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Finding ADP Value in Ambiguous Backfields II


Having previously defined Ambiguous backfields, the next challenge determines whether ambiguous backfields consistently generate ADP value.

In my previous article, I quantitatively defined an ambiguous backfield, and I filtered the 32 NFL teams according to my definition using 2017, 2018, and 2019 ADP data in order to find objectively-defined ambiguous backfields. Such an objective definition is absolutely necessary to analyze data without hindsight. My goal of this objective definition exercise was to challenge conventional wisdom and ascertain whether it’s worthwhile to target ambiguous backfields while the consensus sleeps on them. Today, we will test that goal and discover whether drafts can find ADP value in ambiguous backfields.

Again, the analysis begins with assumptions and a definition. The first assumption assumes that the ultimate goal of every draft pick in a fantasy draft is to draft a player who will be successful. However, success can be defined many different ways, and we need to maintain objectivity for this exercise. In investing, one way to measure success is by “beating the market”, whereby an investor seeks to purchase stocks whose rate of return outperforms the market’s rate of return as a whole. I will take a similar approach and define successful players in ambiguous backfields as players who “beat the market” by beating their respective ADP.

So, what exactly do I mean by “beating ADP”. Specifically, in the context of a fantasy serpentine draft, beating ADP means that a player’s final fantasy point finish met or exceeded his positional draft position.  In other words, if I draft the twenty-fifth running back, that running back will beat his ADP if he finishes the year as the twenty-fourth or higher running back in fantasy points. Of course, injuries happen, so a more productive comparison compares ADP to end-of-year, points-per-game ranking, by assuming that all injuries are random and cannot be predicted, so as not to skew the analysis.

So, if a player matches or beats his positional ADP according in positional points-per-game ranking, then I will consider the draft pick a success (e.g. Player A, drafted as RB30, ends the year as RB14 in points-per-game). However, simply beating ADP isn’t enough. Fantasy managers need players who are usable for their starting lineups, which means that a successful player must be “start-able”. For example, a player drafted as RB50 who ends up being RB48 in point-per-game is useless. As such, I will filter out any running backs that didn’t end up RB36 or better in points-per-game, which would be an RB3 in 12 team leagues (i.e. “flex-worthy”).

As you recall, I found ten ambiguous backfields in 2017 and eleven in 2018 applying the objective definition. Below is a table listing all the drafted running backs in those ambiguous backfields, their ADP, how they finished in fantasy point-per-game, and whether each player was considered a success or failure.

Player – 2017 ADP RB Rank RB Points-per-game Rank Success or Failure
Terrance West (BAL) 36 81 Failure
Danny Woodhead (BAL) 26 60 Failure
Javorious Allen (BAL) 65 30 Success
Joe Mixon (CIN) 20 34 Failure
Jeremy Hill (CIN) 51 102 Failure
Giovani Bernard (CIN) 61 27 Success
C.J. Anderson (DEN) 23 28 Failure
Jamaal Charles (DEN) 47 85 Failure
Ameer Abdullah (DET) 24 45 Failure
Theo Riddick (DET) 33 37 Failure
Frank Gore (IND) 35 29 Success
Marlon Mack (IND) 53 59 Failure
Mark Ingram (NO) 25 8 Success
Alvin Kamara (NO) 52 4 Success
Dion Lewis (NE) 58 20 Success
Rex Burkhead (NE) 44 18 Success
James White (NE) 42 38 Failure
Mike Gillislee (NE) 27 58 Failure
Legarrette Blount (PHI) 37 65 Failure
Darren Sproles (PHI) 46 74 Failure
Wendell Smallwood (PHI) 54 77 Failure
Thomas Rawls (SEA) 39 99 Failure
Eddie Lacy (SEA) 43 102 Failure
Chris Carson (SEA) 55 33 Success
C.J. Prosise (SEA) 49 93 Failure
Rob Kelley (WAS) 28 71 Failure
Samaje Perine (WAS) 48 50 Failure
Chris Thompson (WAS) 68 11 Success

Maintaining a team-level perspective, 2017’s numbers show that 70% of the 2017 ambiguous backfields resulted in at least one player being defined as a success. Now, let’s look at 2018:

Player – 2018 ADP RB Rank Points-per-game Rank Success or Failure
Duke Johnson (CLE) 39 37 Failure
Carlos Hyde (CLE) 25 68 Failure
Nick Chubb (CLE) 49 25 Success
Kerryon Johnson (DET) 28 18 Success
Legarrette Blount (DET) 54 82 Failure
Theo Riddick (DET) 58 50 Failure
Aaron Jones (GB) 41 17 Success
Jamaal Williams (GB) 27 64 Failure
Ty Montgomery (GB) 51 81 Failure
Marlon Mack (IND) 40 14 Success
Jordan Wilkins (IND) 53 86 Failure
Nyheim Hines (IND) 61 40 Failure
Sony Michel (NE) 35 36 Failure
Rex Burkhead (NE) 29 74 Failure
James White (NE) 42 10 Success
Isaiah Crowell (NYJ) 38 35 Success
Bilal Powell (NYJ) 44 48 Failure
Jay Ajayi (PHI) 23 34 Failure
Corey Clement (PHI) 50 60 Failure
Chris Carson (SEA) 30 16 Success
Rashad Penny (SEA) 37 83 Failure
Alfred Morris (SF) 45 73 Failure
Matt Breida (SF) 48 30 Success
Peyton Barber (TB) 31 43 Failure
Ronald Jones (TB) 43 103 Failure
Chris Thompson (WAS) 32 44 Failure
Adrian Peterson (WAS) 34 29 Success

In 2018, ambiguous backfields had even more success than 2017, this time having a successful back come from an ambiguous backfield 82% of the time! So, given a relatively small sample, we can assume that ambiguous backfields have roughly a 76% chance of producing a successful running back that will outperform his ADP! In other words, drafts can expect to consistently find ADP value in ambiguous backfields.

However, the success rate plummets when we statistically measure success at the player level. Instead of 76%, individual players had a 34% chance of being successful in an ambiguous backfield in 2017 and a 33% chance in 2018. This is to be expected, as generally only one running back per backfield becomes a success, with a few exceptions in high-powered, running back friendly offenses (see New England 2017, 2018, New Orleans 2017).

While picking the right player from an ambiguous backfield may seem like a daunting task, the above analysis proves that there is consistent value from at least someone in most ambiguous backfields. In fact, that consistency can be measured at 76%. There are few things in fantasy football that have a 76% success rate. But more importantly, the value in ambiguous backfields shown above isn’t slight; it’s significant. On average, successful running backs average a points-per-game positional finish 22 spots higher than their ADP! Therefore, a major reward can be had from investing in ambiguous backfields at their ADP. As such, fantasy players can significantly capitalize off the trepidations of the fantasy community. Remember, fortune favors the bold; but fortune really favors the bold and clever.

Ambiguous backfields offer inherent and significant value due to the lowered ADP cost caused by common consternation. Picking a player to succeed may be hard, but, by trusting the high success rates at the team level, I can develop an effective draft strategy for ambiguous backfields in 2019. I will explain that draft strategy in the third article in the series.

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