This repo contains a predictive model for footabll matches implemented in golang & python. It leverages historical match data, player statistics, and other relevant factors to generate predictions the morning of each match's kickoff.
- Web Scraping: Automated web scrapers collect fixture and result data from reliable sources daily.
- Predictive Model: Predictive model (developed by myself) to analyze historical data to forecast match outcomes and identify value bets.
- Discord Notifications: Users receive notifications via Discord regarding potential value bets for upcoming matches.
- Backtesting Platform: A backtesting platform allows users to test and refine prediction strategies using historical data.
- Web Scrapers: Python scripts running on AWS CloudWatch Events collect fixture and result data, sending it to an AWS SQS queue.
- Main Application: A Go-based application processes incoming data, updates the database, and creates predictions and notifications. This is triggered by new entries to the SQS queue.
- Database: An SQL database stores historical match data, team statistics, and prediction results.
- Notification System: Discord integration sends notifications to users with details on potential value bets.
- Backtesting Platform: A separate module allows for historical strategy testing and refinement. This saves graphs and statistics to a data folder to help visualise results of the tests.
- Go
- Python
- Terraform
- Docker
- MySql