A tool to automate and optimize DraftKings and FanDuel lineup construction.
Project description
## Introduction · [](https://travis-ci.org/BenBrostoff/draftfast) · [](https://draftfast.herokuapp.com/) · [](https://www.patreon.com/user?u=8965834)
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.
## Installation
Requires Python 3.6.
```bash
pip install draftfast
```
## Usage
Example usage ([you can experiment with these examples in repl.it](https://repl.it/@BenBrostoff/AllWarlikeDemoware)):
```python
from draftfast import rules
from draftfast.optimize import run
from draftfast.orm import Player
from draftfast.csv_parse import salary_download
# Create players
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,
)
# 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](https://github.com/BenBrostoff/draftfast/tree/master/examples).
## Game Rules
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` |
| NASCAR | FanDuel | `FD_NASCAR_RULE_SET` |
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:
```python
from draftfast import rules
nhl_rules = rules.RuleSet(
site=rules.DRAFT_KINGS,
league='NHL',
roster_size='9',
position_limits=[['C', 9, 9]],
salary_max=50_000,
)
```
## CSV Upload
```python
from draftfast.csv_parse import uploaders
uploader = uploaders.DraftKingsNBAUploader(
pid_file='./pid_file.csv',
)
uploader.write_rosters(rosters)
```
## Support and Consulting
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](mailto:ben.brostoff@gmail.com).
# Credits
Special thanks to [swanson](https://github.com/swanson/), who authored [this repo](https://github.com/swanson/degenerate), which was the inspiration for this one.
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.
## Installation
Requires Python 3.6.
```bash
pip install draftfast
```
## Usage
Example usage ([you can experiment with these examples in repl.it](https://repl.it/@BenBrostoff/AllWarlikeDemoware)):
```python
from draftfast import rules
from draftfast.optimize import run
from draftfast.orm import Player
from draftfast.csv_parse import salary_download
# Create players
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,
)
# 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](https://github.com/BenBrostoff/draftfast/tree/master/examples).
## Game Rules
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` |
| NASCAR | FanDuel | `FD_NASCAR_RULE_SET` |
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:
```python
from draftfast import rules
nhl_rules = rules.RuleSet(
site=rules.DRAFT_KINGS,
league='NHL',
roster_size='9',
position_limits=[['C', 9, 9]],
salary_max=50_000,
)
```
## CSV Upload
```python
from draftfast.csv_parse import uploaders
uploader = uploaders.DraftKingsNBAUploader(
pid_file='./pid_file.csv',
)
uploader.write_rosters(rosters)
```
## Support and Consulting
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](mailto:ben.brostoff@gmail.com).
# Credits
Special thanks to [swanson](https://github.com/swanson/), who authored [this repo](https://github.com/swanson/degenerate), which was the inspiration for this one.
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