This document describes Ed Goodwin's approach to optimizing a fantasy football draft in R. It discusses: 1) Using historical NFL player performance data to rank players and select an optimal starting lineup and bench that maximizes projected points within a risk threshold. 2) Connecting to a MySQL database of player stats using RMySQL and using the Rglpk package to solve the linear optimization problem of drafting the best team. 3) Lessons learned including scrubbing data, running mock drafts, and acknowledging luck is still involved in fantasy football.

1. Fantasy Football Draft
Optimization in R
Ed Goodwin, CFA

2. What is this fantasy football
stuff anyway?
• All participants compete in a league, and draft
players from the NFL in a virtual draft. Based
on the real life performance of the athletes,
participants score points. Each league can have
different rules around scoring, drafting, and
trading players.

3. Our League Rules
• Snake draft
• Standard ESPN scoring
• 9 starter players, and 7 bench players

4. Number of hours of football Ed watched last season
0

5. The problems to solve?
• How do we rank players for each position?
• How do we choose the best team?
• How do we handle the realities of a live draft?
Everyone is trying to draft the best available
players every round.

6. Workflow
• Think about the problem and
come up with some initial
thoughts
• Read a lot of blogs on fantasy
football
• Prototype and iterate
• Test
• Revise

7. Constraints
• Time
• Knowledge

8. Initial Thoughts
• Where do we get data? Screen scraping
• How to determine the best players to draft?
Use historic data to forecast performance
• How do we pick the best team? Optimize for the
best risk adjusted score (whatever that means)
• How do we handle the draft? Need to
recalculate the best team based on who has been
selected already

9. Exploratory Graphics
• ggplot2
and dplyr

12. Some good sources for
fantasy football optimization
• fantasyfootballanalytics.com
• advancedfootballanalytics.com
• thehuddle.com
• fftoday.com

13. My Solution
csv
Data
File
MySQL
Sequel
Pro (UI)
R Linear
Optimizer
ESPN
Draft
Site

14. MySQL Database

16. Linear Optimizer
• Uses RMySQL package to connect to database
• Uses Rglpk to run the linear optimization

17. Linear Optimizer Logic
• Select two sets of players from the database
• Players that are still available to draft
• Players that I have drafted
• Set rules based on whether I am filling my starting lineup or filling my bench
• Based on the number of open slots I have on my team, select players to
draft for the open slots that
• Maximizes my expected points
• Keeps my risk within a stated threshold
• Satisfies the rules about what positions can make up a team

18. RMySQL
• Package to connect to
MySQL databases
• Use dbConnect()
function to establish
connection
• Use dbGetQuery()
function to run SQL
statements against
database

19. Rglpk - R/GNU Linear
Programming Kit
• Tools to create generalized linear models in R
• Rglpk_solve_LP() function used to solve for
optimization with constraints

20. Rglpk_solve_LP() requires:
• obj vector you are trying to maximize
• mat matrix of constraints
• dir vector of constraint “directions” (i.e. “>=“, “==“, or “<“,
etc.)
• rhs vector of right hand side of the constraints
• types vector of character types of constraints (“B” for binary,
“C” for continuous, “I” for integer)
• max specifies whether the goal is to maximize (“TRUE”) or
minimize (“FALSE”) the solution

21. Set your constraints

23. Set rhs constraints

24. Optimized Starter Team

25. Lessons Learned
• Scrub your data
• Run mock drafts ahead of time
• Vet out your assumptions
• Think through the correlations
• Replacement value more important than point
value

26. Lessons Learned
• Shake out the optimization
rules
• Don’t weight one team too
heavy in your lineup
• There’s still a lot of luck
involved (Andrew Luck)

27. Next Steps
• Weekly projections to optimize waiver wire
trades
• Screen scraping
• Bench optimization