Abstract

NFL field goal kickers are often criticized for failing to make crucial field goals, but the best kickers are rarely recognized for being such. In an era of growing statistical research, field goal kicking is not widely studied to the same extent as other statistics. This project uses artificial neural networks to create difficulty ratings for NFL field goals. Using these ratings, kickers are evaluated based on a points added system as well as numerous other metrics and there are clear distinctions between most kickers. Despite popular opinion that field goal kickers are largely replaceable, this study shows just how different kickers are and how to recognize which ones are the most successful.

Advisor

Pasteur, Drew

Department

Mathematics

Disciplines

Applied Mathematics

Publication Date

2012

Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis

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© Copyright 2012 Kyle Cunningham-Rhoads