Small Slate Game Theory: Playoff Edition

GPP Tips, Strategies and Ownership Projections for the Divisonal Playoff Round

In this special three-week Fan Duel NFL Playoffs feature, we will take a close look at projected ownership and top plays of the week. We will also spend some time on game theory and discuss ideas on how to build relatively unique lineups that can still score enough points to win GPPs.

We’ll start off with some general strategy ideas and then go through each position and apply the strategies to this week’s slate to identify the top plays given our ownership projections at each position.

SHORT-SLATE GPP STRATEGY

Last week, we focused upon three common strategies for small-slate GPP Success: (1) Get uniqueness on the cheap, (2) Play the studs and (3) Identify the best high-upside bargains. We want to continue to pursue these strategies. As we saw last week, it was difficult to win without the stud players that we identified in advance like Le'Veon Bell and Antonio Brown. Again this week, we want to make players like Bell, Brown, Ezekiel Elliott, Aaron Rodgers, and Julio Jones staples of our lineups. We also want to try to find that "lotto ticket" low-owned, high-upside player like Randall Cobb was last week and we want to differentiate our lineups with low-owned players that have similar projections to higher-owned options.

Having discussed those strategies in-depth last week however, our focus this week will be upon some other strategies that can help us find success in these small slate GPPs. Specifically, the focus this week will be on finding hidden value using correlation plays. We'll look at (1) sneaky high-correlation plays and (2) leveraging negative correlation plays to fade highly-owned players.

Note: For those unfamiliar with the stasticical correlation calculation, we are talking about the correlation coefficient (r). It's not necessary to understand the specific calculation process. It's sufficient to know that the scale goes from -1 to 1. If two players are positively correlated, they will have a positive number between 0 and 1. The higher the number, the higher the correlation (and the better the pairing is for GPP purposes). If two players are negatively correlated, they will have a number between -1 and 0 (and pairing the two is generally a bad GPP play). 

1. Sneaky High-Correlation Plays

Everyone understands the benefit of stacking a quarterback with one or more of his top targets. Most receivers will have a strong positive correlation with their quarterback and vice versa. However, QB-WR (or TE) stacks aren't the only way to reap the rewards of correlated plays in tournaments. Some correlated plays however fly more under the radar and might even seem counter-intuitive on first glance. For example, the pairing of Le'Veon Bell and Antonio Brown was one of the bigger keys to GPP success in the Wild Card round. On first glance, it might seem that a WR1 and RB1 would have a negative correlation where a big game for one would mean a lower likelihood of a big game from the other. But Bell and Brown are a positive correlation play (.12). There is a similar dynamic between Ezekiel Elliott and Dez Bryant (.28) this weekend. So if you are rostering Elliott, you shouldn't be afraid to include Bryant in the same tournament lineup.

We will point out some other specific sneaky high-correlation plays (QB-RB, DEF-RB, DEF-K, WR-WR, etc.) in the positional breakdowns below.

2. Leveraging Negative Correlation Plays to Fade Highly-Owned Players

We can also leverage negative correlation plays in lineups where we are not rostering a highly-owned player. For example, Aaron Rodgers is likely to be the highest-owned quarterback and a player who can tilt the slate with a big game. On GPP rosters where you have a quarterback other than Rodgers, it potentially makes sense to take advantage of negative correlation plays to leverage against Rodgers owners. The two primary ones are Ty Montgomery (-.40) and Mason Crosby (-.14). Both of these plays make sense logically. If Rodgers doesn't have a big fantasy game, there's a decent chance that it's because Montgomery cashed in one or more rushing touchdowns or because the red zone offense struggled and the Packers had to settle for multiple Crosby field goals. We will discuss some other negative correlation plays below in the positional breakdowns. 

POSITIONAL BREAKDOWN

The Ownership Projections below are based upon an in-depth analysis of a number of factors, including: the general "buzz" around each player, projected scoring around the industry, and the pricing structure and typical roster construction decisions most owners are facing when trying to fit players in under the cap. 

Quarterback

Quarterback Own %
Aaron Rodgers 27
Matt Ryan 20
Dak Prescott 14
Russell Wilson 12
Tom Brady 12
Alex Smith 7
Ben Roethlisberger 6
Brock Osweiler 2


Alex Smith and Spencer Ware are the QB/RB duo with the highest correlation (.51) on the slate. On closer inspection, this correlation makes sense. Half of Ware’s four touchdowns this season have come through the air. Plus, Kansas City’s offensive line hasn’t been good enough to simply lean on teams and run the ball when they know it is coming. However, if the passing offense gets rolling, it has opened things up a bit for Ware and the running game. The Smith/Ware duo will be a very low-owned stack, which makes it attractive. Especially given the strong positive correlation and the low price point that allows you to build a unique lineup that still fits in some of the elite players on the slate. If you go with the contrarian Alex Smith play, consider stacking him with Ware instead of more poplular options like Travis Kelce or Tyreek Hill and reap the double reward of a unique lineup and a nice positive correlation play. 

Without looking, I’d have guessed that Matt Ryan would have been a solid correlation play with his top running backs. Devonta Freeman leads the Falcons with 17 red zone targets. Not far behind in second place is Tevin Coleman with 12 (Mohamed Sanu is tied for second with 12 also). However, Ryan has negative correlations with each of his top two backs (both are -.14). Despite the heavy red zone pass usage, 11 of Freeman’s 13 touchdowns have come on the ground. A big Freeman game makes it less likely that Ryan has a big fantasy day and vice versa. With Ryan likely to be the second-most popular play at quarterback, Freeman makes more sense as a negative correlation play in non-Ryan lineups than as a stack with Ryan. 

Running Back

Running Back Own %
Ezekiel Elliott 60
Le'Veon Bell 40
Devonta Freeman 20
LeGarrette Blount 16
Thomas Rawls 12
Ty Montgomery 10
Spencer Ware 10
Dion Lewis 10
Tevin Coleman 9
James White 7
Lamar Miller 6

Ezekiel Elliott looks like he will be the most popular play of the weekend, with the $1,400 price difference between he and Le’Veon Bell tilting ownership in his direction. Many will be hesitant to stack Elliott with Dak Prescott and/or Dez Bryant. But Elliott’s big fantasy games coincide nicely with Prescott’s strong fantasy performances. In fact, Prescott is the strongest correlation play (.47) with Elliott followed by Bryant (.28). Don’t be afraid to fire up the Cowboys super stack with all three of these guys this weekend. If the passing game gets rolling, it opens things up for Elliott and vice versa.

On the other side of the Cowboys-Packers matchup, Ty Montgomery has a big negative correlation with Aaron Rodgers (-.40). This is a perfect example of the type of negative correlation play that makes sense when fading an extremely chalky player. Playing Montgomery should be very much on your radar in any non-Rodgers lineups. If Rodgers doesn’t have the type of big fantasy game that wins GPPs, it’s more likely that Montgomery has a nice day on the ground and perhaps cashes in a rushing touchdown or two.

Wide Receiver

Wide Receiver  Own %
Randall Cobb 45
Davante Adams 40
Antonio Brown 30
Dez Bryant 28
Julian Edelman 26
Julio Jones 24
Doug Baldwin 20
Paul Richardson Jr 13
Jeremy Maclin 10
DeAndre Hopkins 10
Tyreek Hill 7
Eli Rogers 6
Terrance Williams 6
Taylor Gabriel 6
Will Fuller V 4

Often receivers will have negative correlations against receivers on their own teams. There are only so many passing game targets to go around. However, Randall Cobb and Davante Adams are an exception. Cobb’s highest correlation overall is Adams (.39). When Adams has a good game, it’s more likely that Cobb has a good fantasy day as well. Don’t be afraid to fire up a multi-WR Aaron Rodgers stack with both Cobb and Adams this weekend.


Tight End

Tight End Own %
Jason Witten 12
Travis Kelce 28
Jimmy Graham 15
Jared Cook 22
C.J. Fiedorowicz 9
Martellus Bennett 8
Ryan Griffin 3
Austin Hooper 3

Interestingly, many of the top tight ends have strong positive correlations with the starting running back on their team. For example, Jimmy Graham is a positive correlation play with Thomas Rawls (.32) and Jason Witten is a positive correlation play with Ezekiel Elliott (.27). While it may seem counter-intuitive on first glance since RBs and TEs are competing for slices from the same touchdown pie, the postiive correlation makes some sense in the bigger picture. We know that tight ends generally excel in high-scoring games (same as running backs) and are usually stronger plays as home favorites (same as running backs). It also makes sense from a schematic perspective. A strong running game opens up play action passes to the tight end as linebackers and safeties get sucked in while focused on stopping the run, allowing the tight end to get open. Stacking Rawls/Graham or Elliott/Witten are strong under-the-radar positive correlation plays that will likely come at low combined ownership. 

Kicker 

Kicker Own %
Stephen Gostkowski 24
Dan Bailey 18
Matt Bryant 15
Steve Hauschka 14
Mason Crosby 12
Cairo Santos 7
Chris Boswell 7
Nick Novak 3

While there are some exceptions, in general we can use kickers as a negative correlation play when fading a top offensive skill position player. For example, Antonio Brown and Chris Boswell have a negative correlation (-.16). If a lineup doesn't include either Brown or Le'Veon Bell, it makes sense to roster Boswell. If the Steelers offense doesn't score many touchdowns, it's more likely that Boswell hits multiple field goals. 

Mason Crosby is another player who makes sense as a negative correlation leverage play against the popular Packers options (Aaron Rodgers, Davante Adamas and Randall Cobb). One need look no further than the last Packers-Cowboys matchup for a perfect example. The Packers passing offense struggled in the red zone and Rodgers threw just one touchdown. It led to a big day for Crosby who kicked three field goals and scored 12 FanDuel points. 

Defense

Defense  Own %
New England  40
Pittsburgh  10
Atlanta  18
Kansas City  15
Seattle  7
Dallas  4
Green Bay  4
Houston  2

New England Defense- Somewhat surprisingly, there is very little positive correlation between the Pats D and LeGarrette Blount (.04) this season. The much stronger correlation is with Dion Lewis (.72). Lewis is an intriguing GPP option, especially stacked with the Pats D. He’s seen double-digit carries in three straight games and should be in line for a strong game if the Patriots control the game defensively. Stephen Gostkowski also has a strong positive correlation (.32) with the Patriots defense, so the popular Defense/Kicker stack is a strong play here.


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