2020 RB Success Rate by Box Size

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).

6 Man vs 8 Man 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.

PlayerTeamSmall7ManBigSmall – Big
Chase EdmondsCardinals54.40%33.30%10.00%44.40%
Cam AkersRams68.40%32.70%31.00%37.40%
Ty JohnsonJets46.20%55.60%20.00%26.20%
Jordan WilkinsColts42.90%25.60%20.80%22.10%
Tony PollardCowboys48.80%44.10%26.90%21.90%
Zack MossBills51.20%45.20%29.60%21.60%
Salvon AhmedDolphins50.00%34.80%29.20%20.80%
Clyde Edwards-HelaireChiefs45.60%43.70%26.20%19.40%
Carlos HydeSeahawks46.40%35.50%27.30%19.10%
Jamaal WilliamsPackers60.70%28.10%41.90%18.80%
Austin EkelerChargers42.60%40.90%27.80%14.80%
Latavius MurraySaints53.60%50.00%39.10%14.50%
Matt BreidaDolphins48.40%35.70%35.70%12.70%
Myles GaskinDolphins48.90%50.00%36.20%12.70%
La’Mical PerineJets45.50%23.70%33.30%12.20%
David MontgomeryBears43.40%31.90%31.50%11.90%
Kerryon JohnsonLions50.00%34.80%38.50%11.50%
Lamar JacksonRavens52.30%58.50%41.00%11.30%
Miles SandersEagles56.60%39.40%45.50%11.10%
Devonta FreemanGiants30.80%38.90%20.00%10.80%
Jonathan TaylorColts51.40%46.20%40.80%10.60%
Anthony McFarland Jr.Steelers30.00%27.80%20%10.00%
Mike DavisPanthers51.60%38.60%42.20%9.40%
Derrick HenryTitans57.70%46.80%48.30%9.40%
J.D. McKissicFootball Team59.00%40.00%50.00%9.00%
Joe MixonBengals44.40%30.20%35.50%8.90%
Giovani BernardBengals37.00%42.00%28.60%8.40%
Cordarrelle PattersonBears38.90%42.40%30.80%8.10%
Josh JacobsRaiders45.50%39.40%37.90%7.60%
Phillip LindsayBroncos29.40%33.30%22.20%7.20%
Alvin KamaraSaints47.10%46.00%40.50%6.60%
Melvin GordonBroncos48.40%31.20%41.90%6.50%
Kareem HuntBrowns45.50%38.50%39.10%6.40%
Kalen Ballage2 teams42.40%42.90%36.70%5.70%
James RobinsonJaguars43.80%39.80%38.20%5.60%
Frank GoreJets36.50%35.60%31.10%5.40%
J.K. DobbinsRavens52.80%42.90%47.60%5.20%
James ConnerSteelers37.90%41.90%32.70%5.20%
Mark IngramRavens52.60%29.30%50.00%2.60%
Devontae BookerRaiders46.40%32.50%44.00%2.40%
Ezekiel ElliottCowboys41.30%40.90%39.10%2.20%
Duke JohnsonTexans36.80%31.60%35.00%1.80%
Alfred MorrisGiants36.40%41.70%35.00%1.40%
Dalvin CookVikings41.80%53.50%41.40%0.40%
Todd GurleyFalcons36.70%30.10%36.50%0.20%
Gus EdwardsRavens57.70%41.30%58.20%-0.50%
Le’Veon Bell2 teams40.60%30.30%41.20%-0.60%
Chris CarsonSeahawks45.60%55.40%46.40%-0.80%
Brian HillFalcons34.80%35.00%35.70%-0.90%
Aaron JonesPackers43.90%47.10%45.80%-1.90%
Joshua KelleyChargers32.60%29.00%35.30%-2.70%
Samaje PerineBengals50.00%25.00%53.30%-3.30%
Antonio GibsonFootball Team42.10%45.00%45.50%-3.40%
Benny SnellSteelers37.80%22.60%41.90%-4.10%
Jeff Wilson49ers43.50%40.40%49.00%-5.50%
Ronald Jones IIBuccaneers41.20%42.20%46.70%-5.50%
Nick ChubbBrowns37.00%40.00%42.60%-5.60%
Boston ScottEagles32.60%45.80%38.50%-5.90%
Darrell Henderson JrRams42.90%44.10%49.00%-6.10%
Taysom HillSaints58.30%50.00%65.50%-7.20%
D’Andre SwiftLions44.90%50.00%52.40%-7.50%
Kenyan DrakeCardinals39.00%37.40%46.50%-7.50%
Wayne GallmanGiants35.70%38.00%43.50%-7.80%
Rex BurkheadPatriots48.30%50.00%56.30%-8.00%
Raheem Mostert49ers29.00%54.30%37.00%-8.00%
Jerick McKinnon49ers23.80%38.90%33.30%-9.50%
Adrian PetersonLions30.00%32.80%40.00%-10.00%
Leonard FournetteBuccaneers35.10%35.90%47.60%-12.50%
Nyheim HinesColts30.40%40.70%43.80%-13.40%
Devin SingletaryBills34.30%41.00%48.00%-13.70%
Alexander MattisonVikings36.40%34.50%51.10%-14.70%
David JohnsonTexans35.60%22.80%51.10%-15.50%
Christian McCaffreyPanthers46.70%47.60%62.50%-15.80%
Justin JacksonChargers32.40%33.30%50.00%-17.60%
Ito SmithFalcons31.80%44.80%53.80%-22.00%
Malcolm BrownRams29.60%42.90%56.40%-26.80%
RB Box Sensitivity

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.

Chase Edmonds | Bleacher Report | Latest News, Videos and Highlights

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

How much does an RB’s Success Rate RELY on a small box size

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

What RBs Success had the smallest tie/correlation to box size

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.

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