Showcasing NFL Receiver Efficiency Within a Team Context
A tool for creating interactive charts to add context to the strengths and weaknesses of an NFL passing offense.
The production of NFL pass catchers relies heavily on their surrounding offensive environment. The success of a wide receiver, tight end, or running back in the passing game is heavily dependent on their quarterback's ability to get them the ball, so it would be unfair to judge them against each other without considering the circumstances they play in. The goal of this project is to recontextualize individual performance for pass catchers to identify league-wide patterns in efficiency in addition to specific players who stand out amongst their teammates, for reasons good or bad.
Methodology
To measure receiving efficiency, I calculated the average passing EPA[What's this?] for all 32 NFL teams as well as the average EPA per target for all pass-catchers who were targeted on more than 10% of their team's passing plays to create the dataset used in my plot. The minimum target share can be modified to increase or decrease the amount of data on display for future use, but a value of 0.1 appeared best for a sample spanning one season, as it generally displayed the top 2-4 pass-catchers on each team.
- Data Source: nflfastR package (2025 regular season data).
- Tools: dplyr for data processing, ggplot2 and Plotly for visuals.
Results and Analysis
Below is an interactive graph for all pass-catchers with a 10% target share or better for the 2025 NFL regular seasonn plotted by team passing efficiency and individual efficiency.
Figure 1: Individual vs. Team Passing Efficiency. (Hover for details)
Since each player's efficiency factors into their team's overall efficiency, it is no surprise that there is a clear positive correlation between individual and team performance. However, the most interesting details come from the discrepancies in efficiency among teammates. There is a general trend in efficiency based on position played. Running backs are generally less efficient in the passing game than tight ends, with wide receivers generally falling in between the two. Of course, there are some exceptions to this trend. For example, Bijan Robinson and Breece Hall, two RBs who are exceptional in the passing game, were the only qualified players at their position to lead their team in receiving efficiency. Another oddity appears when examining the Minnesota Vikings' pass-catchers. The team struggled to pass the ball mightily in 2025 with inconsistent quarterback play, but it is nonetheless shocking to see Justin Jefferson, a consensus top wide receiver, boasting an EPA/target of -0.10, the lowest on his team. Perhaps there were mentality or chemistry issues involved that hindered Jefferson's production in 2025, but it is certainly concerning for Vikings fans heading into next season.
Although this project was quite simple in scope, I thoroughly enjoyed making it and have found value in the insights it offers for both retrospective and current analysis.
The Code
If you would like to learn more about this project, you may view and download the code for yourself using the button below. Thanks for reading!