Looking For Market Inefficiencies: WR Return On Investment

No matter which type of fantasy owner profile you fit into, you're looking for market inefficiencies as we all are. Why pay an extra-high price for something that ultimately will yield the same results as something similarly good going at a steep discount? That question has no reasonable answer.

The question you might be asking yourself, though, is the one regarding how and where to find market inefficiencies to take advantage of? That's the trick. We can look at correlations between different stats, try to find what goes cheap yet is still productive, etc. One way of measuring how good our draft was in terms of price/production, that is, value or what we come to call Return On Investment (ROI), is to just take two data points in consideration: ADP (where we draft players) and season-end rank (where the player ends the year ranked at in fantasy points). Just using those two values we can easily calculate how valuable our picks were.

The concept of ROI got me thinking about how one year's values relate to the next one, and how they impact future ADPs in fantasy football. That's why I tried to explore the relation and arrive at some conclusions trying to find out if there is anything to exploit there.

Return On Investment Values

When it comes to 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).

For this research, though, I'm using a dataset containing every wide receiver season from 2000 to 2018 (1,380 in total), with ROIs ranging from 0.02 to 24.83. 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.

This is how ADP and ROI correlate in the same season N.

The correlation is almost nonexistent with an R-Squared value of 0.001. It makes sense, considering at the point of drafting we don't really know whether a player will be good or not. We make our best guesses and are mostly right when it comes to ADP and final rank (the R-Squared there goes up to 0.30), but the relationship between draft position and ROI is totally random.

Year-to-Year ROI Stability

If the relation between ADP and ROI is barely existent--if at all--the relationship of ROI marks from one year to the next one should be expected to be absolutely random too...

...and that is precisely the case. The R-Squared value here drops even more down to 0.0009. Don't give this relation even a split of a second of your free time, as you'd be basically throwing it away.

My first takeaway from this was that, if ROI isn't predictable at all, it must be related to the fact that a player exceeding his level in year N would translate into a higher ADP (meaning a more expensive draft position) in year N+1, thus lowering his potential ROI no matter what (the more expensive the player, the lower his ROI becomes). I had no real knowledge of the correctness of that thought, but I had the data to try and back it up.

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 Tyler Boyd. He entered the 2018 season with an ADP of 258.7, yet he finished ranked as the 51st-best player that year and the WR17. Obviously, his ADP in 2019 went all the way up to 63.2, almost 200 spots more expensive! No wonder his ROI in 2018 was an incredible 5.07 but it dropped to just 1.17 (still valuable) in 2019.

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.

There is, in fact, a positive correlation up to an R-Squared value of 0.06 between both variables. The better a wide receiver has done in year N, the more expensive he was 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, yet it doubles the strength of the last one with an R-Squared value of 0.14 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 wide receiver player-seasons from players whom we know their year-to-year changes in ADP, Rank, and ROI, this is how the numbers look:

• 176 players became more expensive while improving their ROI (acceptable investment)
• 416 players became more expensive while lowering their ROI (worst investment)
• 274 players became cheaper while lowering their ROI (acceptable investment)
• 435 players became cheaper while improving their ROI (best investment)

In percentages, we can say that 34.5% of players remained in the balance, 31.9% became worse plays from year N to year N+1, and 33.6% became better plays. Those are three very evenly split numbers, but if we add together the first and the last ones we get to 68.1% 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 31.9% 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 416 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 at or under 25 years of age.
• Virtually "every" player came from playing a full 16-game season, with the rest mostly at 15 games played.
• The vast majority of players logged between 85 and 155 targets, peaking at the 85-100 and 120-135 clips.
• Most of the players logged between 50 and 85 receptions.
• The greatest number of players fell in between 635 and 1405 receiving yards by the end of the season.
• Most players scored between four and eight touchdowns.
• The peak on PPG was in the 10-to-13 clip, with most players finishing the year averaging between 11 and 17.

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

Only two of those players (Amari Cooper and Chris Godwin) improved their ROIs from 2018 to 2019, and only Godwin in more than 0.85 points finishing 2019 with a really great 3.07 ROI. Cooper's 0.94 didn't even reach the minimally acceptable return of 1.0, and only Kenny Golladay's 1.61 and Adam Humphries' 1.03 were on the "positive" side of things.

As we already know, playing for the highest ROI doesn't mean getting the best players. Cooper finished the 2019 season as the 36th-best player overall but his owners paid an average ADP of 34 for him. Golladay was 34th on the season, only two spots over Cooper, but his cost was of just ADP 55, almost two rounds cheaper and therefore much more valuable.

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 at or under 24 years of age, or older than 26.
• Virtually "every" player came from playing a full 16-game season, with the rest mostly at 15 games played.
• The vast majority of players logged between 30 and 115 targets.
• Most of the players logged between 20 and 60 receptions.
• The greatest number of players fell in between 435 and 875 receiving yards by the end of the season.
• Most players scored between zero and one touchdowns, and the great majority fewer than six.
• The peak on PPG was in the 9-to-10 clip, with most players finishing the year averaging between eight and 12.

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

I found nine players fitting the profile. Of them, two (Michael Crabtree and Antonio Callaway) could be left out as they retired at the start of the season (Crabtree) or were under suspension and were released soon enough as to not be considered fantasy-relevant at any point (Callaway).

Focusing on the other seven players, only Donte Moncrief had a notable drop in ROI from 2018 to 2019 (1.39 to 0.29) and thus a very disappointing season. Tyrell Williams' ROI dropped from 1.37 to 1.29 but he still remained a valuable player. Every other of the highlighted/found wide receivers improved their ROIs with Courtland Sutton and Demaryius Thomas having the biggest bumps at 0.59 (Sutton went from 1.25 to 1.84, and Thomas from 0.54 to 1.13).

Every player except Moncrief and Thomas finished 2019 ranked lower than they did in 2018, and even with that Thomas still improved his ROI, making him a reasonable bet in fantasy leagues.

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.

I'm going to confess and let you know that I'm very excited about what the spreadsheet spit out:

• Even in an absolute run-heavy offense such as the Niners, Deebo Samuel had a great season and should only get better next year, so go buy high on him no matter what.
• Diontae Johnson's debut was great even under a bunch of replacement-level players manning Pittsburgh's QB position, which bodes well for him and his potential sophomore breakout.
• In the same environment as Johnson, James Washington turned out to be the second-best receiver of the Steelers and after having an ADP of 45 in 2019 you can expect it to go down this year giving you a great chance of extracting great value from him considering his low price.
• Chris Conley's first season playing under Gardner Minshew was great, with the wide receiver logging the highest AYA of players targeted at least 75 times by the rookie-quarterback last season.
• Both Randall Cobb and Mohamed Sanu are a couple of veterans who will probably drop in ADP after finishing the year out of the top-100 players of the 2019 season. Even with that, both returned ROIs of 1.97 and 1.18, being widely undervalued in almost every league.

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 70th spot (Deebo Samuel) and even that amounts to almost six 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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