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Chasing The FanDuel Sunday Million
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Chasing The FanDuel Sunday Million

*Skip to Week 4 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 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 4.

Week 4 Results

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

Entries Cashed: 74 (49%)

Gross Winnings: $1,380

Best Entry: 191 / 383,511 ($150)

Net Profit: $30 (+2.2%)

Editor’s Note: In general these results are a very good lesson for the variance and top-heavy nature of large field GPPs. As noted above Mike was able to cash 49% of his entries and yet he only returned a 2.2% profit. Entered into smaller field contests with flatter payout structures those very same entries would have likely returned 30-40% or even higher in terms of profit.

General Thoughts

It was nice to finally have a week in the black, albeit a measly 2.2%. While the profit itself is virtually nothing, it’s important not to bleed money every week while taking shots, something that is difficult to do given the immensely top heavy payout structure a contest like the Sunday Million is. If you’re playing a contest like this, you’re playing it to hit a home run. If you’re trying to grind out a profit, look elsewhere.

The Good and Bad of Game Stacks

It was a wild Week 4 with a ton of scoring, both in real life and in fantasy. With a lot of chalk hitting, you had to be close to perfect to win a GPP of this magnitude. As a result, a lot of game stacks went “off” but also left you with a ho-hum play here or there.

DeShaun Watson – DeAndre Hopkins stacks went off, but you hurt yourself if you double stacked with Will Fuller. Coming back on the Colts side, TY Hilton and Eric Ebron had okay but not GPP winning performances.

Mitch Trubisky – Taylor Gabriel stacks went off, but you hurt yourself if you double stacked with Allen Robinson. Double stacking with Trey Burton was pretty good, but he wasn’t on most of the GPP winning lineups on Fanduel. If you forced someone coming back on the Tampa Bay side, as I did, your lineup was likely sunk despite the outlier Trubisky – Gabriel performances. The only acceptable option ended up being a decent DeSean Jackson game. DailyRoto user LSharp / BBOY3110 was able to capitalize on the Trubisky-Burton-Gabriel stack by avoiding the Tamba Bay side of things.

Matt Ryan – Julio Jones/Calvin Ridley/Mohamed Sanu stacks were the best. Jones and Sanu put up big games based on catches and yardage while Ridley found the end zone twice, despite just two scores. Coming back on the Bengals side, Giovani Bernard, AJ Green, and Tyler Eifert were all acceptable but not slate breaking options.

In general, when game stacks go off, you’re benefitting from correlation. You’re a lot less likely to run into a completely unproductive player, and you’re a lot more likely to run into a peripheral, slate breaking play at low ownership (think DeSean Jackson Week 1 and Calvin Ridley Week 3). However, it’s also tough to be perfect, and to a certain extent, you cap upside with each additional skill player since TDs are so important on FD (DK gives you more outs with the 100-yard bonus and full PPR scoring).

On most weeks, that’s fine. You’re not setting up for the chalk to hit completely on the week and for multiple games to clear the over by a substantial margin. This week was unique, though. Potential slate breaking peripheral options like Taylor Gabriel weren’t as impactful as previous weeks, simply because chalk options hit elsewhere (Sterling Shepard, Calvin Ridley, Tyler Boyd), but because there were owned players who had ceiling games at each position, mediocre performances as parts of stacks eliminated you (Allen Robinson, Tampa Bay skill players).

Keep in mind, I’m speaking generally. After all, the DK Milly winner won with a naked Will Fuller. But we did see the impact of this on FD. The winning lineup was a single stack with no opponent skill player coming back the other way:

Contrast that with my top placing lineup, a double stack with an opponent coming back:

I don’t really have a prescient point to make at the end of all this; it was just something I observed and found interesting. If there is a point to be had, I think it’s to whittle down your QB/Stack options to a small enough number that you can have diversity within each stack. If you ran 10 Trubisky stacks and boosted skill players in the game, it was tough to avoid some bad double stacks or a Tampa Bay player killing your lineups. If you ran 30 Trubisky stacks and used our shuffle feature, even after boosting skill players, you probably landed on a few single stacks with Gabriel. It’s a difficult balance between spreading yourself out enough that you increase your odds of having that one lineup that finishes in the Top 1% of the Top 1% and having enough common threads within your lineups that if you hit on a common thread, you’ll have the right combination somewhere.

While we’re seemingly seeing more shootouts in the NFL this season, which opens up the door for a week like this repeating, ultimately, I’d rather err on the side of more correlation/heavier game stacking than on the single stack side of things. It’s difficult to ever win a week where you have to be perfect, but on weeks where there’s a little bit of chaos, having heavy correlation within your lineups can help you bink without being perfect.

What Went Right

Tight, Correct RB Core

In previous versions of this column, I’ve talked about being aggressive. This week I got pretty aggressive in whittling down my RB options. With the exception of a few boosted players in game stacks, the only RBs I played on the week were the following (6 of our Top 7 rated values):

That allowed me to beat the field ownership on all of my primary backs and luckily most of them hit:

Giovani Bernard – 46% Exposure – 32.3% Field Ownership – 23.6 FD Points

Alvin Kamara – 42% Exposure – 39.9% Field Ownership – 38.6 FD Points

Ezekiel Elliott – 39% Exposure – 28.2% Field Ownership – 32 FD Points

Saquon Barkley – 39% Exposure – 30.7% Field Ownership – 19 FD Points

Melvin Gordon – 31% Exposure – 22.9% Field Ownership – 27.4 FD Points

Sony Michel – 29% Exposure – 7% Field Ownership – 17.2 FD Points

Tevin Coleman – 29% Exposure – 13.3% Field Ownership – 9.2 FD Points

Only Tevin Coleman failed to go 2x salary, and only Barkley additionally failed to go 3x salary.

Trusting Cheap WR Math

Despite limited production leading up to Week 4, I trusted our volume projections on WRs that popped below $6,000. Here were my highest exposure levels to cheap WRs that came in with single digit field ownership:

Taylor Gabriel – 18% Exposure – 3.1% Field Ownership – 26.9 FD Points

Corey Davis – 17% Exposure – 2.9% Field Ownership – 26.6 FD Points

Taywan Taylor – 13% Exposure – 0.5% Field Ownership – 11.2 FD Points

The Bears and Titans were two offenses that struggled mightily in the passing game entering the week. However, the requisite volume was there for success, and that became even more apparent with each team losing a WR prior to the game (Anthony Miller for the Bears, Rishard Matthews for the Titans).

Davis entered Week 4 with a 36% MS of air yards and 30% MS of targets. We had both Gabriel and Taylor projected for around an 18% MS of their team’s targets with around a 20% MS of their team’s TDs.

This is an important reminder to buy volume and do your best to ignore recent results, especially when you can do so at low ownership. Entering Week 4, the Bears and Titans ranked 28th and 29th in passing YPG.

What Went Wrong

As mentioned in the lengthy introduction, I did have some good teams killed by being too heavy on game stacks.

Aside from that, the area that hurt me most was…

Expensive WRs

The WRs I had more than 20% exposure to this week were:

-Odell Beckham Jr. – 10.5 FD Points

-Michael Thomas – 6.7 FD Points

-Jarvis Landry – 11.4 FD Points

-Allen Robinson – 9.3 FD Points

Game stack stuff aside, nailing the RB position and having enough strong WR cheap plays (same at TE) should have left me with more live lineups, but my heaviest exposure, more expensive WRs were a complete letdown. Individually, there’s not much fault to find in why I owned the WRs I did. What you could say, though, is that given the variance of the WR position, maybe I should have globally capped WR exposure closer to 20% than 30%.

Doing so would have flooded more exposure the way of DeAndre Hopkins, who I had in just 6% of my lineups, almost exclusively as a part of Watson stacks and not as a one-off anywhere. Hopkins ended up the overall WR1 on the week at just 6% field ownership.

Now, our public ownership projections thought Hopkins would be more popular (14.1%), but it’s disappointing, even with the benefit of hindsight, to realize I could have easily doubled up the field on our fifth-highest projected WR of the week had I simply scaled back exposure to chalkier options. My average exposure on Beckham and Thomas was 29.5%.

Next Week

Honestly, I felt really good about my teams both before and after the fact this week. My lineups felt like they had a chance to bink, placing two inside the top 1,000 and one in 191st overall. Out of 383,511 teams, my top team was in the Top 0.05% of all lineups. I’m excited to build for next week already.

Where my approach will stay the same:

-Find spots to be aggressive (this week it was the condensed RB options)

-Bump peripheral options in game stacks (the DeSean Jackson Week 1 rule)

Where my approach will adjust (at the fear of being too results oriented):

-Try to whittle QB plays down to 6-7 from 8-9

-Set a low global exposure cap on expensive WRs given the variance of the position

NFL Free

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