Looking For Market Inefficiencies: RB Return On Investment

Antonio Losada's top fantasy football running back values and market inefficiencies to invest in for 2020 NFL drafts using ADP and historical year-end finishes.

When it comes to Return on Investment (ROI), I keep things as simple as I can. I just take a player's ADP entering the season and divide it by his season-end final ranking among all players in the league. That's because every player is available in drafts and therefore every player should be taken into consideration for the ranking.

Any player with an ROI at or over 1.0 yielded a positive value and therefore turned into a valuable play for his fantasy owner. Any player with an ROI under 1.0 finished the year in a position lower than that in which he was drafted. Although there is virtually no limit in how large (positively or negatively) an ROI-mark can be, we can assume the lowest value is 0.001 (ADP 1, rank over 500) and the largest 500 (ADP 500, rank 1).

I already looked at the wide receiver position, and the quarterback position, and it's time to tackle running backs now including every RB season from 2000 to 2018 (1,091 in total), with ROIs ranging from 0.01 to 67.6. It only includes players from which I know their ADP in years N and N+1, and their ROI marks for years N and N+1 too.

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Year-to-Year ROI to ADP Correlation

What I wanted to test was a pretty simple idea: If a player exceeds his value in year N, we can assume he will become more expensive in year N+1 and therefore he would be less valuable in terms of potential ROI. Think of Alvin Kamara. He entered the 2017 season with an ADP of 118.4, yet he finished ranked as the fourth-best player of the year and the RB3. Obviously, his ADP in 2018 went all the way up to 7.8, more than 100 spots more expensive! No wonder his ROI in 2018 was an incredible 29.6 but it dropped to just 1.3 (still a value play, but almost insignificant) in 2019 as he finished sixth overall and as RB4.

This is how the relationship between the season-end rank in year N and the ADP in year N+1 has gone through the last couple of decades (negative changes mean lower ADPs, that is, higher draft positions).

There is, in fact, a positive correlation up to an R-Squared value of 0.03 between both variables. The better a running back has done in year N, the more expensive he has gone the following season.

With all we know by now, we should expect a similar relationship to exist between the ROI in year N and the ADP in year N+1. We should assume a player beating his expected value would be drafted higher the next year.

Absolutely correct. The correlation here is positive again, and it actually improves on the strength of the last one with an R-Squared value of 0.08 this time!

This means one thing: Fantasy owners focus more heavily on final raw results rather than the value returned by the players given the paid price.

While that is nothing unreasonable (we're bumping up the prices and paying more for the best performers), it is not the best way to tackle the market. It is an inefficiency. It's a good strategy, but it is not the smartest one nor the one benefitting us the most. That's why there is still a window there to go grab the best possible values that are not yet inflated.

What History Tells Of Changes In ADP And ROI

Since the 2000 season, and looking only at RB seasons from players of whom we know their year-to-year changes in ADP, Rank, and ROI, this is how the numbers look like:

• 140 players became more expensive while improving their ROI (acceptable investment)
• 344 players became more expensive while lowering their ROI (worst investment)
• 212 players became cheaper while lowering their ROI (acceptable investment)
• 389 players became cheaper while improving their ROI (best investment)

In percentages, we can say that 32.4% of players remained in the balance, 31.7% became worse plays from year N to year N+1, and 35.9% became better plays. Those are three almost evenly split numbers, but if we add together the first and the last ones we get to 68.3% of players at least retaining their ROI values from one season to the next one.

What we should try to identify are the commonalities among those in the remaining group of players in order to try and avoid them. I tried to find some similar numbers and traits repeating themselves in their profiles to get to a sound conclusion.

Avoiding ROI Fallers

The 31.7% of players becoming worse plays from year N to year N+1 (that is, more expensive in terms of ADP while providing worst ROI-marks at the end of the season) make for 182 players in my data set ranging from 2000 to 2018.

There is a boatload of data to unpack there, so let's go step by step.

• The majority of players were between 23 and 25 years of age.
• Virtually "every" player came from playing a full 16-game season, with the rest mostly at 13+ games played.
• Most of the players logged either between 230 and 300 rushing attempts or between 50 and 100.
• In terms of yards, most reached either between 230 and 465 rushing yards or between 900 and 1200.
• Most players scored between 0 and two rushing touchdowns, or between five and seven.
• Most players had between 15 and 55 targets.
• Most players had between 10 and 30 receptions.
• The majority of rushers logged fewer than 425 receiving yards.
• Most rushers scored one receiving touchdown at most.
• The peak on PPG was in the 12.5-to-15.5 clip, with most players finishing the year averaging between 10 and 16.

Here are the players from 2018 with similar/close profiles to that one at the year's end, and how they did in 2019.

We were this close to making it six-for-six with this group in terms of ROI change from 2018 to 2019, as five of the six highlighted players dropped their ROI or kept it the same with only one (Jordan Howard) barely improving it (+0.2).

All of these rushers, with the exception of Chris Carson, scored fewer PPR points in 2019 than they did in 2018 and their overall rank at the end of the year was 51 spots lower on average in 2019 than it was in 2018, with just Carson finishing inside the top-50 players of the year (48th). Also, all of them except Carson saw a drop of -2.4 PPG on average from one year to the next one.

Finding ROI-Risers

The same process can be followed to try to identify traits present in all of the historical ROI risers in order to find what has repeated over the years in their profiles to take advantage of it going forward. This is how all of the players in the data set that became cheaper but better ROI values are distributed in different stats.

And some of the shared similarities:

• The majority of players were between 24 and 25 years of age, or between 27 and 28.
• Virtually "every" player played between 14 and 16 games.
• Most of the players logged fewer than 170 rushing attempts.
• In terms of yards, most reached fewer than 490 rushing yards.
• Most players scored between zero and three rushing touchdowns.
• Most players had fewer than 35 targets.
• Most players had fewer than 24 receptions.
• The majority of rushers logged fewer than 185 receiving yards.
• Most rushers scored no receiving touchdowns at all.
• The peak on PPG was in the 5.5-to-8 clip, with most players finishing the year averaging between 5.5 and 12.5.

Here are the players from 2018 that would have fit a similar/close profile to that one at the year's end, and how they did in 2019.

Three of the four identified players improved their ROIs, and although Latavius Murray had a negative change it was only of -0.1 going from 1.1 to 1.0, making him a neutral play given his ADP and fantasy points return. Of the four, only LeSean McCoy ranked worse in 2019 than he did in 2018 (144th overall compared to 140th) while the other three improved at least 24 spots and up to 65 (Carlos Hyde went from 176th to 11th in 2019).

The running back position is quite unpredictable and finding ultra-valuable plays is always going to be hard, with the most valuable ones most probably flying completely under the radar and being hard to identify even using this model.

Potentially Great ROI Plays for the 2020 Season

Now that we have identified stats that fit the model for both good and bad "next-year ROIs", we can try and apply it to the current season trying to take advantage of our knowledge to build the best possible roster in 2020. Here are some 2019 players that fit the profile of the average ROI-Riser.

To be honest, most of the found players make all of the sense in the world:

• Peyton Barber had a down season in Tampa as part of a committee, which will make his ADP drop and boost his ROI even he puts on a similar fantasy performance next year.
• Deandre Washington's role in Oakland will undoubtedly be the No. 2 behind Josh Jacobs, another reason to think owners will skip him making drafting/adding him a very cheap play with high upside.
• Jamaal Williams is the second fiddle to Aaron Jones, but Green Bay will keep using him in some sort of committee. He will never reach the ADP of Jones--nor his numbers, mind you--but he also brings a really high upside and handcuff potential.
• James White doesn't need much explanation. He's not the No. 1 rusher of New England by the numbers, but his pass-catching ability makes him much more valuable than Sony Michel even if most fantasy owners still haven't realized.
• Tarik Cohen as a very similar profile to that of James White, basing most of his fantasy value on the pass-catching side of the game, where he excels but will always remain a cheap play as he's not a bell-cow.
• Duke Johnson is close to White and Cohen in that he also sees tons of targets, but also adds some fantasy value through the rushing game. Another potentially cheap play with a chance at having a high ROI-mark given his ADP.

Here are the actual ADP values of the aforementioned players in best-ball leagues as of this writing.

None of them is currently going off the board earlier than at 87th spot (James White) and even that amounts to more than seven full rounds of picks. The value to extract from any of those players is really high and given their historical comps the odds are all of them have more than valuable seasons in 2020.

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