Booth Id:
MATH044T
Category:
Mathematics
Year:
2021
Finalist Names:
Rachwalski, Nathan (School: Henry W. Grady High School)
Walker, Christopher (School: Henry W. Grady High School)
Abstract:
The purpose of this project is to determine if it is possible to effectively use traditional
regression models to predict an event whose outcome has historically been unpredictable. In
our case, we are analyzing the NFL Draft, but we hope our project could influence the way
people attempt to analyze outcomes of all seemingly ambiguous decisions, not just those in
sports. If we utilize historical NFL Combine and NFL Draft data we will be able to create many
multiple linear regression models for each NFL position that demonstrate a correlation
between performance in certain combine drills and where a player is taken in the NFL Draft.
We started off by collecting data for all of the players using StatHead, a company geared
towards recording statistics for American Professional Athletes, and used this data to develop
spreadsheets. We used these spreadsheets as our data frames in R and were able to run
multiple regression models and use packages such as the MuMIn package which allowed us
to sort the multiple linear regression models by AIC. After selecting the combine events that
correspond with our use of AIC, we ran multiple regression to generate results that can clearly
link a player’s performance in the NFL Combine to where they are taken in the draft. One
example of this was our testing on the set of tight ends drafted in the last five years, a very
small subset (about 40 players) of our larger set of data (about 1500 players).