Knap Kings Greg Dray and Dylan Mortimer Background

Knap Kings Greg Dray and Dylan Mortimer

Background

Daily Fantasy Sports Contest websites Contest formats Contest rules

The Problem Lineup optimizers cost money Good lineups require research Too much data for humans Human bias A computer can do better!

Our Project

Vision Flexible Python package for DFS that leverages algorithmic optimization to deliver strong, personalized lineups to the user

The Algorithm Goal: Optimize total value of your roster Hire P players for N positions $X to spend Player 1. Position 2. Salary 3. Value

The Algorithm (with our constraints) Goal: Optimize total value of your roster Hire P players Constraints Player for N -Must hire for every positions -Hire 2 players for each position 1. -Players play multiple 2. positions -< 4 players from same team 3. $X to spend Position Salary Value

1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 Example problem V[i, x] x (cost) 0 i (position) Position 1 2 3 N=4 10 20 30 40 50 60 X=70

1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 1. Initialize values V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 0 0 0 0 1 2 3 N=4

1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 0 30 G 50 H 50 H 1 2 3 N=4 2. Hire most valuable affordable players for i=N

1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 3. Copy row above V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 3 0 30 30 50 50 50 N=4 0 30 G 50 H 50 H 1 2

if v[i + 1, x - p[i, k]. cost] + p[i, k]. vorp > v[i, x]: 1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 hire(k) V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 3 0 30 30 50 50 50 N=4 0 30 G 50 H 50 H 1 2 4. Compare cells to see if maximum total value is greater if you hire player or not

if v[i + 1, x - p[i, k]. cost] + p[i, k]. vorp > v[i, x]: 1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 hire(k) V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 3 0 30 - 30 50 50 50 N=4 0 30 G 50 H 50 H 1 2 4. Compare cells to see if maximum total value is greater if you hire player or not

if v[i + 1, x - p[i, k]. cost] + p[i, k]. vorp > v[i, x]: 1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 hire(player k) V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 3 0 30 - 40 E 50 50 50 N=4 0 30 G 50 H 50 H 1 2 4. Compare cells to see if maximum total value is greater if you hire player or not

if v[i + 1, x - p[i, k]. cost] + p[i, k]. vorp > v[i, x]: 1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 hire(player k) V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 3 0 30 - 40 E 60 F 50 50 N=4 0 30 G 50 H 50 H 1 2 4. Compare cells to see if maximum total value is greater if you hire player or not

if v[i + 1, x - p[i, k]. cost] + p[i, k]. vorp > v[i, x]: 1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 hire(player k) V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 3 0 30 - 40 E 60 F 50 50 50 N=4 0 30 G 50 H 50 H 1 2 4. Compare cells to see if maximum total value is greater if you hire player or not

if v[i + 1, x - p[i, k]. cost] + p[i, k]. vorp > v[i, x]: 1 2 3 4 Name Cost Value A 20 30 B 10 20 C 30 40 D 40 70 E 10 10 F 20 30 G 10 30 H 30 50 hire(k) V[i, x] x (cost) i (position) Position 0 10 20 30 40 50 60 X=70 3 0 30 - 40 E 60 F 60 F N=4 0 30 G 50 H 50 H 1 2 Finish row and repeat steps upwards

Our Progress

Original Goals ● Scrape multiple well-known projections ○ ○ Number. Fire Roto. Grinders ● Upload custom projections ● Optimize lineups ○ ○ ○ Fan. Duel Draft. Kings Single Entry Contests Multiple Entry Contests Lock Players Exclude Players ● Enter lineups into competition automatically ● Expand to other sports

Goals Accomplished Midway ● Scrape multiple well-known projections ○ ○ Number. Fire Roto. Grinders ● Upload custom projections ● Optimize lineups ○ ○ ○ Fan. Duel Draft. Kings Single Entry Contests Multiple Entry Contests Lock Players Exclude Players ● Enter lineups into competition automatically ● Expand to other sports

Final Goals Accomplished ● Scrape multiple well-known projections ○ ○ Number. Fire Roto. Grinders ● Upload custom projections ● Optimize lineups ○ ○ ○ Fan. Duel Draft. Kings Single Entry Contests Multiple Entry Contests Lock Players Exclude Players ● Enter lineups into competition automatically ● Login to Fan. Duel automatically ● Optimizer for both basketball and baseball

Results Summary User selects. . . Output Contest website(s) Projection website(s) # lineups to output Players to lock/exclude Optimal lineup(s) given constraints

Our Testing Method

Methodology ● Draft. Kings ○ Enter Daily Dollar (single entry) ● Fanduel (One for rotogrinders and numberfire) ○ Enter $1 single entry ○ Enter $1 50/50 entry

Our Success

Winnings ● Draft. Kings (since 4/21) ○ + $10. 84 ● Fanduel (since 4/15) ○ + $18. 74 ○ Rotogrinders ○ ○ ○ ■ ~ $14 Numberfire ■ ~ $4 Tournament ■ ~ $15 50/50 ■ ~ $4

Reflecting

Reflecting ● Exciting! ○ Algorithm about as optimal as proprietary optimizers ○ Implemented most of the same features successfully ○ Single entry bets appear profitable in long run ● Annoying : ( ○ Limitations mostly a lack of access to data ■ Scraping issues ■ Expensive APIs

Demo