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NFL DFS Experiment: Puzzle-Piecing Paradigms Week 2
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Didn’t get a chance to check out the intro to this NFL DFS Experiment? Look back to last week.

For those that are too lazy to go back, here is the gist of the experiment.

My quest?

Tap into different minds and new paradigms and create a unique, but viable strategy for large field GPPS on DraftKings.

How?

By tasking smart people to provide me with individual criteria they felt would be successful or trends they wanted to have tracked and then meshing them all together to create GPP teams.

Week Two (actually Week Three)

There were some slight tweaks to criteria for week two, the changes have been bolded:

  • Colin Drew – Pairing of two pass catchers with a quarterback priced at less than $7,000.
  • Mike Leone – at least one wide receiver from the $7,000 range. (I used two to make this criteria stand out a bit more and because the value-centric criterion provided by others.)
  • Drew Dinkmeyer – running backs priced at $5,000 or below with an average of two receptions per game.
  • DFSalbatross – tight end from a game with a total equal to or greater than 45.
  • ME! Logan Hitchcock – WR in the flex at less than $5,000.

premium_access_now  WEEKLY PROJECTIONS  |  DST PROJECTIONS  |  K PROJECTIONS  |  TARGETS  |  CARRIES  |  HISTORICAL  |  SNAPS  |  DEFENSE

Lineup Construction

I fixed the few pre-lineup building errors from last week by making sure each criterion was explicitly reviewed with the creator. I also changed the tournament I entered, moving the entry fee down to $1 and creating ten lineups to grab a larger sample size for criteria analysis.

Since I created ten lineups, I won’t post an individual screenshot of each team, but here they are with point total and result.

Screen Shot 2016-09-26 at 10.29.55 AM

Trend Analysis

Reminder on Tracking From Last Week:

Tracking the success of these trends is somewhat unfair – especially since they tie to each other and restrict the player pool at each position (particularly if a trend affects more than one player on the roster) but, I’m going to track them anyway. Using a made up statistic called APPA (average points per player affected).

For example, Colin Drew’s trend affects three players on each team. By totaling their points and finding the average, I can see how large a footprint his trend left on a team on average.

Here are the totals from this week:

Trend APPA
<$7,000 QB + 2 Pass Catcher Pair 18.55Δ= +2.7
<$5,000 RBs with > 2 catch avg. 17.96Δ= +3.95
WRs between $7,000-$7,900 23.48Δ= +10.37
TE in game with total > 45 7.72Δ= -11.76
WR in Flex < $5,000 19.63Δ= +4.4

 

TRUE EFFECT = APPA – (3*(AVG. SALARY OF THOSE AFFECTED)/1,000 + 6)

Trend True Effect
<$7,000 QB + 2 Pass Catcher Pair -5.33
<$4,500 RBs with > 2 catch avg. -2.05
WRs between $7,000-$7,900 -4.59
TE in game with total > 45 -15.02
WR in Flex < $5,000 1.12

 

Last week I didn’t do much analysis on the individual trends as I didn’t have anything to compare their point averages to. This week though, I’ve included their change (noted by the delta in the table) as well as their average total affect.

You’ll first notice that four of the five trends received a positive boost in their APPA, the two biggest being WRs between $7,000-$7,900 (Mike Leone’s) and WR in flex at $5,000 or less (ME!!). First I should note, that thanks to the dud put up by Leone’s trend last week, he had the largest opportunity for growth. Yet, that shouldn’t be a knock on the performance of those from his trend.

I haven’t asked Mike for a reason he chose this range, but if I had to hypothesize I’d guess that this particular range provides the easiest exploitation of the salary cap. Littered in the range of $7,000 receivers are plenty of WR1’s that carry a similar upside (in terms of market share and targets) to those that others are paying $9,000+ for.


Aside from the APPA though, what I’m most interested in is the True Effect, the neutralization of salary that analyzes a trend’s results relative to our perceived “awesomeness” threshold. That threshold is something that we’ve created, it’s what we want a particular player or trend to achieve.

In the formula above that is: 3x (x =a player’s salary, for this experiment the average salary per player in a trend) + 6.

Given that this threshold is difficult to achieve, make note that any positive scoring “True Effect” is exceptional, but even small negative results meant that a trend was particularly successful on a given week.

With that in mind, four of the five criteria performed quite admirably, especially the WR in flex under $5,000 (that was me!). A few things have contributed to success of the value-centric trends. First and foremost, there is abundance in value at both the WR and RB positions essentially putting no restrictions on stacks or lineup creation, while providing high floors and ceilings for players in those positions despite low salaries. Secondly, and not to go overlooked, the DailyRoto Premium Weekly Projections have been Supa-Hot-Fiya at nailing value picks at every position. If you haven’t already signed up for the Premium NFL product, I urge you to at least give it a week-long trial.

Team Success

I mentioned last week that the true success of the experiment would be the meshing of the trends as a whole team and the complete ROI. Here is how things have shaped up after two weeks.

Week High Score Low Score Avg. Score Cash Line Weekly ROI Total ROI
Week 1 153.86 111.92 131.59 145.04 -16.67% -16.67%
Week 2 184.14 137.3 157.13 143.72 95% 34.09%

*Week one of this experiment is actually week two of the NFL season.


This was a highly successful week as eight of the ten lineup iterations found their way into the cash, resulting in a weekly ROI of 95%. Furthermore, while this was a high scoring week in general, the cash line actually dropped (tournament parameters did change) but the average lineup score increased enough that it nearly surpassed the cash threshold. If these particular trends can keep it up, I’ll be pleased with a 34.09% ROI at the end of the season.


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