Python Football Betting Model for Six Leagues
Using statistics, Pandas, BeautifulSoup and AWS to identify value bets
Last year, I built a football betting model (algorithm) in Python to help me make data-driven predictions and to identify betting opportunities in the English Premier League (EPL).
This year, I re-built the system from the ground up to find betting opportunities across six different leagues (EPL, La Liga, Bundesliga, Ligue 1, Serie A and RFPL).
After completing my last model in late December 2019, I began putting it to the test with £25 of bets every week. Unfortunately, I only managed to fit in eight weeks of betting before COVID-19 cut the EPL short.
The good news is that I broke even during this period. I bet £200, and I got £200 back.
But I’m not here to break even.
In this article, I will explain my methodology, technical implementation and betting strategy to help you create your own betting model with Python.
Methodology & Code
The new model leverages a lot of the code that was used in the previous model and can be simplified into four steps:
