Football Recruiting and Impact in the NFL
Introduction
For this activity, you will be exploring football wide receivers that graduated high school from 2013 to 2019 and committed to Division 1 universities. Also, the data includes NFL players who were targeted for receptions during the 2023 season.
In particular, you will modify datasets and analyze the data using visualizations to compare and contrast receivers in the NFL.
Investigating this data is useful for several reasons. First, exploring the data can help us understand the accuracy of high school recruiting data and how it relates to performance in the NFL. Also, cleaning and working with the data provides opportunities to gain impactful insights into both productivity in the NFL and what places players are recruited from. Analyses like these can help scouts find and recruit the players that will have the most impact on their team and help win games.
Data
The data contains 3965 rows and 37 columns. Each row represents a football player who either was targeted for receptions in the NFL in 2023 and/or was recruited to play Division 1 football from the years 2013-2019.
Download data:
Available on the SCORE Data Repository
Data: College_NFL_WR.csv
Variables from Rk to Id contains data from the 2023 NFL season for all players that were targeted for a reception that season (not just wide receivers). Variables from AthleteId to FipsCode contains high school recruiting data for wide receivers from 2013-2019.
Variable Descriptions
| Variable | Description |
|---|---|
| Rk | rank based on number of receptions |
| Player | first and last name of the player |
| Tm | NFL team |
| Age | age in years of player |
| NFL_Position | player position in the NFL |
| G | number of games played |
| GS | number of games started |
| Tgt | total times targeted for a pass |
| Rec | total number of receptions |
| Catch_Pct | percentage of passes caught |
| Yds | total number of receiving yards |
| Yd_per_Reception | average number of yards per reception |
| TD | number of receiving touchdowns |
| First_Downs | number of first downs |
| Success_Rate | player gains 40% of yards to go on first down, 60% on second down, or 100% on third or fourth down |
| Lng | longest reception in yards |
| Yd_per_Target | number of receiving yards per target |
| Rec_per_Game | number of receptions per game |
| Yd_per_Game | number of yards per game |
| Fmb | number of fumbles |
| Id | identification number of the player in the dataset |
| AthleteId | unique id number for each recruit |
| RecruitType | type of school the player got recruited from (i.e. high school) |
| Year | player’s high school graduation year |
| Ranking | prospect ranking (1 = best) |
| School | name of school being recruited out of |
| CommittedTo | college player is committed to play at |
| Height | player’s height in inches when recruited |
| Weight | player’s weight in pounds when recruited |
| Stars | ranking based on chance in play in college or NFL from 0-5 (5 = best chance) |
| Rating | rating of prospect from 0 to 1 (1 = best) |
| City | home city of the prospect |
| State | home US state/Canadian province of the player (including Washington DC) |
| Country | home country of the prospect (either US or Canada) |
| Latitude | latitude of the player’s hometown |
| Longitude | longitude of the player’s hometown |
| FipsCode | FIPS code of the player’s hometown |
Data Sources
NFL receptions data from the 2023 season:
High school recruiting data from 2013-2019:
Materials
We provide editable MS Word and Quarto handouts along with their solutions. The Word handouts are designed for in-class quizzes when students do not have access to R. The Quarto documents are designed for students with access to R.