In DFS, a common strategy to maximize the chances of placing highly in a large tournament is to enter several different lineups. In order to differentiate the lineups you enter and decrease risk, while still basing your lineups on sound data and analytics, you can utilize the concept of maximum player exposure.

The phrase “maximum player exposure” may sound complex, but it’s actually fairly simple. Once you select a maximum player exposure percentage on SaberSim, no player will appear in more than that percentage of the total lineups you generate. For example, if you were to create ten lineups and set the maximum player exposure at 50%, then Clayton Kershaw would appear in no more than five of those ten lineups. This allows for minimization of risk, as you’ll have a greater variety of players and a one bad performance won’t end your day right then and there.

Though it’s tempting to simplify the process of lineup creation by entering a single lineup into a higher-cost tournament, multi-lineup generation offers the ability to increase your odds of hitting just the right combination of players. At the same time, adjusting player exposure allows you to broaden your player pool, thus increasing the odds that you will cash a few lineups, and decreasing the risk of a big loss.

DFS Hitter Projections & Fun with Conditionals

Baltimore Orioles
There are three Orioles in the top ten projected offensive players for DraftKings tonight. Baltimore faces Mike Pelfrey in Camden Yards, and the team is projected to score 5.34 runs per game on average. The top three projected Orioles are Chris Davis, Manny Machado, and Adam Jones, with Davis as the top projected player across all games today. Mark Trumbo and Joey Rickard round out the top five.

Conditional Target: Mike Pelfrey
There are several different routes you could go when applying Conditionals to achieve an optimal Orioles stack. In the following example, I added a Conditional of Mike Pelfrey allowing at least five runs, and applied it to the optimized lineup. Notice that in addition to stacking an optimal combination of Orioles batters, the optimizer also included Ubaldo Jimenez in a pitcher slot, which is partly influenced by the Pelfrey >= 5 RA Conditional; when Pelfrey allows that many runs, Jimenez’s odds of earning a win drastically increase.

Milwaukee Brewers
The Brewers are projected to score 4.45 runs per game in their home matchup against James Shields and the Padres. Miller Park is a hitter-friendly park, and especially increases home run production. Ryan Braun, Chris Carter, and Jonathan Villar all rank in the top ten, and Alex Presley ranks in the top twenty as far as projected DraftKings points for tonight’s games.
A Conditional of Jonathan Villar scoring at least two runs results in an optimal stack of Brewers hitters based on that conditional, and also ensures that James Shields is very unlikely to be included.

Notice that while Ubaldo Jimenez is also included in this optimal lineup, his projected point total is slightly less than it was in the optimal lineup when Pelfrey allowed at least five runs. That’s because the Conditional of Villar scoring at least two runs in his game against the Padres has no effect on the totally separate game of Tigers at Orioles.

DFS Pitcher Projections
The top three pitchers for tonight based on projected DraftKings points are Clayton Kershaw, Vincent Velasquez, and David Price. These projections are mostly in line with the pricing, but just outside the top three is Jimmy Nelson, who faces the San Diego Padres at home. There are a couple more potential value plays outside of the top five, with Ubaldo Jimenez coming in at number six and Aaron Blair at seven.

Streaming Pitcher Options for Friday

Ross Stripling (11% Y!)
Stripling faces Michael Wacha and the Cardinals at home in his Friday matchup. The Dodgers are projected as favorites in that game (55%), and Stripling is projected to have a solid outing (0.38 W/G, 4.71 K/G, 3.68 ERA).

Nicholas Tropeano (8% Y!)
Tropeano faces Nate Karns and the Mariners in Seattle. The Angels are projected as underdogs, but Tropeano is still projected well enough to warrant a stream (0.36 W/G, 4.63 K/G, 3.79 ERA).

Conclusion
There are countless ways in which you can create DFS strategies using SaberSim projections and lineup creation tools. In addition to Conditionals, there are also features that allow you to exclude players, adjust exposure, and much more. To keep you fully informed on all the possibilities, we will continue to explore more strategies in the coming days and weeks. Also, 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.

In order to differentiate DFS lineups in large tournaments, you can utilize a maneuver commonly referred to as “fading.” A fade consists of avoiding a pitcher who may be highly owned — for instance, Masahiro Tanaka against the Orioles today — in order to capitalize on the off-chance that he has a subpar outing. By using SaberSim’s Conditionals to fade a pitcher, you will also determine which combination of opposing hitters would be optimal in the event of that pitcher having a below-average outing.

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A Better Way to Create Optimal Combinations of Players

Many DFS players utilize a fairly unscientific approach to creating stacks (combinations of batters from one particular team) when building lineups. Rather than making educated guesses at optimal combinations though, it’s more effective to approach the strategy from an objective standpoint that accounts for the interdependence between players within the same game. Batters in different spots in the lineup will be affected differently by performances from other batters within the lineup depending on how many slots they are away from one another. Furthermore, one batter’s specific skillsets and projected rates of outcomes like home runs, steals, and strikeouts will affect others with different specific skillsets and projected rates.

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The season is only a little over two weeks old, and all the rote small sample size caveats apply, but some hitter performances in the early going have been enough to move the needle when looking at rest of season projections. I’m specifically going to be looking only at hitters in this article, mostly because rest of season projections aren’t quite as quick to adapt to changes in talent for pitchers. I’m also going to be presenting this information through the lens of ottoneu Fangraphs points leagues, which utilize linear weights scoring.

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The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X 2.0, and the process continues to evolve and improve.

Let’s rewind to spring training of 2015. It was one in which the hype became deafening for Taijuan Walker. He pitched 27 innings, allowing just two runs, for a microscopic 0.67 ERA. His underlying peripherals (ya know, the spring stats that might actually matter) were strong too, but it was most certainly that tiny ERA that took Walker from sleeper and breakout candidate into that risky territory in which he has to break out just to break even for his fantasy owners. So how were his new owners rewarded? With a luscious 4.56 ERA. Oops. His skills were excellent though and he managed to post a more respectable 3.69 SIERA. So is this the breakout year?

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Yesterday, I compared hitters’ FANS projections to their Depth Charts projections in order to identify the biggest discrepancies between the two. For more information on why I’m doing this or what FANS and Depth Charts projection entail, I cordially invite you to click here.

I’m here to repeat the exercise but with pitchers instead. Focusing on playing time (as measured by innings pitched), K/9, BB/9 and saves. I’ll mix in starters and relievers at my discretion or where obviously necessary, like for saves. Like I did yesterday, I’ll try to limit each blurb to three sentences.

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It’s time for another (and perhaps final) comparison between my Pod Projections and Steamer. Today we’ll look at another starting pitcher who I am significantly more bullish on by ERA than Steamer.

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Last year, I performed this very exercise, in which I compared FANS projections — projections generated by fans — to the Depth Charts projections — a composite of Steamer and ZiPS with playing time allocated by generally informed FanGraphs staff. I intended to highlight the largest discrepancies and offer a quick take on them.

I explain my interest in FANS during the inaugural of this exercise. Said interest pertains largely to anticipated versus most likely outcomes for a player and how those disparities manifest themselves in price distortions on draft day.

This time around, instead of discussing five National League outfielders at length, I’ll focus on the largest differences between FANS and Depth Charts projections in playing time, home runs, stolen bases, wOBA and WAR for a couple of players per category. I’ve set a personal goal for no more than three sentences per player so I don’t spend all day doing this. Because I could, and nobody wants that.

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The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X 2.0, and the process continues to evolve and improve.

After a breakout 2014 performance, Jeff Samardzija followed up with a stinker of a season. His ERA spiked by nearly two full runs, his SIERA jumped by more than a run, and his strikeout rate plummeted, as did his ground ball rate. It’s no surprise then that the RotoGraphs ranking crew don’t exactly agree on his 2016 value. His individual ranking ranged from a bullish 25 to a bearish 69, but a “split” (difference between high and low ranking) of 44, the highest mark among the top 45 consensus pitchers.

Despite his poor 2015 results, the Giants gave him a five-year contract. Let’s find out if a return to the National League and calling the most pitcher friendly park in baseball home can spark a rebound.

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It’s time to move on to the starting pitching side of the ledger for our next set of Steamer and I entries. For the pitchers, I’ll be comparing my ERA Pod Projection to that of Steamer to identify who I am significantly more bullish and bearish on.

First, we’ll start with one of the pitchers I am far more optimistic on than Steamer is. But before we dive in, I wanted to note some of the pitchers I skipped over. Chris Young, Royals version, topped the list, for obvious reasons. He breaks all computer models and is a perfect example of why it is often better to rely on human forecasts than computer ones. After Young was the man that came back from the dead last year, Rich Hill. Obviously, a computer system is going to struggle with his projection and is also unaware of the work he did on his mechanics last season that may have been behind his improved control. He’s a total crapshoot though and a complete shot in the dark, so he’s not really worth discussing.

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