BeatTheBookieApp – Model performance

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.

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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.

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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.

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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.

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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?

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ZIP 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.

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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…

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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.

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