ff-optimizer

This repo contains a quick and dirty fantasy football optimizer for weekly competitions found on DraftKings or FanDuel.

Scraping data

I am scraping data from 3 sites:

1. Roto Guru: provides weekly player salary information and historical FF points scored
2. Football Locks: provides weekly schedules, lines, and over/under
3. Fantasy Pros: provides weekly player points projections and a "start/sit grade"

Each site has its own quirks for actually getting the data into a usable form. I also tried creating a singular mapping for teams and a primary key for merging the datasets together.

Team Optimizer

The first step involves filtering the set of players. I chose to filter by position with minimum bounds on salary, "value," and "start/sit grade." Getting the number of "feasible" players down into 50s-60s helps with the optimization time since it lowers the number of combinations.

The second step involves filtering down all the possible combinations to a set of "feasible" teams. I define a "feasible" team by the following constraints/rules of thumb:

1. The total salary should equal $50k (i.e., don't leave money on the table);
2. The offensive players' teams should not be playing the defense;
3. No more than 2 players on the same team;
4. QB and RB not on the same team;
5. QB and WR on the same team; and
6. TE and WR not on the same team.

The third step involves scoring each team. I use the following 4 metrics to score each team:

1. Total average historical points per game
2. Total expected team score based on O/U and lines
3. Total projected points
4. Average start/sit score (converting the grades to an academic 4.0 scale; i.e., A = 4.0, B = 3.0, etc.)

Using these scores, I sort the teams and create a "normalized" score based on the ordinal position of each team. I then take a weighted average of those scores and re-sort the entire list of teams.

Running the code

Create a Python 3.8 virtual environment and run pip install -r requirements.txt.

Run the scraper: python scraper.py
(note this will save the scraped data to a /data directory)

Run the optimizer: python optimizer.py
(this could take a while depending on your computer)