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Chasing The FanDuel Sunday Million
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Chasing The FanDuel Sunday Million

*Skip to Week 6 Results if you read the introduction last week.

One of my goals for this NFL season was to find more time to do data analysis and turn that into creative content. I feel like I’ve been able to do that in a few ways this season:

Floor/Ceiling Projections

Pick ‘Em Tier Probabilities

Fun With Air Yards and Receiver Efficiency

Still, I wanted to do a regular piece that was a bit more fun and hopefully is able to portray how I go about using a lot of the information on the site.

I figured, hey why don’t I try to win a $1million in the process? After all, I’m surrounded by million dollar winners and starting to feel a bit left out.

SportsGrid CEO Jeremy Stein, who is making lineups for the DR Sweat promotion, has done it TWICE.

I was live sweating with Drew when he pulled it off:

Each week I’ll go through the process of how I made my lineups, and the macro (roster construction) and micro (specific player pool decisions) strategies employed. I’ll then evaluate the results and how I likely got screwed by variance with no other explanation for my failings.

I’ll document the results in here, although the goal isn’t to grind out a weekly profit in this format. The goal is to give myself the best chance to win life-changing money, something I feel got lost in my macro strategy this week (more on that later).

In general, I’ll keep my entry fees in the $1,000 – $2,000 range. So, Week 1 that allowed me to max enter 150 times. In Week 2, the entry fee was $15, and I entered 100 lineups.

Here was the Week 1 recap: https://dailyroto.com/chasing-the-fanduel-sunday-million/

Now, onto Week 6.

Week 6 Results

Entry Fees: $9 x 150 entries = $1,350

Entries Cashed: 19 (13%)

Gross Winnings: $293

Best Entry: 12,359 / 353,907 ($18)

Net Profit: $-1,057 (-78.3%)

What Went Wrong

This was the first week all season where I felt I was completely drawing dead, which shouldn’t be a surprise given the ugly results above. Following two straight weeks of having really strong cores and stacking the right environments, my teams were so bad I couldn’t even play the “what if” game, lamenting hypothetical 1v1s that would have vaulted me to first place.

As a result, it was difficult to have any strong macro takeaways since I was simply so wrong on my individual player stances. The projections weren’t even that far off, but I got aggressive in all the wrong spots. As I’ve noted in several of these reviews, I’ve been trying to get more aggressive in my builds, so I don’t regret doing so despite the fact a spread approach would have been more effective this specific week. The RB position is a good place to showcase my issues this week:

Running Back Projections

Simply looking at our RB values, there was a mix of good plays and busts. Some of the best plays of the week were Todd Gurley (our top value), Melvin Gordon (third), James Conner (seventh), and Latavius Murray (11th). However, the rest of the names were either pedestrian or failed miserably.

Heavy Mixon

I chose this week to get aggressive on two players: Joe Mixon (CIN) (45% exposure) and Tevin Coleman (ATL) (41%).

Regarding Mixon, I was excited about the volume potential in a spot where Cincinnati had a high 26 team total at home. Mixon received 100% of RB carries coming back from injury the week prior, and Giovani Bernard was out again. As it turned out, Mixon had an 85% MS of carried and was efficient, scoring once on those 11 carries and averaging 5.82 YPC. However, he ran the ball just 11 times as the Bengals had 42 pass attempts compared to just 13 rush attempts. Mixon, who has been awesome as a receiving back (7.28 career YPT, 81% catch rate), did receive 7 targets but converted that into just 4 catches for 20 targets.

Ironically, with both Mixon and Coleman, I got bailed out by late TDs or it could have been even worse. In Mixon’s case, I think this was right process from a projection standpoint. If there was a part of the process that was wrong, it was that I expected Mixon to come in under the radar. It was a big reason I went heavy Mixon and consequently light James Conner (PIT). However, Mixon came in at 24% owned and Conner at 17% owned. The results could have very easily been flipped for these two, but I wanted to get Mixon at the same or even lower ownership than Conner.

Wrong On Timeshares

As I mentioned above, I played heavy Tevin Coleman this week. I thought he fit the slate really well, giving you access to the highest team total at home with a decent chance at a favorable game script. This was all coming at a cost that allowed bigger spends elsewhere and provided natural leverage on our highest expected ownership player on the slate (Julio Jones). The issue is, I flat out got the playing time split wrong between Coleman and Ito Smith (ATL). Our projection of a 61% MS of carries and 50% MS of rushing TDs was, in my opinion, a little on the conservative side. I increased the volume in my MME runs.

The actual volume breakdown? Coleman played just 57% of his team’s snaps while Smith played 46%. I was nearly an even split in every way with Coleman logging 10 carries, 2 targets, and 2 red zone opportunities compared to 11, 2, and 4 for Ito Smith. Purely based on volume, Smith was actually the better play overall, not just per dollar.

The crazy thing is, as bad as my week was, it could have been that much worse if not for late TDs from both Mixon and Coleman.

While I had a ton of Mixon and Coleman, I also made sure to get overweight on Jordan Howard (CHI). I didn’t have the same confidence in Howard as I did Coleman, but still thought it was an opportunity to buy low on Howard in a likely positive game script and on a site where the scoring system most benefits him.

Instead of seeing a natural pullback in Howard’s snap rate, it further declined: 73% to 62% to 54% to 51% last week. This was a lose-lose situation, as less playing time for Howard meant more for Tarik Cohen (CHI), who is a more explosive player on a per touch basis. Cohen ended up on the Milly Maker winning lineup.

Volume can be a fickle beast. What can look like an obvious trend in retrospect, may be something that is in truth not predictable in the moment, especially when volume can twist and turn at the whims of game script. There probably will be opportunities, as a result, to buy low on Howard in small amounts moving forward. Clearly, though, I was too optimistic on two backs that had showed some clear chinks in the volume armor previously.

Not Enough of the Studs

Being aggressive, and wrong on Mixon and Coleman, was a double edged sword. It also forced me to come in underweight on Todd Gurley (LAR) and Melvin Gordon (LAC), who our projections liked quite a bit this week. I don’t have much to add here. I didn’t do anything in my MME runs to directly dock Gurley and Gordon, but when you’re being aggressive in spots, there’s opportunity cost beyond simply how the players you’re using perform.

Rams WRs

The crux of my problems this week laid at the RB position, so I don’t want overanalyze everything else. However, one other spot I made sure to be aggressive on was the Rams WRs – going overweight on all three of Brandin Cooks (LAR)Robert Woods (LAR), and Cooper Kupp (LAR). I love the Rams WRs due to the combination of efficiency and condensed volume in that offense and had hoped ownership would be down since people seemed overly concerned over the pre-game snow. Part of the ideas, as well, is that if we have three players on the same team all rating well, there’s a good chance at least one of them ends up a premiere value on the slate….should the team as a whole hit their yard and TD expectations. For example, we had Cooks, Woods, and Kupp all rate as good values with just over 0.6 projected receiving TDs. Now, obviously, a partial TD can’t happen. If that group scores the 2 receiving TDs we expected, someone is getting 0, and someone is getting at least 1.

While I was right about the weather being a non-issue, the Rams passing game did not hit the expected targets, completing just 14 passes for 201 yards and failing to find the end zone. Due to effectiveness and game script, 58% of the Rams attempts were on the ground.

Browns WRs

Antonio Callaway and Jarvis Landry had 19 targets. Antonio Callaway and Jarvis Landry had 4 catches for 20 yards. Antonio Callaway and Jarvis Landry made me cry.

What Went Right

Not much, honestly, or at least nothing that remotely helped me to overcome the issues above. Time to have a short memory and do better next week!

 

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