Print Close

Investigating Predictors of NFL Running Back Production Via Traditional and Regression Tree Models

Abstract Number:

2958

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Speed

Participants:

Bob Downer (1)

Institutions:

(1) Grand Valley State University, N/A

First Author:

Bob Downer
Grand Valley State University

Presenting Author:

Bob Downer
Grand Valley State University

Abstract Text:

Highly drafted running backs are becoming increasingly rare in the NFL Running back contracts are also not typically as lucrative and lengthy as other positions due to injuries and lack of longevity in the league. As a potential instructional data set to compare traditional regression techniques and tree modeling, a data set was compiled that investigated NFL running back production in the period from 1999 to 2021. Responses such as years with original draft team and total career NFL rushing yards were investigated. Predictors included total college attempts, college conference and overall draft pick number. Results revealed some anticipated predictor significance as well as some less anticipated predictor importance. Furthermore, tree modeling revealed interesting ranges of predictor variables that might be useful in evaluating college players and predicting NFL performance.

Keywords:

Sponsors:

Section on Statistics in Sports

Tracks:

Miscellaneous

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand