As a data engineer with a focus on predictive modeling for sports betting, one of the key challenges is matching team names from different data sources. In this blog post, we will explore how to use fuzzy matching to match team names from different sources and discuss an example implementation in Python. Additionally, we will introduce a new endpoint from BeatTheBookieDataService that provides a comprehensive matching of team names.
Continue reading “Matching Team Names in Sports Betting Data: A Fuzzy Matching Approach”Automate your betting models with AWS
How does my typical betting weekend looks like, when I start ckecking, whether there are some interesting matches? I start my laptop, open the browser, start my Python program, start the database and after some minutes, I am able to start my data prcoessing, which collects all the data and calculates the predictions. That’s already great, but wouldn’t it be even better to have all predictions always already up-to-date? This blog will show you how to setup and run a small automated data pipeline in AWS, which extracts all stats from Understat.com.
Continue reading “Automate your betting models with AWS”A data journey – market values (part1)
When a rich club in Germany goes through a bad performance phase or loses an important match, we like to use the phrase “Geld schießt eben keine Tore”. What means more or less, that big money doesn’t ensure goals. But the overall acceptance is of course, that richer clubs are expected to win more often as they got the money to buy the best players. This inspired me to start a data journey about market values in the big 5 European leagues: What do the market values tell about the development in the different leagues? How do teams perform in relation to the money they spent? Does the market value of a team has a predictive significance?
Prepare data: football-data.co.uk (part 2)
In the first part Prepare data: football-data.co.uk (part 1) I described how the Data Vault model for the data of football-data.co.uk looks like. In the second part I will now focus on loading data into the Data Vault model. With the overall analytical architecture in mind this equates the data integration process between the stage layer and the raw data layer.
Continue reading “Prepare data: football-data.co.uk (part 2)”
Prepare data: football-data.co.uk (part 1)
In the post Gather data: football-data.co.uk I described, how you can load CSV data into the Exasol database. As the data is now available at the Stage Layer in the database, I must now prepare the data and persist it at the Raw Data Layer, so that I can easily use it for building predictive models.
With part 1 of this post I want to explain, what Data Vault modeling is and how the Data Vault model for the data structure of football-data.co.uk looks like. With part 2 I will explain, how you load data into the developed Data Vault model.
Continue reading “Prepare data: football-data.co.uk (part 1)”
Beat the Bookie 