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British Journal of Mathematics & Computer Science, ISSN: 2231-0851,Vol.: 22, Issue.: 4

Original-research-article

Estimating the Strength of the Impact of Rushing Attempt in NFL Game Outcomes

 

Xupin Zhang1*, Benjamin Rollins2, Necla Gunduz3 and Ernest Fokoue2
1University of Rochester, Rochester, NY 14620, USA.
2Rochester Institute of Technology, Rochester, NY 14623, USA.
3Department of Statistics, Gazi University, Ankara, Turkey.

Article Information
Editor(s):
(1) Qiankun Song, Department of Mathematics, Chongqing Jiaotong University, China.
(2) Tian-Xiao He, Department of Mathematics and Computer Science, Illinois Wesleyan University, USA.
Reviewers:
(1) Paul Shea, Bates College, USA.
(2) Eduardo Cabral Balreira, Trinity University, USA.
(3) Marcos Grilo, Universidade Estadual de Feira de Santana, Bahia, Brasil.
(4) P. Wijekoon, University of Peradeniya, Sri Lanka.
(5) Julyan Arbel, Inria Grenoble Rhone-Alpes, France.
Complete Peer review History: http://www.sciencedomain.org/review-history/19299

Abstracts

In this paper, we use estimators of variable importance from the ensemble learning technique of random forest to consistently discover and extract the knowledge that Rush Attempt is strongly related with winning football games in the NFL. Almost all researchers before us have consistently made claims of the impact/influence other statistics in the outcomes of NFL games, with Third Down Conversion Percentage and Takeaways almost universally considered as having the greatest impacts in game outcomes. Rushing as a factor of NFL success has also been mentioned, but mostly in terms of number of rushing yards per game. The novelty in this present work lies in the fact that not only do we discover Rush Attempt differential to be the strongest and most dominant variable, but we also establish its dominance throughout the years, namely with 14 seasons worth of NFL games data providing firm evidence of the ubiquitous appearance of Rush Attempt at the root of every classification tree.

Keywords :

NFL; rush attempt; classification trees; and random forests.

Full Article - PDF    Page 1-12 Article Metrics

DOI : 10.9734/BJMCS/2017/31565

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