This machine learning model utilizes a Random Forest Regressor to generate recommended spread bets for Major League Baseball (MLB) games. The model fetches game data from The Odds API, which aggregates data from reputable sportsbooks such as Fanduel, DraftKings, and Barstool.
The model considers several key performance metrics that reflect a team's past performance in the current 2023 season such as:
R: Runs,
H: Hits,
R/G: Runs per Game,
OPS: On-base Plus Slugging,
and more.
By comparing these metrics with the latest betting lines offered, the model can predict which spreads are most likely to be successful.
- Python
- Pandas
- NumPy
- Scikit-learn
- HTML
- CSS
- Gmail API
- The Odds API
-
Create an account with The Odds API to get an API key. The API key is then used as an argument to the
fetch_data_from_api()function in the script. -
Obtain credentials for Gmail API. These credentials should be stored in a
credentials.jsonfile in the root directory of the project. Read more on setting up the Gmail API with Python here -
Replace
msg["To"] = "YOUR_EMAIL_HERE"on line 247 with the email you want to receive the recommendations to. -
Once the above prerequisites are completed, you can run the
main()function to fetch the betting lines, generate recommendations, and send an email with the recommendations to a specified recipient.
Everyone is welcome to contribute to this project! Feel free to add new features, fix bugs, or make improvements. Just fork the repository, make your changes, and submit a pull request. I appreciate your help! :)
This project is intended for educational purposes only. It should not be used as the sole basis for actual betting decisions. Betting involves risk, and you should only bet with money you can afford to lose. Always bet responsibly.