Justin Fields Player Profile: Passing

Author: Billy Jones

Inspiration: Paul Sabin (@SabinAnalytics/SumerSports)

Introduction

Welcome back to the blog! I’m excited to continue this series where I analyze Justin Fields' production compared to other elite rushing quarterbacks over their first two years. In the first two parts I discussed Justin Fields’ rushing production and highlighted his extremely high scramble and sack rates. Quarterback runs are fun and all that but we all know this is a passing league and that’s where games are won. In this part I'll shift my focus to Fields' passing production compared to the other players in this study. To do this I am going to use some more traditional statistics but also include some of the newer ones that you may not have of heard but are growing in popularity. Thank you for those that have followed along with this series and let's dive back into the world of football analytics!

Non-Traditional Statistic Explanation

Per PFF, “Expected Points Added (EPA) is a measure of success which defines the value of each play by the effect it has on the offense's likelihood to score. For every play, EPA is attributed equally to both teams, and the metric is fairly reliable in identifying the best teams in football. It is common practice to discuss EPA on a per-play basis (EPA / Play), so that the stat is normalized for any disparity in total plays run. 

Foundationally, EPA is the difference in Expected Points before and after each play. Expected points is an estimate of how many points a team will score on a drive, given the current situation (Down, Distance, Time Remaining, etc.). Intuitively, as a team gets closer to its opposing end zone, the higher the expected points of the drive.”

Bad ball rate measures the percentage of bad throws a quarterback makes. This statistic I learned from Ed Feng (thepowerrank.com & The Football Analytics Show) and he defines it as any time the ball is thrown and the defender gets their hand on it, whether it's an interception, a batted ball at the line, or a deflected ball in the secondary. He created this metric as a way to more accurately predict interceptions. Unsurprisingly, he found that interceptions (or interception rate) appear to be super random year over year, but bad ball rate wasn’t as random. This makes perfect sense when you think about it. Bad ball rate helps to highlight situations where a quarterback is making poor decisions or putting the ball in risky positions, regardless of whether or not the defense actually intercepts it.

Ground Rules

Before we jump back into the analytics, I would like to remind the readers of the ground rules we will be playing with. The data used for this analysis was obtained from NFLFASTR for 2000 through 2022. For each of the quarterbacks in the analysis we will be looking at only their first 2 years in the NFL. The quarterbacks included in the study are the top 10 rushing quarterbacks based upon total rushing EPA in their first 2 years. These are Cam Newton, Justin Fields, Lamar Jackson, Josh Allen, Andrew Luck, Dak Prescott, Mike Vick, Kyler Murray, Jalen Hurts, and Robert Griffin III.

Visualizations and Analysis

With that now explained, let’s investigate total passing EPA by player. 

Analysis: First we look at passing EPA as a whole where Fields is pulling up the rear of the study. I did look at the impact of spikes and removed the data, but it did not have any impact on the rankings.

* Mike Vick is removed from the analysis below as Depth of Target is not included in data for 2000 and 2001. *

The next step in the analysis was to break down each player’s passing production to a more granular level. To do this I isolated passing efficiency and allocation by depth of target as this would allow me to see how each player performed on different throw types. 

Analysis: Justin Fields was the least productive quarterback of the group. He was the worst in EPA per attempt and really struggled on the passes 5 yards and under, which accounted for 45.74% of his passes. While he struggled across the board, he showed signs performing well in the 10-20 yards range with a 0.44 EPA per attempt (this is good news as this is our most valuable passing area – see Deep Passing Study). I have heard all over twitter about Fields’ struggles on short and intermediate throw and the data reaffirms this. This is something that will need to be improved if he is to take the next step into elite quarterback status. 

EPA misses 1 major thing for me. It doesn’t truly convey the negative risk associated with interceptions. I personally think turnover risk reduction is a huge part of playing quarterback. To review Justin Fields’ risk reduction, I presented two different statistics related to interceptions.

Analysis: Justin Fields performs middle of the road in our risk reduction statistics of bad balls per attempt yet his interceptions per attempt rate is the highest amongst the group. This would imply that less interceptions per attempt could be in his future as interception rate is not a sticky statistic while bad ball rate is (see Bad Ball Study coming soon). 

Conclusion

This concludes the look back portion of the Justin Fields player profile series. I found the deep dive look into Justin Fields and his elite rushing quarterback peers insightful in understanding what has happened in the past. In the final post of this series, I will take a look at how Justin Fields peers faired in their 3rd season in the NFL. With such a small sample size I will stay away from making formal predictions, but his peers’ performances will give a great baseline for what might be. Thanks for reading and stay tuned for more football analytics posts!

*This blog post was enabled by ChatGPT. The text was generated by me, and the content is my own, but some sentences and wording were provided by the model. I take full responsibility for all information produced in this blog. More information about OpenAI and their technology can be found at https://openai.com.*