The Little Engine That Could - January, 22nd 2020

I noticed my lineup optimizer created lines that finish 1st in a large grand prize pool tournament (GPP) . I then applied machine learning algorithms to use outputs from a monte carlo simulation to find the winning lineups. Looking at the data, I realized “beaufourd” was wrong- he assumed winning lineups would fall within the top 20% of the monte carlo simulation. It turns out “middle of the pack” lines far exceed their expectations and win GPPs!!! I think this is a product of a chaotic system- NBA player performance night to night is random, the lineup combinations you face night to night are random; injury and late scratches change optimal lineups in a blink of an eye. In the past, I had to use advanced statistics to predict antenna performance- characteristic functions are the inverse of probability density functions but can be used to model chaotic systems. Applying some of these concepts to the contest simulator, I was able to fish out the highest scoring lineup.

This discovery lead to a set of a python scripts I call the DFS Engine.

My DFS Engine was used to generate a lot of lineups. These lineups were then uploaded to low stakes tournaments on DraftKings and FanDuel. The Engine’s results will be tracked from 12/28/2019 to 3/01/2020.

January 22nd – January 29th, I will feed different projections systems to my lineup optimizer.

Each day I have been saving the projections from RotoGrinders,,, and BathrobeDFS. I decided to stop using customDFS. They’re are still under development and their website is in its beta version. I haven’t been able to collect projections every day to compare fairly with other sites. Stay tuned for a weekend post.

DFS Engine Notes

I have a few side projects going on:

  • creating a smart speaker app to (1) give updated player news and (2) allow DFS players to change lineups with their voice
    • I registered as a twitter developer and created a script to save tweets based off a hashtag. The next step is to download Amazon Lambda’s API so I can create a smart speaker application to read the tweets in real time
  • creating another optimizer but with a different solver. I will be using a variant of integer programming instead of mixed integer that uses linear programming (which I believe the entire DFS industry is using)
    • I started to dive into the Julia JuMP package and python’s PuLP package. Also I started exploring the GAMS package that has many different variants of mixed integer programming solvers such as the COIN-OR BONMIN.

If you are interested in collaborating on one of these projects let me know. Also find out more about the engine at: Sailing Wide Right

Player Exposures

This blog was updated on January 22nd, 2020 at 11AM (EST). When reading this remember a lot will change from now till lock. Players highlighted are important pillars to my lineup builds. Purple shows my core plays, green shows my cash play and red shows my GPP dart. Percentages next to players name show my exposures.


Core: Jeff Teague, Gordon Hayward, and Lou Williams

Cash: Buddy Hield

GPP: Devin Booker

If you have any questions, comments or would like to hash out an idea, DM me @Numeric_DFS and we can continue the discussion.

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