Justin Fields Player Profile: Rushing

Author: Billy Jones

Inspiration: Paul Sabin (@SabinAnalytics/SumerSports)

Introduction

Hey football analytics fans, I'm excited to dive into another interesting topic with you! In this blog I am going to take a break from my ongoing "Deep Passing Study" series to do a player profile for Justin Fields. The Chicago Bears quarterback is entering his third year in the league, and there's been a lot of twitter buzz around him, especially in the fantasy football community, most of which seems to be subjective opinions rather than objective analysis. As a data guy, the subjective opinion only analysis approach frustrates me but there was one article I read recently by Sumer Sports’ Paul Sabin which looked at Fields potential entering his 3rd year through the use of analytics and a data centric approach that I found particularly insightful. This article was great but talked more about general principles that Fields is likely to fit into and less specific analysis on how Fields has compared to his "peers". Paul's article piqued my interests and I wanted to take things to a level deeper so I dove into the data to see what I could find. So, if you're a fantasy football enthusiast, a Bears’ fan, or just curious about football analytics, welcome to my blog series where I get into the weeds of Justin Fields' production over his first 2 years. Let's get started!

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.”

While this stat is amazing, what I struggle with is using a statistic derived from data science model that creates a probability of scoring points on that drive before and after a play and finding the delta that I can’t currently manipulate. I can’t find anywhere that has more than a theoretical explanation for how it is calculated, which frustrates me, but I will look harder (any analytics friends want to point me to some literature on the topic?). 

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. I do want to note I questioned if I should present first 2 years or something like first 20 starts to include players who sat (e.g., Patrick Mahomes) but in an effort to mirror the Sumer Sports article I kept to the players listed above.

Visualizations and Analysis

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

Analysis: First we look at rushing EPA as a whole where Fields leads the study group followed closely by Cam Newton. But this figure is a little deceiving as quarterback kneels are baked into the statistic. This would penalize a quarterback for having a random end of first half kneel or being good at the game of football and winning too much. I backed this out and saw a bit of a tweak to the leaderboard. Cam Newton is the clear top rushing producer while Justin Fields is more closely aligned with Lamar Jackson, Josh Allen, and Andrew Luck. All quarterback kneels are removed from below figures.

Next I was curious to analyze the source of their rushing production, specifically designed runs versus scrambles. Luckily, the NFLFastR package includes a 'scramble' column that makes it easy to slice the data accordingly. I broke this out by total EPA and per attempt to layer on a level of efficiency to the analysis. 

Analysis: Justin Fields is a “scrambling man”. A significant proportion of his rushing EPA comes from scrambles, much greater than any other player in the study. Fields was 3rd best per scramble (#1 Mike Vick & #2 Andrew Luck) but he did not see the same efficiency on designed runs. Fields had the lowest EPA on designed runs by a solid margin of anyone in the study. This concerns me. I would assume the Bears hope to improve their offensive line issues from years past so Fields doesn’t have to scramble as much. If his scramble rate goes down and efficiency rates hold true, then there is some definite concern about his rushing output in 2023 and beyond.

With Justin Fields being a scrambling man, I was curious just how scramble happy he was compared to the other study members. 

Analysis: With the general accepted knowledge that the Bears offensive line was trash I knew Justin Fields was going to have very high scramble and sack rates. But Justin Fields didn’t just have bad scramble and sack rates, they were off the charts. To me this makes me question his pre-snap ability to put his team in a position to succeed. It will be interesting to see if he can make some sort of leap in 2023 with the Bears finally giving him tools to be successful.

Conclusion

In conclusion, Justin Fields has established himself as the 'scrambling man' in this study of elite rushing quarterbacks in their first 2 years in the NFL. He has relied heavily on his improvisational skills to create big plays on the ground yet seems to not as productive on designed runs compared to his peers. Nonetheless, the data suggests that Fields is a dynamic force in the rushing, even if it is mostly tied to his scrambles. Finally, we noted Fields has had incredibly high scramble and sack rates in his first 2 seasons which raises concern about his ability to read the defense pre-snap. Stay tuned for next week as I look into those scramble and sack figures in more detail in part two of this series!

*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.*