A home-run is easier to hit over a 380 ft. wall than a 400 ft. wall. A jump shot made wide-open vs. a contested shot is far more likely to succeed. Running the football in the NFL effectively is easier when there are less would-be-tacklers close to the line of scrimmage. Because of this, today we take a look at RB Success Rates by box size, and learn about how “Box Sensitive” a RB is (how much his success rate is tied to the amount of players in the box).

NOTE: Traditional defenses (think 4-3, and 3-4) typically deploy 7 men in the box, and therefore “7” is the “baseline” amount of players in the box (note: this has nothing to do with personnel, there could be 4 DBs, or 7 DBs…all box size relates to is: how many defenders are “in the box”) vs a HEAVY box (8-men or more) and a light box (6 or fewer).
Hence, how much of a RB’s success is propped up (or depleted) by how many men are in the box? Or looked at another way, what RBs can succeed regardless of men in the box, “Box Insensitivity” (no worries, we’re not going to use that)? Thanks to our friends at SIS, and its datahub, we’ve broken down RBs and their “box size sensitivity” as follows.
Player | Team | Small | 7Man | Big | Small – Big |
Chase Edmonds | Cardinals | 54.40% | 33.30% | 10.00% | 44.40% |
Cam Akers | Rams | 68.40% | 32.70% | 31.00% | 37.40% |
Ty Johnson | Jets | 46.20% | 55.60% | 20.00% | 26.20% |
Jordan Wilkins | Colts | 42.90% | 25.60% | 20.80% | 22.10% |
Tony Pollard | Cowboys | 48.80% | 44.10% | 26.90% | 21.90% |
Zack Moss | Bills | 51.20% | 45.20% | 29.60% | 21.60% |
Salvon Ahmed | Dolphins | 50.00% | 34.80% | 29.20% | 20.80% |
Clyde Edwards-Helaire | Chiefs | 45.60% | 43.70% | 26.20% | 19.40% |
Carlos Hyde | Seahawks | 46.40% | 35.50% | 27.30% | 19.10% |
Jamaal Williams | Packers | 60.70% | 28.10% | 41.90% | 18.80% |
Austin Ekeler | Chargers | 42.60% | 40.90% | 27.80% | 14.80% |
Latavius Murray | Saints | 53.60% | 50.00% | 39.10% | 14.50% |
Matt Breida | Dolphins | 48.40% | 35.70% | 35.70% | 12.70% |
Myles Gaskin | Dolphins | 48.90% | 50.00% | 36.20% | 12.70% |
La’Mical Perine | Jets | 45.50% | 23.70% | 33.30% | 12.20% |
David Montgomery | Bears | 43.40% | 31.90% | 31.50% | 11.90% |
Kerryon Johnson | Lions | 50.00% | 34.80% | 38.50% | 11.50% |
Lamar Jackson | Ravens | 52.30% | 58.50% | 41.00% | 11.30% |
Miles Sanders | Eagles | 56.60% | 39.40% | 45.50% | 11.10% |
Devonta Freeman | Giants | 30.80% | 38.90% | 20.00% | 10.80% |
Jonathan Taylor | Colts | 51.40% | 46.20% | 40.80% | 10.60% |
Anthony McFarland Jr. | Steelers | 30.00% | 27.80% | 20% | 10.00% |
Mike Davis | Panthers | 51.60% | 38.60% | 42.20% | 9.40% |
Derrick Henry | Titans | 57.70% | 46.80% | 48.30% | 9.40% |
J.D. McKissic | Football Team | 59.00% | 40.00% | 50.00% | 9.00% |
Joe Mixon | Bengals | 44.40% | 30.20% | 35.50% | 8.90% |
Giovani Bernard | Bengals | 37.00% | 42.00% | 28.60% | 8.40% |
Cordarrelle Patterson | Bears | 38.90% | 42.40% | 30.80% | 8.10% |
Josh Jacobs | Raiders | 45.50% | 39.40% | 37.90% | 7.60% |
Phillip Lindsay | Broncos | 29.40% | 33.30% | 22.20% | 7.20% |
Alvin Kamara | Saints | 47.10% | 46.00% | 40.50% | 6.60% |
Melvin Gordon | Broncos | 48.40% | 31.20% | 41.90% | 6.50% |
Kareem Hunt | Browns | 45.50% | 38.50% | 39.10% | 6.40% |
Kalen Ballage | 2 teams | 42.40% | 42.90% | 36.70% | 5.70% |
James Robinson | Jaguars | 43.80% | 39.80% | 38.20% | 5.60% |
Frank Gore | Jets | 36.50% | 35.60% | 31.10% | 5.40% |
J.K. Dobbins | Ravens | 52.80% | 42.90% | 47.60% | 5.20% |
James Conner | Steelers | 37.90% | 41.90% | 32.70% | 5.20% |
Mark Ingram | Ravens | 52.60% | 29.30% | 50.00% | 2.60% |
Devontae Booker | Raiders | 46.40% | 32.50% | 44.00% | 2.40% |
Ezekiel Elliott | Cowboys | 41.30% | 40.90% | 39.10% | 2.20% |
Duke Johnson | Texans | 36.80% | 31.60% | 35.00% | 1.80% |
Alfred Morris | Giants | 36.40% | 41.70% | 35.00% | 1.40% |
Dalvin Cook | Vikings | 41.80% | 53.50% | 41.40% | 0.40% |
Todd Gurley | Falcons | 36.70% | 30.10% | 36.50% | 0.20% |
Gus Edwards | Ravens | 57.70% | 41.30% | 58.20% | -0.50% |
Le’Veon Bell | 2 teams | 40.60% | 30.30% | 41.20% | -0.60% |
Chris Carson | Seahawks | 45.60% | 55.40% | 46.40% | -0.80% |
Brian Hill | Falcons | 34.80% | 35.00% | 35.70% | -0.90% |
Aaron Jones | Packers | 43.90% | 47.10% | 45.80% | -1.90% |
Joshua Kelley | Chargers | 32.60% | 29.00% | 35.30% | -2.70% |
Samaje Perine | Bengals | 50.00% | 25.00% | 53.30% | -3.30% |
Antonio Gibson | Football Team | 42.10% | 45.00% | 45.50% | -3.40% |
Benny Snell | Steelers | 37.80% | 22.60% | 41.90% | -4.10% |
Jeff Wilson | 49ers | 43.50% | 40.40% | 49.00% | -5.50% |
Ronald Jones II | Buccaneers | 41.20% | 42.20% | 46.70% | -5.50% |
Nick Chubb | Browns | 37.00% | 40.00% | 42.60% | -5.60% |
Boston Scott | Eagles | 32.60% | 45.80% | 38.50% | -5.90% |
Darrell Henderson Jr | Rams | 42.90% | 44.10% | 49.00% | -6.10% |
Taysom Hill | Saints | 58.30% | 50.00% | 65.50% | -7.20% |
D’Andre Swift | Lions | 44.90% | 50.00% | 52.40% | -7.50% |
Kenyan Drake | Cardinals | 39.00% | 37.40% | 46.50% | -7.50% |
Wayne Gallman | Giants | 35.70% | 38.00% | 43.50% | -7.80% |
Rex Burkhead | Patriots | 48.30% | 50.00% | 56.30% | -8.00% |
Raheem Mostert | 49ers | 29.00% | 54.30% | 37.00% | -8.00% |
Jerick McKinnon | 49ers | 23.80% | 38.90% | 33.30% | -9.50% |
Adrian Peterson | Lions | 30.00% | 32.80% | 40.00% | -10.00% |
Leonard Fournette | Buccaneers | 35.10% | 35.90% | 47.60% | -12.50% |
Nyheim Hines | Colts | 30.40% | 40.70% | 43.80% | -13.40% |
Devin Singletary | Bills | 34.30% | 41.00% | 48.00% | -13.70% |
Alexander Mattison | Vikings | 36.40% | 34.50% | 51.10% | -14.70% |
David Johnson | Texans | 35.60% | 22.80% | 51.10% | -15.50% |
Christian McCaffrey | Panthers | 46.70% | 47.60% | 62.50% | -15.80% |
Justin Jackson | Chargers | 32.40% | 33.30% | 50.00% | -17.60% |
Ito Smith | Falcons | 31.80% | 44.80% | 53.80% | -22.00% |
Malcolm Brown | Rams | 29.60% | 42.90% | 56.40% | -26.80% |
Again, the chart above shows each RB’s Success rate in different box sizes, with the final column showing what we’re calling “box sensitivity” (NET VALUE of Success in Heavy vs Light Box).
NOTE: The closer to 0 (regardless of +/-) an RB’s sensitivity, the less sensitive/more consistent a RB is running the ball, regardless of box size.
NOTE 2: Get your head out of the gutter, trust me its the most applicable name for the data point.

Here is how the top 25, relevant backs look, in terms of “small box sensitivity”:

And the opposite, or lack of “small box need”:

With everything in football, and all that FPS and other analytics companies try to understand better: very few plays, players or games are apples to apples comparisons. By adding a layer of explantion for a players succes, RBs in this case, we can better understand TRUE productivity. The next step would be to learn if there is a relationship between these numbers year-to-year. That is, was a certain RB’s success tied to “luck” (or more small boxes than expected) and vice versa to help you draft your fantasy team next year.