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Running Back Vision Model – 2021

Focusing on the fit paid off in 2020 if you drafted the Rams’ Cam Akers. There is more opportunity to be had with the simplified 2021 Running Back Vision. Check out the data and learn how it is used by the creator, @FFB_Vern.

Free agency is upon us, and the draft will soon follow. This is the time where we are all hungry for information that could give us an edge. This season, the aim was to simplify the information, reduce subjectivity but still provide useful insight into possible fits. Fit is one of the most intriguing aspects of predicting running back upside for rookies and free agency. This is why there are two data tables: one for the rookies and one for NFL team success and tendencies.

The idea is to provide information so that you would be able to generate your own insights. Over time, I will provide a few of my own for your reading pleasure. When using this, it could help you identify synergies between prospects and destinations.

Last draft season, I predicted D’Andre Swift to San Francisco based on his prowess and the 49ers’ high frequency of employing the outside zone scheme. He went to Detroit, but he still performed better in outside zone than gap, power, or counter schemes. Similarly, I pegged Akers to the Rams, given the frequency in which the team runs gap schemes. By the end of the season, the projected synergy really started to pay off.

Why the Changes?

2020 was a tough year for us all, and there were many tough lessons learned throughout. During the season, I started to evaluate “vision” differently. It felt irresponsible to skew opinions over a subjective “vision” metric, despite efforts to make it objective. With my shift in that area, other areas were considered for subjectivity like “burst,” “Offensive Line (OL) Score,” etc.

With that said, the data still provides you an objective measure to consider: Adjusted Success Rate. This metric bakes in when prospects get more than needed on a given play while also accounting for touchdowns. The data gives the user a sense of where the prospect is effective, especially with the division into run schemes. This iteration of RBV separates power and counter concepts from gap blocking to provide a more granular look.

The lack of “vision” and “burst” metrics won’t leave you high and dry. Subsequent articles will identify a summary of the intriguing fits and assessment of these qualities in narrative form. As with any analytic, this data acts as a compass before making conclusions about the player. Let’s not delay any further and look at the data.

RBV Adjusted Success Rate - 2021 RB Prospects

Plays - # of plays factored into Adj Success Rate from play-by-play data.
Observed Plays - # of plays observed via publicly available tape samples
IZ - Inside Zone; OZ - Outside Zone; PWR - Power; CTR - Counter; Gap - Man blocking including Duo
XX# - # of plays observed in the scheme
NameTeamAdj Success RatePlaysObserved PlaysIZIZ#OZOZ#PWRPWR#CTRCTR#GAPGAP#
Kylin Hill (2019)Mississippi State51.5%23411554.7%5931.3%456.7%41N/A072.7%11
Jaret PattersonBuffalo67.7%14010386.0%4171.7%4566.7%395.8%634.4%8
Najee HarrisAlabama56.1%21310158.6%2951.9%2777.8%946.0%2561.4%11
Larry RountreeMissouri49.3%2059344.2%3042.3%42100.0%154.4%170.0%3
Javian HawkinsLouisville47.5%1317860.7%1436.8%550.0%10.0%157.1%7
Javonte WilliamsNorth Carolina73.6%1567570.2%3161.1%18101.7%15140.0%100.0%1
Kenneth Gainwell (2019)Memphis49.5%2187529.2%1247.9%3656.3%475.0%1261.4%11
Michael CarterNorth Carolina59.1%1567383.0%2847.2%1859.1%1134.4%16N/A0
Master TeagueOhio State46.6%887249.0%2445.5%3316.7%6N/A0102.8%9
Elijah MitchellLouisiana54.0%1206847.1%3468.3%260.0%135.0%5100.0%2
Zamir WhiteGeorgia56.8%1336764.8%4436.4%11N/A058.3%6108.3%6
Jermar JeffersonOregon State55.5%1316650.7%3768.4%190.0%1125.0%168.8%8
Cam'Ron HarrisMiami54.0%1196683.0%530.0%341.7%633.3%30.0%1
Trey SermonOhio State62.8%846457.3%2490.0%250.0%3N/A047.9%12
Travis EtienneClemson55.4%1546075.0%180.0%933.3%955.0%1091.1%14
Khalil HerbertVirginia Tech53.5%1555758.9%1451.5%3375.0%218.8%8N/A0
Rhamondre StevensonOklahoma49.4%815079.5%1152.9%1775.0%639.3%140.0%2
Josh Johnson (2019)Louisiana Monroe52.5%1905064.2%440.0%1N/A030.0%5N/A0
Brian Robinson Jr.Alabama63.5%784860.0%546.9%16100.0%357.5%2056.3%4
Chuba HubbardOklahoma State52.9%1314521.4%1436.8%170.0%10.0%461.1%9
Dameon PierceFlorida47.5%1014241.7%970.0%1030.0%1033.3%357.5%10
James CookGeorgia58.3%453846.3%2084.4%8150.0%158.3%633.3%3
Jake FunkMaryland59.6%603753.3%1533.3%333.3%9105.6%90.0%1

The 2021 rookie RBV data (above) revealed some surprises for sure. Most expect to see Najee Harris and Travis Etienne’s names gracing the top of the overall success rate category. Instead, Javonte Williams (maybe not too shocking) and Jaret Patterson (probably shocking) sit atop the overall adjusted success rate category. Of course, there is a ton of context missing from these numbers, for example, the level of competition in Patterson’s case.

As stated before, the above data’s value came when it was combined with their landing spot and the knowledge of their tendencies. While this won’t happen until the end of April, it definitely great to get ahead of the valuation game. Small adjustments to your board can be made using the above data and last season’s NFL team data (below).

RBV Adjusted Success Rate - NFL 2020

Each NFL Team with adjusted success rate in each scheme and the scheme tendency for each scheme. Additionally, Zone and Pull percentages are offered as an additional data point. This is data through week 13 (normal end of fantasy regular season).

RBV Application Example – Jaret Patterson


Just a taste of how I use this data. Patterson should jump off the table being a small school guy without a lot of hype. The thing is, Patterson is showing out on both inside and outside zone. When seeing a high percentage in any scheme, I sanity check the sample size. For the most part, I do not trust much when there are less than 10 snaps observed in the scheme. Fortunately, he checks the boxes for sample size in both zone categories.

Patterson Scouting Report

Patterson has excellent quickness and change of direction, which facilitates occasions where he can weave through the trenches to the second level, allowing speed to be maintained. His vision on zone concepts helps him quickly identify a point of attack and anticipate flow. In the open field, his spatial awareness allows him to split defenders and minimize direct contact. Patterson has burst and good play speed; it shows that he consistently creates open cutback lanes when he is exploiting the edge or threatening the edge. He has phenomenal finishing ability with outstanding contact balance, good power, pad level, and homerun speed. It requires clean form tackles to bring him down with one person; in the open field, good angles and excellent speed will be required to chase him down.

Patterson has solid receiving skills with a good ability to adjust to passes behind him and good body control to penetrate upfield immediately. His hands are strong, and he has a good catch radius. He attacks blocking with competitive toughness and willingness to engage. Patterson uses good and wise execution of cut blocks when his size is outmatched. His vision in pulling concepts is adequate due to a lack of patience with blocks develops. He often gets ahead of lead blockers.

Overall, despite his size (5’9” 195 lbs), he will fit any offense that features zone run-blocking schemes. His burst, if it translates, will be an asset yielding consistent production with solid or better blocking. Getting him into space will allow chunk gains through contact balance, home-run speed, and elusiveness at the second level. Without a significant improvement in blocking technique, his third-down usage would bank on his pass-catching ability, which is solid to good.

Finding the Fit

The picture of Patterson’s usage from above should help identify each of these potential fits’ upside. Read a few write-ups of a prospect to get additional perspectives if you aren’t the tape grinding type. The profile on Patterson by @FFPeeblesChamp is a good place to start. With his potential as a runner in mind, look at the team data to find a match. Patterson charts out well in both schemes, prompting a sort of the teams by the “Zone %” column. Using 60% as a floor, we need to identify a team with a potential vacancy. Buffalo looks promising, but they have two backs drafted in the last two years. Unfortunately, Patterson doesn’t have the physical size supplant either of them, so we keep going.

The next three teams to consider include the 49ers, the Jets, and the Texans after Duke Johnson’s departure. The 49ers seem to rotate backs a lot since Shanahan’s arrival, which may be a subpar landing spot. The Jets have La’Mical Perine and Ty Johnson, but Patterson could be the change of pace back in that offense. Houston could be a strong fit for the former Buffalo RB.

Looking at these potential fits, Patterson landing with one of them in rounds 2-4 will pique my interest. Landing with one of these teams any later than that puts him in a stash category for me. The biggest things holding down his projection at the NFL level are the level of competition and his size. We must remember that some of the best fantasy running backs in years past have come out of nowhere. His profile, fits, and adjusted success rate call us to pay attention to pro days and draft spot.

Try It Out

Now that you have seen how I use the data, I implore you to give it a shot yourself. As the draft approaches and free agency clarifies the opportunities, I will share my perspective on fits. Follow me on Twitter @FFB_Vern for some RBV high-level views and other revelations as I continue to dig.

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