The BeatTheBookie App is a web-based data application that grants me access to predictions generated by various models that have proven their performance over time on my blog. However, determining which model to utilize for a specific match can be challenging. Thankfully, the model performance dashboard offers the solution to this dilemma. By exploring the dashboard, I gain valuable insights into the theoretical performance of the different models in past scenarios. This information aids me in selecting the most suitable model for each match, increasing the chances of making informed betting decisions.
Continue reading “BeatTheBookieApp – Model performance”BeatTheBookieApp – Model predictions
The BeatTheBookie App is a web data app that provides me with predictions for all models that have demonstrated their performance throughout the history of my blog. As it has already been proven, there is no single best model for all divisions. Therefore, I wanted to be able to compare predictions, as each model takes into account different aspects of past performance. The result is the model predictions dashboard.
Continue reading “BeatTheBookieApp – Model predictions”Matching Team Names in Sports Betting Data: A Fuzzy Matching Approach
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”Season 2022/23 – The Ligue 1 Disaster
Welcome to my latest blog post, titled “The Ligue 1 Disaster.” As a passionate sports bettor, I’ve been keeping a close eye on the results of my latest betting history over the past month, and one league that has left me utterly disappointed is Ligue 1. Known for its exciting matches and talented players, the French top-tier football league has seen unexpected twists and turns that have turned my betting predictions upside down. In this post, I’ll delve into the recent woes I’ve experienced with my bets on Ligue 1 and analyze the factors that have contributed to what can only be described as a disaster. Join me as I reflect on the surprising outcomes, unforeseen upsets, and the rollercoaster ride of emotions that have made Ligue 1 a source of frustration in my recent betting endeavors.
Continue reading “Season 2022/23 – The Ligue 1 Disaster”Ensemble modeling for football predictions – Is one model enough?
The intention of this blog post, was a bit different at the beginning. At first I wanted to improve my existing ML Poisson model by adding the team market values as additional features. But as I worked on the topic, one question came more and more to the fore: Is one single model enough to BeatTheBookie?
Continue reading “Ensemble modeling for football predictions – Is one model enough?”Vanilla Poisson performance incl. minor leagues
As already done for the ZIP Poisson model, I also added some smaller leagues data to my Vanilla Poisson model: Championship, Seria B, La Liga 2, Eredivise, Liga Portugal. All these additional leagues are already available through my data service. So it’s time to take a look how profitable these new leagues are using the Vanilly Poisson model.
Continue reading “Vanilla Poisson performance incl. minor leagues”Season 2022/23 – A turning point (in theory)
This will be the 2nd blog about my betting performance in the season 2022/23 using my ML Poisson model. The beginning of the season was a sobering one. My theoraticaly worst model performed the best and the ML Poisson model, which I am using for betting showed a really bad profit. So let’s have a look, how things changed in the 2nd part of the first season half.
Read MoreZIP model performance incl. minor leagues
Some days ago I extended my ZIP Poisson model by some additional leagues. These are: Championship, Seria B, La Liga 2, Eredivise, Liga Portugal. It’s always helpful to be able to select more possible bets. Playing more bets reduces the variance of your hit rate and provides a more stable average profit. So let’s have a look, how the ZIP Poisson model performs including the new leagues.
Continue reading “ZIP model performance incl. minor leagues”Season 2022/23 – A sobering beginning
In the past I already posted some summaries in my pick history for different models. So everybody could get an impression, how a real life betting using my models could look like and to test, whether the profit, indicated by the backtesting, can also be reached in the future. With this post I want to start such a series again for my ML Poisson model and additionally compare it to the performance of the other models. So let’s start…
Continue reading “Season 2022/23 – A sobering beginning”Inflated ML Poisson model to predict football matches
My last blog post “Poisson vs Reality” did change something in my head. I realized, that I not yet checked single parts of my model enough, whether they differ from reality and whether I could reduce this difference and improve the model performance. That’s why I started creating a new model approach for the new season and focus on the improvement of single steps during the model process. After the training of multiple models, I will test against the fair profit, which kind of adaptions improve a Poisson distribution model the most.
Continue reading “Inflated ML Poisson model to predict football matches”
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