Power Patrol – 5.25.19 MLB DFS early slate

Hello everyone! I’m glad to contribute some content for the DFSD folks. Before we begin, you should know how my particular model works. I have included an in-depth explanation at the bottom of this article. Please read it so you know what the data represents and how you can effectively incorporate it into your research.

That being said, let’s get started! I haven’t settled on a particular format I want to use from here on out so you may get a hodge podge (people still say this, right?) of content formats. For this slate I will give you my top power bats at each position and then my 3 favorite stacks. So I will be primarily focusing in on ISO numbers and I DO take into consideration weather, bullpen, ballpark, and vegas, but do NOT take into consideration ownership or price. Below are projected lineups and may change.

Top Power Bats:

Top Stacks:

The Mets are again a top stack for me. Not the best park, but these power numbers are fantastic and they’re not too expensive. Add the fact that the Tigers bullpen is tired and #notgood, with wind blowing out to left, and you get a great stacking option. Plus the numbers are good throughout the lineup, as you can see, so they should be able to turn over the lineup frequently. Oh, and they’re reasonably priced.

It looks like last night is on repeat because the Twins are again in a nice spot. They are expensive, but again facing a bad White Sox pitcher and mediocre bullpen who is a bit taxed. The Twinkies are hot right now and will probably get some ownership with that total and their performance last night, but you can’t ignore these numbers. I like the full stack, but Cruz is certainly worth a one-off look, especially at that price.

I think Vegas has this one wrong. I guess it depends on which version of Yu Darvish shows up on the bump, but we have a Wrigley wind day where any fly ball has a chance. Again, good numbers throughout the lineup so if the Reds can get to Darvish, they should be able to turn over the lineup enough for any bat to succeed. Their prices are very reasonable too you can pair them with a lot of other stacks.

Honorable mentions: ARI, OAK, SEA, MIL, TB

DATA MODEL:

My model is built on the analysis of pitch types. The numbers you see are a batter’s stats (ISO, wOBA, xwOBA) vs handedness and pitch type. If the sample size is there, I firmly believe that this could be a better way to look at stats than your typical “ISO vs lefties”, “wOBA vs righties”, etc. thing you typically hear. HOWEVER, sample size for batted ball events, even with 2018-2019 data, can be misleading. You will see some extremes because there isn’t a good sample size, for the batter and/or the pitcher. Keep this in mind when looking at these stats, especially with rookie bats or rookie/bullpen arms.

I’ll explain my model in a couple easy steps since that is the way I built it and probably the easiest way for you to digest it:

  • Step 1: Calculate batters’ stats vs the opposing SP’s pitch mix and handedness. So instead of saying that I have a .200 ISO vs LHP, that may comprise of .300 ISO vs fastballs and .100 ISO vs changeups. If the opposing SP throws primarily fastballs, this will adjust my .200 ISO upwards to account for this scenario.
  • Step 2: Calculate batters’ stats from Step 1 vs how well the opposing SP throws his pitches. Step 1 tells us that, I hit fastballs really well. However, we know that not all fastballs are created equal. Some pitchers have a devastating fastball, others do not. This will adjust for that distinction.

Some other things to note:

  • These stats are based on 2018 and 2019 data. Data may not capture the last 1-2 weeks of play.
  • These stat numbers do NOT take into effect ownership, weather, ballpark, opposing bullpen, etc. I have, however, supplied ballpark and bullpen grades to assist in your lineup making decisions.
  • Heat mapping (aka conditional formatting) is applied for each stat category across the entire slate.
  • Ballpark and bullpen grades are based on the batters’ perspective.
  • “Value” stats are calculated as: (stat*100000)/DK salary
  • You will notice “#N/A” fields for some hitters. Most of these are for the pitcher as I have not calculated that into the model at this point. Others I need to do some data cleaning 🙂
  • Although I have updated and expanded my model it is not foolproof and is still a work in progress.

Keep in mind that this is meant to be one of many tools you use in your research and should not be considered the end-all, be-all of data.

~clary24

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