CanonDraft Strategy

Monte Carlo Strategies to Win 2016 MFL10s – Part I

Editor’s note: This is one of two Monte Carlo simulation articles aimed at solving the best-ball puzzle, each using different assumptions. We believe doing so gives a good idea of the range of possible outcomes. For the other article by Nick Giffen (@RotoDoc), click here.

We’re back!

You MFL vets out there might remember the Monte Carlo article that your humble authors wrote a few years back. Some of you took our advice and increased your winnings by double digits. Kevin Cole wrote these very kind words about our Early-RB Strategy that we advocated for in our original 2014 article:
Around mid-way through my MFL10 drafting, I switched from a 4 RB allocation with one or two taken in the first five rounds, to much earlier RB drafting with three to four taken in the first 5 rounds. I finally “bought in” on the philosophy presented in the excellent Monte Carlo analysis by AJ Bessette and Greg Meade. The improvement in results was dramatic. Luckily, my switch in strategy coincided with a switch to MFL50s & 100s, which ended up turning my losses into a 25 percent gain.
While we try not to strain our arms patting ourselves on the back, the reality is that the 2016 NFL year will be much different from the 2014 one. The Golden Age of Passing continues to overwhelm both real life and fantasy football, and past seasons are dropping further and further out of sample. Our original sample of 2011-2013 isn’t recent enough to use for decision making this year, so we dug into our Monte Carlo model to update our analysis. After figuring out what the hell we were doing in 2014 (comment your code people), we made a few improvements to develop a new pick order for 2016.


We used a similar Monte Carlo model to our 2014 version, with the big change being using 2014-2015 data instead of 2011-2013. If you care about the specifics of what a Monte Carlo analysis is, check out our old article where we describe it in depth. Tl;dr, it simulates weekly scores based on the positions you pick at each ADP to figure out what positional allocation is optimal. We also put in some adjustments for injuries which our original article didn’t account for. We looked at historical injury rates by position as well as some third party injury predictions to come up with a likelihood of missing each game by position. This was added into the simulation so that players took zeroes for bye weeks and injuries the correct proportion of weeks. This was modeled in separately from ADP since injury likelihood and ADP have no reason to be correlated, so we assumed the same rate of games missed by position regardless of where they were selected.

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By AJ Bessette and Greg Meade | @mirage88 | Archive

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