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MLB DFS Stacks: Welcome to DFS StacksMania

MLB DFS

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Ever felt like the Vegas odds on a particular baseball game seemed fishy? Ever wondered about past results of similarly fishy lines? A decade ago – upon seeing people in a betting forum celebrating a previously untouchable Cliff Lee getting shelled – I started asking myself questions like: “Was there negative line movement against Philly that got people betting against Lee?” and “Was there anything in the initial odds that could’ve possibly foreshadowed such a blowup?” 

 

Determined to go down this rabbit hole, I started tracking anything and everything odds-related I could access… game totals, public consensus numbers, line movements, moneylines, run lines, etc. Gradually this turned into a morning ritual – tracking up to 15 games a day, seven days a week. Fast forward 10 years, and I still don’t have an answer as to why Cliff Lee got destroyed that day (thankfully Statcast didn’t debut until 2015), but I do have over 10,000 lines (Vegas game odds and numbers that I track) logged into my database – a database that has become instrumental to my MLB DFS success.

Part of the reason for my initial success when I first started playing on DraftKings in 2014 is that after a couple of years of looking over these lines, I began to see patterns and trends based on a number of criteria. For instance, a line for one game would have a strong track record of being a low-scoring pitching duel. Or a line for another game would often result in a surprising offensive performance by the home underdog. A line that was strange that I had rarely seen before usually resulted in a higher-scoring affair that went over the Vegas total. I soon realized that these types of trends could serve as a starting point to help me embrace contrarian DFS plays.

So what does my process look like these days? Every morning before I begin researching the day’s games, I first run the lines through my database to see if any games stand out (I now have a program that allows me to enter the various odds for a game, and it’ll spit out the games in my database that are matching my parameters). After eliminating the games that typically result in 3-2 or 4-3 snoozefests, I’m usually left with a handful of games that have historically resulted in higher scores… with the teams in these games becoming my initial leans. At this point, I then begin digging deeper with typical MLB DFS analysis (barrel rate, batted ball data, pitch mix, etc.) to see if any of my initial leans have merit… and if they do have merit, then these teams will become featured in my regular Stacksmania article, four days a week right here on FTNDaily.

 

Obviously, it’s not exactly as simple as relying on past games to recommend stacks for future ones. But I do believe historical box scores can give us a glimpse into a possible range of outcomes. And in GPPs, we’re hoping to catch one of those outliers. While I know most pros are very model-heavy in their approach and that I’m marching to a very different beat with my process, I’m perfectly fine having an unconventional approach. After all, the goal in GPPs is to get different and finish in that top 1%, and my process has allowed me to do just that over the years.

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