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Finding Quantitative Value in Ambiguous Backfields: Part 1


Today, we’ll discuss conventional wisdom surrounding ambiguous backfields and how to circumvent conventional wisdom to gain an edge.

Fantasy football teems with conventional wisdom, and generally fantasy managers carefully and conservatively play fantasy football blindly abiding conventional wisdom. Presumably, there is a reason conventional wisdom is, in fact, wise: because conventional wisdom has usually been proven correct, logical or useful at some point.

However, the fantasy community is getting smarter each year, which means that most fantasy football enthusiasts already at least accidentally follow vetted conventional wisdom (e.g. by drafting according to expert rankings). As a result, even in your home leagues, you are going to find a fair amount of people following conventional wisdom by drafting QBs late, drafting defenses and kickers in the final two rounds, and using tiers to draft.

But the collective rising of fantasy IQ results in strengthened competition, and in order to win according to conventional methods, you would need to be the best conventional player. Alternatively, you can win by zagging when everyone else is zigging.

Presently, conventional wisdom suggests that you should avoid ambiguous backfields, and instead, draft other positions (usually WR) where running backs in ambiguous situations are being drafted. But instead, can we capitalize on the majority subscribing to this common recommendation?

Spoiler alert: we can. In fact, we can quantitatively measure backfield ambiguity, we can determine that quantitatively-defined ambiguous backfields are likely to produce at least a flex-worthy running back having value against ADP, we can generate a draft strategy capitalizing on the fantasy community’s apprehension regarding ambiguous backfields, and we can find data indicating which backfields are most likely to produce value.

This article is the first article in a series of articles that analyzes backfield ambiguity and describes the quantitative process used to find which backfields are relevantly ambiguous, meaning the backfields that can offer value vs. ADP.

So to begin, let’s quantitatively define an ambiguous backfield, thereby avoiding any differing subjective opinions. That is, instead of setting an arbitrary threshold in terms of touches or carries, let’s leverage conventional wisdom and consensus to identify ambiguous backfields. Moreover, once an objective definition is formed for an ambiguous backfield, we can apply the definition to historical data to objectively study the past.

Leveraging consensus requires some assumptions. First, we assume that fantasy players minimize risk at the draft. Under this assumption, “workhorse running backs” will go early, and “muddled backfields” will get dropped in rankings and ADP. That is, we assume that fantasy players will draft perceived guaranteed opportunity (i.e. low risk) using early-round picks, and fantasy players will be willing to take chances on higher risk players only with later-round picks. As such, ambiguous backfield situations are measurable simply by analyzing ADP. Let’s trust consensus and use measurable fantasy player perception, in the form of average draft position, to find ambiguous backfields.

Historical data supports this assumption. I began with ADP data between 2017 and 2018. Over the last two years, almost all workhorse running backs went in the first three rounds. Round 3 may seem like an arbitrary line, but it actually isn’t. In 2017, 19 RBs were drafted in the first three rounds, and in 2018, 19 RBs were again drafted in the first three rounds.[1] For reference, RB20 was drafted at pick 4.02 in 2017 and 4.06 in 2018, so it’s right around the round-four turn where the fantasy community begins to waver over who is a workhorse running back and who isn’t. While RB20 might seem like a better cut-off, I prefer the three-round threshold because the number of perceived workhorses is bound to change from year-to-year, even if it didn’t in 2017 and 2018.

Now, we need additional inputs to define ambiguity because some backfields drop in ADP because the consensus doesn’t believe that a certain team’s running game is very good. So, we need a second assumption: ambiguous backfields are ambiguous because the fantasy community is torn between two or more backs in a backfield. Thus, an ambiguous backfield not only lacks a running back drafted in the first three rounds but also has two running backs drafted between rounds four and twelve or three running backs drafted between rounds four and sixteen.[2] Round twelve is also likely arbitrary as well, but analysis of ADP data from 2017 and 2018 suggests that most of the handcuffs for those workhorse backs being drafted in rounds 1-3 begin getting drafted in rounds 13-16, with a few exceptions.

So, applying these simple rules to 2017 and 2018, the tables below show the “quantitatively measured ambiguous backfields” for the past two years.

2017 Teams having Ambiguous Backfields Running Backs in Ambiguous Backfield
BAL Danny Woodhead, Terrance West
CIN Joe Mixon, Jeremy Hill, Giovani Bernard
DEN CJ Anderson, Jamaal Charles
DET Ameer Abdullah, Theo Riddick,
IND Frank Gore, Marlon Mack
NO Alvin Kamara, Mark Ingram, Adrian Peterson
NE Dion Lewis, Rex Burkhead, James White, Mike Gillislee
PHI LeGarrette Blount, Darren Sproles, Wendell Smallwood
SEA Thomas Rawls, Eddie Lacy, Chris Carson, C.J. Prosise
WAS Rob Kelley, Samaje Perine


2018 Teams having Ambiguous Backfields Running Backs in Ambiguous Backfield
CLE Duke Johnson, Nick Chubb, Carlos Hyde
DET Kerryon Johnson, LeGarrette Blount, Theo Riddick
GB Aaron Jones, Ty Montgomery, Jamaal Williams
IND Marlon Mack, Jordan Wilkins, Nyheim Hines
NE Sony Michel, Rex Burkhead, James White
NYJ Isaiah Crowell, Bilal Powell
PHI Jay Ajayi, Corey Clement
SEA Chris Carson, Rashad Penny
SF Alfred Morris, Matt Breida
TB Peyton Barber, Ronald Jones
WAS Chris Thompson, Adrian Peterson


Pretty impressively, 2017 and 2018 have almost the exact number of ambiguous backfields. As such, it appears that we are onto something.

From the names above, savvy readers will remember that many of these backfields produced a solid fantasy producer. Without spoiling my future articles, I will tell you that most of these ambiguous backfields generated significant value v. ADP.

Let’s apply those filters to 2019 and find our ambiguous backfields for 2019. This year, we get 10, but I am going to exclude the Bears from this exercise because David Montgomery and Tarik Cohen are both being drafted highly (5th round or higher). With such high draft prices, we are far less likely to find significant value in the Bears backfield. So, here is our 2019 ambiguous backfield list (current ADP in parenthesis).

2019 Ambiguous Backfields Running Backs
BUF LeSean McCoy (9.03), Devin Singletary (12.06)
DEN Phillip Lindsay (4.07), Royce Freeman (8.08)
HOU Lamar Miller (6.08), Donta Foreman (9.08)
NE Sony Michel (5.02), James White (5.07), Damien Harris (10.01)
PHI Miles Sanders (7.11), Jordan Howard (8.05)
SEA Chris Carson (5.05), Rashaad Penny (7.02)
SF Tevin Coleman (6.03), Jerrick McKinnon (10.07), Matt Breida (13.10)
TB Ronald Jones (8.04), Peyton Barber (11.09)
WAS Darrius Guice (7.01), Adrian Peterson (10.12), Chris Thompson (13.09)


So, what can we learn from this fairly rudimentary exercise?

  • First, we can expect that every year approximately 9-11 NFL teams will have an ambiguous backfield. 
  • Second, due to the widespread application of conventional wisdom, we learned that we can trust consensus to find risky situations measurable through ADP.

You may be thinking that this process was pointless since you could have picked the same list subjectively, but this process is actually quite valuable because the objective definition allows us to analyze previous year’s data without hindsight. Additionally, the definition provided is measurable today. Almost everything else at this point in the fantasy season is a projection or expectation. We certainly expect Ezekiel Elliot to lead the Cowboys in opportunity, but so does the entire fantasy community. Rather than splitting hairs over projections, this exercise generated a tangible list of backfields that are almost guaranteed to generate value.

Almost guaranteed, you say? Indeed, it is only because of this definition and filtering process that we can find truly meaningful data, which I will reveal in the next article. In the next article in this series, I’ll analyze the historical data to show you just how strong of a guarantee that is.

[1] All ADP data from in 1-QB, PPR leagues

[2] I also needed to adjust any backfields having a backfield leader who was injured or suspended at the beginning of the year (e.g. Doug Martin in 2017).

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