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Updated ReadMe for future Use and Contributions #284

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Updated documentation
ejmercier Oct 2, 2023
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Update README.md
ejmercier Nov 27, 2023
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# NBA Sports Betting Using Machine Learning 🏀
<img src="https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting/blob/master/Screenshots/output.png" width="1010" height="292" />

A machine learning AI used to predict the winners and under/overs of NBA games. Takes all team data from the 2007-08 season to current season, matched with odds of those games, using a neural network to predict winning bets for today's games. Achieves ~75% accuracy on money lines and ~58% on under/overs. Outputs expected value for teams money lines to provide better insight. The fraction of your bankroll to bet based on the Kelly Criterion is also outputted. Note that a popular, less risky approach is to bet 50% of the stake recommended by the Kelly Criterion.
A machine learning AI used to predict the winners and under/overs of NBA games.

## About

Takes all team data from the 2007-08 season to current season, matched with odds of those games, using a neural network to predict winning bets for today's games.

Achieves ~75% accuracy on money lines and ~58% on under/overs.
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Is this correct? The current model in the repo is at 68.9% on ML and 54.8% on OU


## Betting Strategy
Outputs expected value for teams money lines to provide better insight. The fraction of your bankroll to bet based on the Kelly Criterion is also outputted.

Note that a popular, less risky approach is to bet 50% of the stake recommended by the Kelly Criterion.

## Installation
*Side comment:
Make sure you use Python 3.8. If for some reason "python3" does not work, try using "python" instead.*
### Installing the source code
```
git clone https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting.git
```

### Create environment

Navigate into to project and create an environment
```
cd NBA-Machine-Learning-Sports-Betting
```
```
python3 -m venv env
```
### Activate environment
On Windows:
```
source env\Scripts\activate.bat
```
On Unix/MacOS:
```
source env/bin/activate
```

### Install packages
```
python3 -m pip install -r requirements.txt
```
## Packages Used

Use Python 3.8. In particular the packages/libraries used are...
In particular the packages/libraries used are...

* Tensorflow - Machine learning library
* XGBoost - Gradient boosting framework
Expand All @@ -21,42 +64,43 @@ Use Python 3.8. In particular the packages/libraries used are...

Make sure all packages above are installed.

```bash
$ git clone https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting.git
$ cd NBA-Machine-Learning-Sports-Betting
$ pip3 install -r requirements.txt
$ python3 main.py -xgb -odds=fanduel
```

Odds data will be automatically fetched from sbrodds if the -odds option is provided with a sportsbook. Options include: fanduel, draftkings, betmgm, pointsbet, caesars, wynn, bet_rivers_ny
python3 main.py -xgb -odds=fanduel
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why is xgb model exclusively used over the other ones?

```
*Note:
Odds data will be automatically fetched from **sbrodds** if the -odds option is provided with a sportsbook. Options include: fanduel, draftkings, betmgm, pointsbet, caesars, wynn, bet_rivers_ny*

If `-odds` is not given, enter the under/over and odds for today's games manually after starting the script.

Optionally, you can add '-kc' as a command line argument to see the recommended fraction of your bankroll to wager based on the model's edge

## Flask Web App
<img src="https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting/blob/master/Screenshots/Flask-App.png" width="922" height="580" />

This repo also includes a small Flask application to help view the data from this tool in the browser. To run it:
```
cd Flask
flask --debug run
```
Optionally, you can add `-kc` as a command line argument to see the recommended fraction of your bankroll to wager based on the model's edge

## Getting new data and training models
## How to get new Data and Train Models
### Create dataset with the latest data for 2022-23 season
```
# Create dataset with the latest data for 2022-23 season
cd src/Process-Data
python -m Get_Data
python -m Get_Odds_Data
python -m Create_Games

# Train models
```
### Train models
```
cd ../Train-Models
python -m XGBoost_Model_ML
python -m XGBoost_Model_UO
```
## Flask Web App
<img src="https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting/blob/master/Screenshots/Flask-App.png" width="922" height="580" />

This repo also includes a small Flask application to help view the data from this tool in the browser. To run it:
```
cd Flask
flask --debug run
```

## Contributing

All contributions welcomed and encouraged.
All contributions welcomed and encouraged.

Please abide by the Open Source Contributing Guidelines:
https://opensource.guide/how-to-contribute/