2015-17 data obtained from ESPN’s college football API. Offense type though important but non-measurable was incorporated as a random effect. We found that distance to go, play type, field position, and week of the season were key leading covariates in predictability. On average, our model performed as much as 14% better than coaches in 2018.
Date Created
2019-05
Contributors
- Voeller, Michael Jeffrey (Co-author)
- Blinkoff, Josh (Co-author)
- Wilson, Jeffrey (Thesis director)
- Graham, Scottie (Committee member)
- Department of Information Systems (Contributor)
- Department of Finance (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Resource Type
Extent
34 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2018-2019
Handle
https://hdl.handle.net/2286/R.I.52436
Level of coding
minimal
Cataloging Standards
System Created
- 2019-04-12 12:00:26
System Modified
- 2021-08-11 04:09:57
- 3 years 3 months ago
Additional Formats