# Odds by Poisson Distribution
This library enables the calculation of betting odds (moneyline, handicaps, totals) based on team qualities using the Poisson distribution.
It provides a simple interface for initializing calculations, requiring only the qualities of both teams. These qualities can be obtained using metrics such as expected goals (xG) or average number of goals.
## Installation
```
pip install poisson-odds
```
## Usage Example
```
from poisson_odds import *
test = Poisson(1.1, 2.1)
test.print_probability_table_goal_draws()
### Output:
```
```
print(test.moneyline)
### Output:
```
```
1-X-2: 5.291-4.878-1.669
```
```
handicaps = test.calculate_handicap_odds_by_Poisson()
print('\n'.join([str(items) for key, items in handicaps.items()]))
### Output:
```
```
...
0.75: 2.206 / 1.879
1.0: 1.918 / 2.089
1.25: 1.75 / 2.403
1.5: 1.582 / 2.717
...
```
```
totals = test.calculate_total_odds_by_Poisson()
print('\n'.join([str(items) for key, items in totals.items()]))
### Output:
```
```
...
Over/Under 2.75: 1.788 / 2.333
Over/Under 3: 1.961 / 2.04
Over/Under 3.25: 2.243 / 1.848
Over/Under 3.5: 2.525 / 1.656
...
```
## Demonstrating
A [real-world example](https://github.com/nikolaitolmachev/poisson_odds_demo) of using the 'poisson_odds' library to analyze actual NHL odds by comparing them to probabilities based on expected goals (xG).
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"description": "# Odds by Poisson Distribution\r\nThis library enables the calculation of betting odds (moneyline, handicaps, totals) based on team qualities using the Poisson distribution.\r\n\r\nIt provides a simple interface for initializing calculations, requiring only the qualities of both teams. These qualities can be obtained using metrics such as expected goals (xG) or average number of goals.\r\n\r\n## Installation\r\n```\r\npip install poisson-odds\r\n```\r\n\r\n## Usage Example\r\n```\r\nfrom poisson_odds import *\r\n\r\ntest = Poisson(1.1, 2.1)\r\n\r\ntest.print_probability_table_goal_draws()\r\n### Output:\r\n```\r\n\r\n```\r\nprint(test.moneyline)\r\n\r\n### Output:\r\n```\r\n```\r\n1-X-2: 5.291-4.878-1.669\r\n```\r\n```\r\nhandicaps = test.calculate_handicap_odds_by_Poisson()\r\nprint('\\n'.join([str(items) for key, items in handicaps.items()]))\r\n### Output:\r\n```\r\n```\r\n...\r\n0.75: 2.206 / 1.879\r\n1.0: 1.918 / 2.089\r\n1.25: 1.75 / 2.403\r\n1.5: 1.582 / 2.717\r\n...\r\n```\r\n```\r\ntotals = test.calculate_total_odds_by_Poisson()\r\nprint('\\n'.join([str(items) for key, items in totals.items()]))\r\n### Output:\r\n```\r\n```\r\n...\r\nOver/Under 2.75: 1.788 / 2.333\r\nOver/Under 3: 1.961 / 2.04\r\nOver/Under 3.25: 2.243 / 1.848\r\nOver/Under 3.5: 2.525 / 1.656\r\n...\r\n```\r\n\r\n## Demonstrating\r\nA [real-world example](https://github.com/nikolaitolmachev/poisson_odds_demo) of using the 'poisson_odds' library to analyze actual NHL odds by comparing them to probabilities based on expected goals (xG).\r\n",
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