Not only is it the core feature of the BeatTheBookie app, but it’s also the primary reason why I started developing the app. However, why is this the case, and how does this type of predictive model work for predicting the outcomes of football matches? Let’s delve into it.
Continue reading “Inside the BeatTheBookie App – Predicting football matches with an Ensemble model”BeatTheBookieApp – Ensemble performance
In this blog post, we will explore the performance dashboard for the BeatTheBookie App’s ensemble model. This powerful predictive model combines the expertise of multiple individual models to forecast football match outcomes. By leveraging insights from the performance dashboard, we can make informed betting decisions and increase our chances of success. Let’s dive into the details of this game-changing feature.
Continue reading “BeatTheBookieApp – Ensemble performance”BeatTheBookieApp – Ensemble predictions
The BeatTheBookie App is a web data app that provides me with predictions for my daily betting. However, there has always been something that bothered me a bit: the comparison to the odds of a bookie. Each bookie uses a different margin, making it difficult to identify value and determine the amount of value a bet offers. Fortunately, this issue is addressed with the introduction of the Ensemble predictions dashboard.
Continue reading “BeatTheBookieApp – Ensemble predictions”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.
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”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?”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.
Continue reading “ZIP model performance incl. minor leagues”Retrospective for Bundesliga season 2018/19
Before the new season will start I should take a look at the last season. Everybody following my pick history already knows: the last season again was very disappointing! But I again have to point out, that I of course did not expect to find the “holy grail” after just two seasons of model testing. So how bad do the numbers really look, and what are the most important “lesson learned” are….
Continue reading “Retrospective for Bundesliga season 2018/19”
A data journey – market values (part 3)
In the 3rd part of this series, I take a look at the – from my point of view – most important part about my market value data journey: Does the team market value holds some predictive power? If so, I could use it as another feature for my predictive models.
Four things I have learned after using a neural network for 6 months
This time, after over 20 matchdays in the German Bundesliga, I don’t want to take a look at the predicted results. I used my Team Strength MLP now for about 6 months. During this time I analysed the predictions and tried to learn some more stuff about deep learning. So let’s summarize some lessons I have already learned and what could be improved for my model for the next season.
Continue reading “Four things I have learned after using a neural network for 6 months”
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