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Predictive analytics for University of Virginia football recruiting

Authors :
Jonathan Cooke
Kunrui Peng
John Valeiras
Roby Williams
Annie Crockett
Stephen Adams
Mark Rhodes
Dongmin Shin
Austin Foster
Jacob Rue
William T. Scherer
Chris Tuttle
Source :
2018 Systems and Information Engineering Design Symposium (SIEDS).
Publication Year :
2018
Publisher :
IEEE, 2018.

Abstract

College football programs rely on recruiting to attract high-quality talent, which helps build a team's foundation and ensure success year after year. By conducting systems analysis of the current University of Virginia recruiting process and building upon Walter et al.'s work [1], the Virginia recruiting staff can gain a competitive advantage in the recruiting landscape. Analyzing Virginia's football recruiting and utilizing data analytics could provide the coaching staff with powerful tools to gain such a competitive edge. This study uses a database encompassing over 53,000 football recruits and over 200 predictive attributes to model the four aspects of collegiate football recruiting, as defined by Virginia's football coaches. Specifically, a desirable athlete is defined as one who 1) would succeed on the field at the collegiate level, 2) will meet Virginia's strict academic standards to achieve four years of playing eligibility, 3) fit Virginia football's “gritty” team culture, which is characterized by players who are resilient and able to overcome challenges, and 4) would commit to Virginia if given an offer.

Details

Database :
OpenAIRE
Journal :
2018 Systems and Information Engineering Design Symposium (SIEDS)
Accession number :
edsair.doi...........5f67b13b0cdb95b51bb1486a9e9c9318