An incredibly powerful tool that automates and optimizes lineup building, allowing you to enter thousands of lineups in any DraftKings or FanDuel contest in the time it takes you to grab a coffee.
Requires Python 3.9+.
pip install draftfastExample usage (you can experiment with these examples in repl.it):
from draftfast import rules
from draftfast.optimize import run
from draftfast.orm import Player
from draftfast.csv_parse import salary_download
# Create players for a classic DraftKings game
player_pool = [
Player(name='A1', cost=5500, proj=55, pos='PG'),
Player(name='A2', cost=5500, proj=55, pos='PG'),
Player(name='A3', cost=5500, proj=55, pos='SG'),
Player(name='A4', cost=5500, proj=55, pos='SG'),
Player(name='A5', cost=5500, proj=55, pos='SF'),
Player(name='A6', cost=5500, proj=55, pos='SF'),
Player(name='A7', cost=5500, proj=55, pos='PF'),
Player(name='A8', cost=5500, proj=55, pos='PF'),
Player(name='A9', cost=5500, proj=55, pos='C'),
Player(name='A10', cost=5500, proj=55, pos='C'),
]
roster = run(
rule_set=rules.DK_NBA_RULE_SET,
player_pool=player_pool,
verbose=True,
)
# Or, alternatively, generate players from a CSV
players = salary_download.generate_players_from_csvs(
salary_file_location='./salaries.csv',
game=rules.DRAFT_KINGS,
)
roster = run(
rule_set=rules.DK_NBA_RULE_SET,
player_pool=players,
verbose=True,
)You can see more examples in the examples directory.
Optimizing for a particular game is as easy as setting the RuleSet (see the example above). Game rules in the library are in the table below:
| League | Site | Reference |
|---|---|---|
| NFL | DraftKings | DK_NFL_RULE_SET |
| NFL | FanDuel | FD_NFL_RULE_SET |
| NBA | DraftKings | DK_NBA_RULE_SET |
| NBA | FanDuel | FD_NBA_RULE_SET |
| MLB | DraftKings | DK_MLB_RULE_SET |
| MLB | FanDuel | FD_MLB_RULE_SET |
| WNBA | DraftKings | DK_WNBA_RULE_SET |
| WNBA | FanDuel | FD_WNBA_RULE_SET |
| PGA | FanDuel | FD_PGA_RULE_SET |
| PGA | DraftKings | DK_PGA_RULE_SET |
| PGA_CAPTAIN | DraftKings | DK_PGA_CAPTAIN_RULE_SET |
| NASCAR | FanDuel | FD_NASCAR_RULE_SET |
| NASCAR | DraftKings | DK_NASCAR_RULE_SET |
| SOCCER | DraftKings | DK_SOCCER_RULE_SET |
| EuroLeague | DraftKings | DK_EURO_LEAGUE_RULE_SET |
| NHL | DraftKings | DK_NHL_RULE_SET |
| NBA Pickem | DraftKings | DK_NBA_PICKEM_RULE_SET |
| NFL Showdown | DraftKings | DK_NFL_SHOWDOWN_RULE_SET |
| NBA Showdown | DraftKings | DK_NBA_SHOWDOWN_RULE_SET |
| MLB Showdown | DraftKings | DK_MLB_SHOWDOWN_RULE_SET |
| XFL | DraftKings | DK_XFL_CLASSIC_RULE_SET |
| Tennis | DraftKings | DK_TEN_CLASSIC_RULE_SET |
| CS:GO | DraftKings | DK_CSGO_SHOWDOWN |
| F1 | DraftKings | DK_F1_SHOWDOWN |
Note that you can also tune draftfast for any game of your choice even if it's not implemented in the library (PRs welcome!). Using the RuleSet class, you can generate your own game rules that specific number of players, salary, etc. Example:
from draftfast import rules
golf_rules = rules.RuleSet(
site=rules.DRAFT_KINGS,
league='PGA',
roster_size='6',
position_limits=[['G', 6, 6]],
salary_max=50_000,
)Usage example:
class Showdown(Roster):
POSITION_ORDER = {
'M': 0,
'F': 1,
'D': 2,
'GK': 3,
}
showdown_limits = [
['M', 0, 6],
['F', 0, 6],
['D', 0, 6],
['GK', 0, 6],
]
soccer_rules = rules.RuleSet(
site=rules.DRAFT_KINGS,
league='SOCCER_SHOWDOWN',
roster_size=6,
position_limits=showdown_limits,
salary_max=50_000,
general_position_limits=[],
)
player_pool = salary_download.generate_players_from_csvs(
salary_file_location=salary_file,
game=rules.DRAFT_KINGS,
)
roster = run(
rule_set=soccer_rules,
player_pool=player_pool,
verbose=True,
roster_gen=Showdown,
)PlayerPoolSettings
min_projmax_projmin_salarymax_salarymin_avgmax_avg
OptimizerSettings
stacks- A list ofStackobjects. Example:
roster = run(
rule_set=rules.DK_NHL_RULE_SET,
player_pool=player_pool,
verbose=True,
optimizer_settings=OptimizerSettings(
stacks=[
Stack(team='PHI', count=3),
Stack(team='FLA', count=3),
Stack(team='NSH', count=2),
]
),
)Stack can also be tuned to support different combinations of positions. For NFL,
to only specify a QB-WRs based stack of five:
Stack(
team='NE',
count=5,
stack_lock_pos=['QB'],
stack_eligible_pos=['WR'],
)custom_rules- Define rules that set if / then conditions for lineups.
For example, if two WRs from the same team are in a naturally optimized lineup, then the QB must also be in the lineup. You can find some good examples of rules in draftfast/test/test_custom_rules.py.
from draftfast.optimize import run
from draftfast.settings import OptimizerSettings, CustomRule
# If two WRs on one team, play the QB from same team
settings = OptimizerSettings(
custom_rules=[
CustomRule(
group_a=lambda p: p.pos == 'WR' and p.team == 'Patriots',
group_b=lambda p: p.pos == 'QB' and p.team == 'Patriots',
comparison=lambda sum, a, b: sum(a) + 1 <= sum(b)
)
]
)
roster = run(
rule_set=rules.DK_NFL_RULE_SET,
player_pool=nfl_pool,
verbose=True,
optimizer_settings=settings,
)Another common use case is given one player is in a lineup, always play another player:
from draftfast.optimize import run
from draftfast.settings import OptimizerSettings, CustomRule
# If Player A, always play Player B and vice versa
settings = OptimizerSettings(
custom_rules=[
CustomRule(
group_a=lambda p: p.name == 'Tom Brady',
group_b=lambda p: p.name == 'Rob Gronkowski',
comparison=lambda sum, a, b: sum(a) == sum(b)
)
]
)
roster = run(
rule_set=rules.DK_NFL_RULE_SET,
player_pool=nfl_pool,
verbose=True,
optimizer_settings=settings,
)Custom rules also don't have to make a comparison between two groups. You can say "never play these two players in the same lineup" by using the CustomRule#comparison property.
# Never play these two players together
settings = OptimizerSettings(
custom_rules=[
CustomRule(
group_a=lambda p: p,
group_b=lambda p: p.name == 'Devon Booker' or p.name == 'Chris Paul',
comparison=lambda sum, a, b: sum(b) <= 1
)
]
)
roster = run(
rule_set=rules.DK_NBA_RULE_SET,
player_pool=nba_pool,
verbose=True,
optimizer_settings=settings,
)Importantly, as of this writing, passing closures into CustomRules does not work (ex. lambda p: p.team == team),
so dynamically generating rules is not possible. PRs welcome for a fix here, I believe this is a limitation of ortools.
LineupConstraints
locked- list of players to lockbanned- list of players to bangroups- list of player groups constraints. See below
roster = run(
rule_set=rules.DK_NFL_RULE_SET,
player_pool=player_pool,
verbose=True,
constraints=LineupConstraints(
locked=['Rob Gronkowski'],
banned=['Mark Ingram', 'Doug Martin'],
groups=[
[('Todd Gurley', 'Melvin Gordon', 'Christian McCaffrey'), (2, 3)],
[('Chris Carson', 'Mike Davis'), 1],
]
)
)no_offense_against_defense- Do not allow offensive players to be matched up against defensive players in the optimized lineup. Currently only implemented for soccer, NHL, and NFL -- PRs welcome!
from draftfast.csv_parse import uploaders
uploader = uploaders.DraftKingsNBAUploader(
pid_file='./pid_file.csv',
)
uploader.write_rosters(rosters)DFS optimization is only one part of a sustainable strategy. Long-term DFS winners have the best:
- Player projections
- Bankroll management
- Diversification in contests played
- Diversification across lineups (see
draftfast.exposure) - Research process
- 1 hour before gametime lineup changes
- ...and so much more
DraftFast provides support and consulting services that can help with all of these. Let's get in touch today.
Run tests or set of tests:
# All tests
nosetests
# Single file
nosetests draftfast/test/test_soccer.py
# Single test
nosetests draftfast/test/test_soccer.py:test_soccer_dk_no_opp_dRun linting
flake8 draftfast
Special thanks to swanson, who authored this repo, which was the inspiration for this one.
Current project maintainers:
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