Skip to content

loganchali4/NFL-Big-Data-Bowl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 

Repository files navigation

NFL Big Data Bowl

Dec 2019 | Predicting NFL Rushing Yards

There are many elements that determine the success of a run play in a professional league that represents the world’s biggest, fastest, and strongest athletes. Many of these factors are outside of the ball-carrier's control, relying rather on coaching decisions, schemes, opposing defenses, and random variability through blown assignments.

This report focuses on the relationship between yards gained on a running play and the variables that are indicative in predicting those yards. Using data acquired from Next Gen Stats of the 2017 and 2018 seasons in the NFL, the goal of this report is to propose a model that accurately predicts how many yards an NFL player will gain after receiving a handoff. Through regression analysis, we determine which of these factors can help coaches and front office executives answer the question of “What makes for a ‘good’ run play?”

The model includes variables such as speed, acceleration, defenders in the box, down, and distance to predict yards gained. We then described how this model can be utilized and applied to help franchises improve their team and prepare for opponents. Additionally, the model is tested for the assumptions of regression and a practical example is provided. The details of these variables and findings are explored throughout this report.

View our report HERE

Releases

No releases published

Packages

No packages published