Featured Content Front Page Management Theory

The Return of Taco Tuesday: Siren Song of Statistics

Jan Michael Larkin

Welcome to the second installment of Taco Tuesday with JanMichaelLarkin, in which I will be discussing how to selectively use statistics to sell trades that increase the value of your team.  Before we begin, I feel it necessary to mention some concerns that were raised upon the posting of my first Taco Tuesday article (conveniently linked here).  Some users felt that my tone was a bit predatory and that trading should be approached from a more mutualistic standpoint, arguing that trading from a zero-sum perspective can foster a corrosive mindset in your league.  To all of this I say- while I understand your concerns, fantasy football as I play it (I can only speak for myself) is meant to be a harmless game, and as it is just a game I feel that the ethics that apply to it need not be as rigorous as those you would apply to normal life.  Or, to put it more simply, all’s fair in love and fantasy football.  If you feel differently I respect that, but please limit your posts in the comments to the subject at hand (fantasy football), rather than discussions of ethics.  However, I will begin this post as always with the guidelines to which I *do* adhere.

JML Trading Guidelines

  1. Know your audience.  If your trade partner does particularly thorough research before making a trade, avoid the use of selective statistics.  If they are disdainful of “experts”, avoid appealing to authority.  If they undervalue or overvalue draft picks or will overpay for players from their favorite team, be aware of it.  Tailor your approach to your target.
  1. Don’t be pushy, don’t make insulting offers, and be willing to take no for an answer (eventually).  It is not in your best interest to alienate potential trade partners by aggressively pursuing trades in which they’re not interested or by offering trades that insult their intelligence.  Having a more relaxed attitude and approach makes your offers seem less suspicious and keeps you from developing a reputation as a shark, which can make trading difficult.
  1. Avoid being “that guy”.  While the point of this guide is to help add value to your team at the expense of others, sufficiently lopsided trades have the potential to ruin leagues.  You want to be the guy who got his less-savvy friend to trade him Keenan Allen for 1.07, not the guy who got his ten-year-old cousin to trade him Amari Cooper for Tom Brady.

As always, if you bear these guidelines in mind, logical fallacies and misleading statistics can be your friends.  Today we will be focusing on three specific types of misleading statistical argument- false equivalence, the use of hidden variables, and reductio ad absurdum.

 

Part 1- False Equivalence

False equivalence is exactly what it sounds like- the fallacious claim that two inequivalent things are equivalent.  The gist is that you highlight a few ways in which a mediocre player compares favorably to a great player, and claim that the mediocre player must therefore in fact be great (or the great player mediocre, depending on if you’re buying or selling).  This strategy works best when the physical measurables of a player are out of sync with their production, as such a situation allows for comparisons that focus on the positive while conveniently ignoring the negative (or vice versa).

A big-name pedigree receiver with elite physical attributes hasn’t broken out yet?  If you’re selling, his measurables are still equivalent to whatever superstar you had hoped he’d become; if you’re buying, his production in a vacuum is equivalent to any number of hyped busts that now litter NFL practice squads.  A 6th-round nobody burst onto the scene to tie for the league lead in rushing TDs?  If you’re buying, he’s a fluke who still has the same measurables that got him drafted in the sixth round; if you’re selling, his production is every bit as valuable as that of the blue-chip pedigree stud who commands double the price.  Below I have included what I feel are examples of buys and sells for players who have both over- and under-produced relative to their measurables thus far, along with players to whom they could be fallaciously compared.

 

Jordan Howard

In 2016 the young Bears running back put up numbers that bordered on elite despite lacking the measurables typically associated with elite production.  Considering that he achieved his numbers behind a banged-up offensive line and with an inconsistent surrounding offense, I believe that he is no fluke, will continue to excel, and is a strong buy.

When buying, it can be advantageous to draw comparisons between Howard and Jeremy Hill.  Jeremy Hill had similarly below-average measurables and low draft stock but produced surprisingly gaudy RB1 numbers in a favorable system in Cincinnati in 2014, only to lose much of his volume to Giovani Bernard the following year.  Howard is an average-at-best athlete with poor draft stock who put up impressive production in 2016 and faces questions going into the upcoming year, with some owners worried he could lose at least some if not all of his job to a newly drafted or signed running back.

I believe the Hill comparison is fallacious for a number of reasons (shout out to u/bastegod here), the most convincing being that there was a significant shift in offensive scheme and philosophy in Cincinnati following Hill’s big year and something similar seems unlikely in Chicago).  If you can convince your trade partner that Howard will follow a similar regression to Hill you can likely get him at a discount.

 

Kelvin Benjamin

Kelvin Benjamin has exactly one thing working in his favor- size.  Beyond that, he is a slow, unathletic, lazy plodder of a wide receiver who has managed decent to good production so far mostly because of a strong offense that made him a focal point upon arrival (though this seems to be trending in the wrong direction).  I consider him a strong sell.

To do so, I would recommend comparing him to Mike Evans.  While no one will take this comparison at face value or give you a comparable price, there are enough parallels to be drawn that an Evans comp can serve to make Benjamin significantly more appealing.  They are both big, young receivers with pedigree who were drafted to be the WR1 in a powerful offense led by a young stud quarterback.  It could be argued (though perhaps not with a straight face) that it is only the regression of Newton and the Panthers’ offense as a whole that kept Benjamin from putting up Evans-like numbers.

Obviously, this comparison is ridiculous.  While Evans has trended all the way up to become the top dynasty asset, Benjamin has been consistently trending down and shows no signs of stopping.  Reports from multiple sources for years now have implicated him as being lazy and unmotivated, and he has yet to learn to use his big body to the full advantage that Evans does.  However, if you can make it seem realistic that something like Evans is Benjamin’s ceiling his value could go up significantly in your trade negotiations.

 

Jerrick McKinnon

Jerrick McKinnon is probably one of the most athletic humans on the planet.  He grades out in the 89th percentile or above in all major measurable categories for RBs and possesses the highest SPARQ score (Nike’s proprietary measure of athleticism) of all time at 155.7.  However, he has spent the beginning of his career stuck behind a Vikings line that has been among the worst in the league and as such he has yet to put his freakish abilities to good use.  Considering the recent free agency signings by Minnesota he should have better blocking in 2017, so I currently consider him a buy.

To facilitate buying I recommend comparing him to recent Titans bust Bishop Sankey.  While Sankey doesn’t have quite the measurables that McKinnon does, he was still an explosive athlete with a SPARQ score in the 97th percentile.  He was drafted more highly than McKinnon and expected to become a quality starter, but was never able to transition successfully from the college game to the pros.  He can be used as “proof” that you should take it at face value when a running back fails to produce, regardless of how athletic he may be.

I don’t believe McKinnon will follow the career path of Sankey for several reasons.  For one, most people don’t really expect him to become a true feature RB that completely dominates the backfield touches.  He can be an extremely successful NFL and fantasy RB without ever becoming particularly proficient at running between the tackles.  I also distinguish between the two of them because while Sankey was an exceptional athlete, McKinnon is a borderline superhero.  Do your best to convince your trade partner that McKinnon is the next Sankey, and reap the benefits.

 

Kevin White

Like McKinnon, Kevin White is a physical freak.  His world-class size and speed were alluring enough for the Bears to roll the dice and pick him seventh overall, a move that has yet to pay off.  Owners have been clenching their teeth and their buttcheeks progressively harder since drafting him, and I am of the opinion that their patience will not be rewarded- I currently have White as a strong sell.

There are no shortage of third-year breakout receivers who could make enticing comparisons for White, but I recommend Demaryius Thomas.  Like White, he is a size/speed freak who struggled with injuries his first two seasons and failed to truly excel (at this point gloss over the fact that Thomas flashed when on the field whereas White has not looked the part thus far) until his third year, at which point he broke out for ten TDs and 1400+ yards.  White could be the next receiver in the DT mold- or so you hope your trade partner will believe.

Like all my others, this comparison doesn’t really work.  As previously mentioned, Demaryius Thomas missed games in both of his first two years due to injury, but when on the field he still managed to put up two and four touchdowns in his first two years, respectively, and to crack 500+ yards in year two.  There was every reason to believe that the potential was there going into DT’s third year, whereas White has given us far more reason for pause.

When negotiating trades of this sort be aware of whether your league places a premium on potential or on proven production, and take advantage of any market imbalance that exists there.  The premium in leagues made up of newer players tends to skew towards production (they want guys whose names they know), and then (at least as far as I’ve observed) it tends to swing in the opposite direction as time goes on.

 

Part 2: Misleading Metrics

Statistics are not to be trusted.  Virtually any metric can be rendered misleading if framed properly, but I’ve found that there are a few in particular that lend themselves especially well to creative manipulation.  I have broken down my favorites below.

Metric: Yards-per-Attempt (YPC and YPR)

When it Works: When applied to a player with a well-rounded skill set who operates within a relatively average offense.

When it Doesn’t: When a player operates within a specialized role (such as a goal-line back with low YPC, a possession-specialist WR with low YPR, or a deep threat-specialist WR with high YPR).  Also when a running back plays behind a particularly effective offensive line or is used in a limited capacity, thus artificially inflating their YPC.

How to Use It: Use YPR to downplay value when buying possession-specialist WRs, or as a selling point when selling deep threat-specialist WRs.  Use YPC to downplay value when buying a goal-line specialist RB or when buying a talented RB who has been stuck in a bad offense, or use it as a selling point when selling an RB who has been benefiting from an excellent offensive line or from limited use.

Relevant Fantasy Players Affected: Chris Hogan (17.9 YPR), Marvin Jones (16.9 YPR), Stefon Diggs (10.8 YPR), Golden Tate (11.8 YPR), Mike Gillislee (5.7 YPC), Bilal Powell (5.5 YPC), Melvin Gordon (3.9 YPC), Todd Gurley (3.2 YPC)

 

Metric: Touchdowns

When it Works:  When a player is so exceptional that they can’t help but consistently find the endzone (at which point their excellence should be self-evident anyway).

When it Doesn’t:  Pretty much any other time.

How to Use It: Since touchdowns are affected by so many different variables they are one of the most volatile and unpredictable metrics in fantasy football.  The savvy manager always assumes that touchdown numbers will regress to the mean, so this volatility is best used by selling players with unusually high TD numbers and buying players who scored fewer TDs than their talent and situation would seem to indicate they should score.

Relevant Fantasy Players Affected: LeGarrette Blount (18 TDs), Latavius Murray (12 TDs), Jeremy Hill (9 TDs), Spencer Ware (3 TDs), Sterling Shepard (8 TDs), Amari Cooper (5 TDs), Stefon Diggs (3 TDs), Travis Kelce (4 TDs)

 

Metric: Fantasy Rank (Positional)

When it Works: When analyzing exclusively healthy, elite players.

When it Doesn’t: When analyzing anything but healthy, elite players.

How to Use It: Positional rank by fantasy points takes into account absolutely nothing but raw numbers, leaving a million different ways for hidden variables to skew what those numbers mean.  This is not typically particularly relevant for the players at the very top of the rankings, as their value is self-evident, but there are often players in the 10-30 range whose talent does not justify their presence.  Most often this is a result of a decent-to-slightly-above-average player managing to stay healthy for an entire season and getting good supporting play from his offense along with a few lucky breaks (or they may simply be the only option in their offense).

There are also “runs” of players who are separated by a couple of points or less- for example, the WR7 last season (Michael Thomas) only scored 2.3 points more over the course of the entire season than the WR10 (Brandin Cooks). If you scan through the top scorers in your league by position, players who are higher than they should be can become apparent and their “rank” can be used as a selling point in trade negotiations.  Talented players who missed time or were stuck in a bad offensive situation will also have ranks not in line with their skills, which can be used when attempting to purchase them.

Note: all positional ranks based on standard scoring + 0.5 PPR.

Relevant Fantasy Players Affected: Tyrell Williams (WR15), Rishard Matthews (WR18), DeAndre Hopkins (WR36), Stefon Diggs (WR44), Latavius Murray (RB13), Spencer Ware (RB16), Todd Gurley (RB20), Lamar Miller (RB22)

 

Part 3- Reductio Ad Absurdum

Reductio Ad Absurdum refers to a type of argument in which you take apparently logical premises and draw them out to reach an extreme and illogical conclusion.  In fantasy football it refers to the practice of projecting the stats of a player who currently receives limited touches (such as a committee RB or gadget WR) to what they would be if said player were receiving greater volume (if they became a feature RB or “true” WR), conveniently assuming that efficiency and continued production would be a given.

This strategy in particular tends to be rejected out of hand by more savvy owners, but in some cases the gaudiness of the projected numbers can lead to impulsive decisions on the part of buyers (as always, know your audience).  A few players whose partial numbers project out impressively in what is likely a misleading way include:

Notes: all projections are based on standard + 0.5 PPR scoring, APG= attempts-per-game, FP= fantasy points, FPPG= fantasy points-per-game.

Player: Jalen Richard

2016: 6.3 FPPG at 5.2 APG

Projects to: 18.9 FPPG at 15.6 APG (the average of the top 20 RBs by usage in 2016), or 21.1 FPPG at 17.45 APG (the average of the top 10 RBs by usage in 2016)

 

Player: Tyreek Hill

2016: 10.5 FPPG at 5.3 APG

Projects to: 11.2 FPPG at 5.7 APG (the average of the top 20 WRs by usage in 2016), or 12.1 FPPG at 6.1 APG (the average of the top 10 WRs by usage in 2016)

 

A similar strategy can be used when projecting stats from a partial season out to a full sixteen games for players who were injured or who secured a starting job midseason.  Examples of this include…

Player: Kenneth Dixon

2016: 68.2 FP in the last 7 games, ended up the RB51 with 87.4 total FP

Projects to: 155.9 FP in a full 16 games, which would have made him the RB20

 

Player: Tevin Coleman

2016: 175.6 FP in 13 games played, was the RB17

Projects to: 216.1 FP in 16 games, which would have made him the RB11

 

And that’s all for today,!  Thanks as always for reading and tune in next Taco Tuesday (or Wednesday… or Thursday… who knows, just think of it like Rick and Morty Season 3) for my tips on how to appeal to that trickiest of emotions, fear.

About the author

Jan Michael Larkin

Jan Michael Larkin

By law, can only be in one quadrant at once.

Leave a Comment