Building DFS Lineups for Small Slates

So far, this weekly column has largely focused on various general aspects of DFS strategy for the first half of the post, and specific projections for the day in the second half. Today, I’d like to switch gears a bit and discuss my process for building lineups in small (2-5 game) slates, using today’s early 3-game slate as an example.

Small slates in DFS require a unique strategy due to their limited player pool. In full 15-game slate, there are over 250 batters and 30 pitchers to choose from, leading to a massive (billions upon billions) number of possible lineups. In these slates, you’re unlikely to share a lineup with another user even when picking the most popular players.

On the other hand, in today’s early slate, there are only about 50 batters and 6 pitchers to choose from. While this still leads to many many potential player combinations, the limited pool makes differentiation that much more important. If you use conventional strategies and pick popular players, you’ll likely end up with very similar lineups to others. There’s nothing wrong with this if you’re just looking to maximize your average points scored — however, for large DFS tournaments, you only need to score more than other lineups in the field, rather than the most points possible. This makes game theory and other differentiation strategies more important.

Of course, differentiation is easier said than done. Each pool of players is unique, and strategies on how to handle them vary depending on the variance between player projections and public perception/ownership. Let’s take a look at today’s early slate pitchers for DraftKings:

Name	Team	Opponent	SaberSim Projection	Price
Michael Pineda	NYY	TEX	20.06	7800
Carlos Rodon	CHW	MIN	18.09	8500
Kenta Maeda	LOS	MIL	17.14	9500
Tommy Milone	MIN	CHW	14.38	6100
A.J. Griffin	TEX	NYY	12.7	8400
Zach Davies	MIL	LOS	11.69	8000

As you can see, there aren’t any obvious aces on the slate, which makes ownership more difficult to predict. That said, because projections are fairly close together, we may be able to find a big edge in choosing a lower owned pitcher. Because of baseball’s high variance, someone like Tommy Milone still has a fairly high probability of a good game (according to SaberSim, 37% chance of 20+ DK points). Though he’s clearly a worse option than Pineda (55% chance of 20+ points) objectively, it’s very likely that he’ll be *far* lower owned than Pineda, and his price allows you to roster more expensive batters.

That said, price is often highly correlated with player ownership, which, along with the Rangers having the best record in baseball and being in Yankees Stadium, leads me to believe that Maeda and Rodon will be higher owned than Pineda. This makes Pineda is great play, being the top projected pitcher on the slate and (hopefully) lower owned than other options. Because of the aforementioned ownership of the next two pitchers, I’ll likely be going with Pineda and Milone today, and hope that we hit the higher portions of Milone’s distribution.

On the hitting side of things, one important lesson I’ve learned for early slate lineup construction is to avoid full (5-player) stacks. This is a common strategy and will be utilized in many lineups, but because of the small player pool, any stack of 5 players, unless it includes the 7-8-9 batters, will likely be owned by other lineups as well, limiting your possibility of winning the tournament.

For this slate, it’s especially important to take advantage of the fact that we’re fading Maeda and Rodon, two likely high owned pitchers. SaberSim Conditionals are a great tool for this purpose. In order to place highly using Milone and Pineda, we need them to perform well, and we need Maeda and Rodon to perform relatively poorly, or at least *not* hit the higher tails of their distribution. Therefore, we’ll use Conditionals to filter the simulated games to only include those in which Pineda and Milone hit the top half of their distribution (score more than their median) and ignore the top quartile of projected performance for Maeda and Rodon. Here’s what that looks like on SaberSim:

After isolating our distributions in this way, our pitcher projections look like this:

As expected, the projections for Pineda and Milone increase, and Rodon and Maeda decrease. Davies and Griffin also chance slightly, because higher projections for their opposing starters mean lower probability of a win.

If we optimize with these Conditionals in place, we get the following lineup:

As you can see, we now have some Twins and Brewers in our lineup due to the worse performance for Maeda and Rodon, in addition to some Yankees and Dodgers, who were projected highly regardless.

The above lineup is not the best possible objective lineup, nor is it likely to win a tournament or even be profitable. However, given what I feel to be the ownership of the pitchers involved in the slate, I’m hoping that random variation swings in my favor with regards to these pitchers — if it does, and the Conditionals hit, the optimizer has given me a lineup with a high chance to succeed. It still very well may not, but DFS — and these small slates in particular — are all about using game theory and probability to give yourself every edge possible, and increasing the small chance of beating the field.

DFS Projections: Batters

Top Overall Offenses

1. Dodgers (vs MIL – Zachary Davies) – 5.66 r/g
2. Nationals (vs CIN – Brandon Finnegan) – 5.43 r/g
3. Yankees (vs TEX – A.J. Griffin) – 4.91 r/g
4. Marlins (@ ATL – Mike Foltynewicz) – 4.78 r/g
5. Indians (@ TOR – R.A. Dickey) – 4.71 r/g

Top Batters

1. Jose Abreu 9.91 DK; 13.15 FD
2. Bryce Harper 9.90 DK; 13.39 FD
3. Corey Seager 9.71 DK; 12.68 FD
4. Adrian Gonzalez 9.67 DK; 12.88 FD
5. Brandon Moss 9.54 DK; 12.86 FD
6. Giancarlo Stanton 9.52 DK; 12.99 FD
7. Todd Frazier 9.51 DK; 12.70 FD
8. Joc Pederson 9.33 DK; 12.58 FD
9. Carlos Santana 9.24 DK; 12.41 FD
10. Jacoby Ellsbury 9.22 DK; 11.90 FD

DFS Projections: Pitchers

Top Pitchers (DraftKings)

DraftKings

1. Michael Pineda
2. Gio Gonzalez
3. Carlos Carrasco
4. Taijuan Walker
5. Wei-Yin Chen
6. Carlos Rodon
7. Steven Matz
8. Kenta Maeda
9. Jake Odorizzi
10. John Lackey

FanDuel

1. Michael Pineda
2. Gio Gonzalez
3. Carlos Carrasco
4. Carlos Rodon
5. Taijuan Walker
6. Wei-Yin Chen
7. Steven Matz
8. Kenta Maeda
9. John Lackey
10. Jake Odorizzi

Conclusion

There are many ways to create DFS strategies using percentile projections, boosting/fading, conditionals, and more. We will continue to explore more strategies in the coming weeks in order to keep you up to date on the most current DFS cash game and GPP strategies.

Remember to check back for updated projections throughout the day. As teams release official lineups, SaberSim automatically updates accordingly and reruns simulations in order to stay as current as possible. Good luck!