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NFL DFS Experiment: Puzzle-Piecing Paradigms Week 3
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Did you read the results of our NFL DFS Experiment from weeks one and week two? If not, I’d like to slap you, but also you might not understand much of what is happening.

Lucky for you, I’m sympathetic to those that are forced to hit hyperlinks. I grew up in an area with horrible internet access and perhaps your internet service provider, not you, is actually responsible for your lack of access.

So, if this is the first stop for you, you’re in luck. Here is the gist of what I’m doing.

I’m trying to escape my personal paradigm and personal biases by asking some of the smartest people in the world of DFS to specify trends for experimentation in large field GPPs. After each person (detailed below) provided me their criterion, I unfairly mashed them all together, judging them separately but also how they affected the team.

There were some changes this week, here’s how it went.

Week Three (week four of NFL season)

Changes in criteria have been bolded:

  • Colin Drew – Pairing of two pass catchers with a quarterback. (The big change to Colin’s criteria was the removal of the cap on the quarterback price)
  • Mike Leone – wide receivers ranging from $6,500-$7,600 with expectations of low-mid ownership (this is a bit ambiguous, but Mike provided me a list of acceptable receivers which I worked with)
  • Drew Dinkmeyer – running backs priced at $5,000 or below with an average of two receptions per game.
  • DFSalbatross – tight end less than $4,000 from high implied total game (I deemed “high implied total game” as one of the top 25% of totals)
  • ME! Logan Hitchcock – WR/RB/TE in Flex that has received at least 25% of carries or targets in team’s redzone opportunites (I wanted to switch up my criteria to take advantage of the new red zone data provided in DailyRoto’s premium product – check it out here)

Lineup Results

Screen Shot 2016-10-03 at 4.30.31 PM

Trend Analysis

Reminder on Tracking:

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
QB + 2 Pass Catcher Pair*** 16.94Δ= -1.61
<$5,000 RBs with > 2 catch avg. 15.35Δ= -2.61
WRs between $6,500-$7,600*** 9.42Δ= -14.06
TE < $4,000*** 10.92Δ= +3.2
RB/WR with 25%> RZ Opps*** 13.36Δ= -6.27

 

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

Trend True Effect
QB + 2 Pass Catcher Pair*** -7.33Δ= -2.0
<$5,000 RBs with > 2 catch avg. -2.77Δ= -.75
WRs between $6,500-$7,600*** -19.06Δ= -14.47
TE < $4,000*** -4.53Δ= +10.49
RB/WR with 25% > RZOpp*** -10.52Δ=-11.64

*** = indicates a change or tweak to criterion from the previous week. Deltas are still measured using the most recent criteria versus the previous week.

WELP! If the APPA or True Effect changes didn’t immediately signal to you that this was a bad week, I’m telling you now – it was not a good week. In fact only one trend (@DFSalbatross’) got a bit better this week and it was on the back of a trend tweak. Every other trend got a little bit worse, but the most notable downgrades were to Mike Leone and I’s trends.

There is good news and bad news though. The good news? This was a trend switch for me and only the first week of data collection for this particular criterion. Using DailyRoto’s new red zone data I’m looking to take advantage of players that are receiving attention while inside the red zone. Unfortunately this week didn’t pan out as I used a ton of salary and had a poor showing in terms of fantasy output (noted by True Effect).

The bad news? Mike struck out for the second time in three weeks. I’m still interested in this particular grouping of wide receivers though and the process feels sound. There continue to be plenty of WR1’s priced in this range and their pricing discrepancy from the top appears to be exploitable. Mike has some good fortune though because I like him and won’t kick him out of the experiment.

One thing that has potentially gone overlooked due to the consistent performance is that of Drew Dinkmeyer’s criteria/trend. There has been so much value at running back in the first four weeks of the season that each week we’ve gotten plenty of opportunity and volume from cheap, starting running backs that catch passes. You might notice that Dink’s trend still holds a negative True Effect, but reminder, the true effect measures the threshold that we set on players and their salaries with expectations that performing at that threshold would win us a GPP (if all players achieved it). With this being said, even small negative numbers in True Effect can be successful GPP trends. That’s what we’re getting from Dink’s trend, a successful and consistent trend thus far.

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 3 150.7 93.48 120.08 142.94 -80% -1.56%

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

BLEH! This was the worst week yet. We set a new low for high score, a new low for average score and nearly a complete loss at -80% ROI. Thanks to a nice week last week we’re holding out at just -1.56% ROI this season, so I’m not entirely dissatisfied thus far, but I’m hoping we can avoid another dreadful week like this.

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