Accurately Predicting Football with Python & SQL

And betting on the outcomes

“I’ve got a hunch that Chelsea is going to win” isn’t a very good argument for why you should place a bet.

“I’m using an algorithm that is proven to accurately predict outcomes better than the bookmakers” is much better.

To predict these outcomes I’ve created a data warehouse with my co-developer (Estèphe Corlin) throughout the 2021/22 football season to store: bookies odds, our own odds, player stats, fixture outcomes and more. Below is an example of our “HomeOdds” table being created in the warehouse:

CREATE TABLE Football.HomeOdds(
       FixtureId      INT           
   ,   TeamId         INT
   ,   Market         VARCHAR(50)   
   ,   Odd            DECIMAL(4,2)
   ,   DataTimeStamp  DATETIME      NOT NULL
   ,   CONSTRAINT PK_BookieOdds PRIMARY KEY (FixtureId, TeamId, Market)
);

The data warehouse is a fully managed relational database that contains five seasons of data. It is populated through a mixture of backfills, web scraping and algorithmic calculations to calculate outcomes.

These calculations are based on the given xG for a team, their current form, their opponents ability to defend and whether they’re playing home or away. More details of this calculation can be found in the article below.