With the BeatTheBookieDataService in place it’s also time to provide some new models. This post will take a look at possible models using the team statistics provided for each match by understat.com. Therefor I will compare 3 of the most used machine learning algorithms. Beside this, it’s also time to test again some basics for predictiv modeling for football: “To differ between home/away performance or not to differ”? For my Poisson models I always differed between home and away performance. But is this also needed, when using ML algorithms?
Continue reading “Using xG & advanced stats to predict football matches”Running Exasol on AWS
Automating data pipelines in AWS was just the first step of moving my betting models into the cloud. Nearly all my calculations were done in a Exasol database and I also want to keep them in a database. So I need to host one in my AWS account. For such use-cases AWS offers virtual EC2 instances. This blog will explain the single steps how to install an Exasol DB in AWS.
Continue reading “Running Exasol on AWS”Why every data scientist should learn SQL
It’s been quite a long time since my last post for my blog. But that has been because of a specific reason: I participated at the 2nd DFB Hackathon, which consumed a huge amount of my freetime, which I normally spent creating some content for my blog. The Hackathon was again a great experience as all this deep data science stuff is still a challenge for me. But there’s again on big question on my side: Why are data scientist often just using Python (or R) and don’t know, how and when to use SQL.
Continue reading “Why every data scientist should learn SQL”
Beat the Bookie 