NASCAR DFS Pocono: Model Power Rankings and Early Values

To get the early jump on NASCAR DFS Pocono this weekend, I used my machine learning model to generate expected finishing position for each driver just based off their driver attributed and performance histories this year and at similar tracks. This let’s us create a power ranking for each driver purely based off the most accurate model fits possible, and with no subjectivity. This way we can find early value plays, as well as the top expected performers this weekend at Pocono. I’m going to update this model after practice as well, but this will give us a heads up on which drivers might be chalk depending on how they practice and where they qualify, or if they are slightly off in one of those categories, then the best values might become good pivot plays off the chalk in GPPs. Here are the pre-race weekend model-based power rankings for NASCAR DFS Pocono this weekend. For more this weekend, check out this week’s NASCAR DFS Pocono content schedule. For strategy insights, check out this weekend’s NASCAR episode of On the Daily DFS and my YouTube video on how to use the NASCAR DFS Multi-Lineup Optimizer for this weekend’s race.

NASCAR DFS Pocono – Model Power and Value Rankings

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By RotoDoc | @RotoDoc | Archive

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