With Euro 2024 in full swing and all the football leagues concluded, it’s the perfect time to look back at the past season. This period of reflection is crucial for any serious bettor. Understanding what went well, identifying areas for improvement, and strategizing for the future are key steps to long-term success. The 2023/2024 season was filled with exciting matches, unexpected results, and valuable lessons. In this recap, I’ll break down the performance of my betting strategy, highlight the successes, pinpoint the failures, and discuss the changes I plan to implement for the upcoming season. Let’s dive into the detailed analysis and insights that will shape my approach moving forward.
Continue reading “Reflecting on the 2023/2024 Football Betting Season: Insights, Challenges, and Future Strategies”Season 2023/24 – Comparing my Mollybet bets with my Ensemble model
Just like every season, I am excited to share my experiences with using different betting models. This year, I have been particularly focused on my ensemble model, which incorporates a diverse range of data and algorithms. Over the first three months of the season, I placed numerous bets, and now I want to take a closer look at the outcomes. I aim to share my insights and analyses regarding these bets. Let’s delve into the realm of sports betting together and explore what we can learn from these initial months.
Continue reading “Season 2023/24 – Comparing my Mollybet bets with my Ensemble model”The predictive power of xG – Predicting football matches with Expected Goals
“Goals are the only statistic, which decide a match” – sentences like this appeared not only once, while reading discussion about the latest xG statistics of single matches on Twitter. Even if the statistic xG is more and more used by sport journalists and during broadcasts, the meaning and importance of the statistic is not yet widely understood. This might be caused by the usage of xG for single matches or single shots. The final result of a match and the corresponding xG values might differ a lot. But over the long-term xG is a statistic, which tells us way more about a football team than goals and shots alone. To prove this, this post will take a look at the predictive power of xG in comparison to goals. The more information a statistic contains the more it should help us to predict the result of future matches.
Continue reading “The predictive power of xG – Predicting football matches with Expected Goals”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.
Team strength MLP (part 3)
Part one defined the basic architecture of the Team Strength MLP (multi layer perceptron). The training process and its monitoring via Tensorboard was explained in part two. Now it is time to take a look at the prediction of football matches. Primarily this consists of following steps:
- Load the prediction data set
- Re-build neural network architecture and load pre-trained weights
- Execute prediction
The Bundesliga season 2017/18 will be the test case for this example. The season 2008 – 2016 were used to train the mode.
Team strength MLP (part 2)
The first part of this series covered the definition of the network architecture for my Team Strength MLP. This neural network must now be trained. To explain and visualize the training process, Tensorflow offers the web frontend TensorBoard. This post will explain, how you use TensorBoard and what are some basic indicators for a well-trained model.
Team strength MLP (part 1)
It is time to build and test my first predictive model with Tensorflow! As I am currently totally unexperienced in creating and optimizing neural networks, I will start with a very simple one, which just uses the predictive variables of the Poisson model. By doing this, I will be able to compare the resulting network with the Poisson model. I am excited to see, whether Tensorflow is able to outperform this statistical model with such a low number of predictive variables. In this series I will provide some basic information, how you are able to build a simple multilayer perceptron (MLP) with Tensorflow, supervise the training process with Tensorboard and use the trained neural network to predict the outcomes of the matches.
Calculate odds for lay markets based on back markets
Some days ago I read an interesting article about how bookies arrange their margin to the possible outcomes. All bookies keep this of course secret as this offers them a specific range, where they can shorten or lengthen the odds depended on the amounts of placed bets. But I need this information, because I simulate my prediction models for back and lay markets, with just the odds of the back markets. This post will explain, how you should calculate the bookie margin, and how you should not do it. I handled this topic a little bit naively during the development of my Poisson model, which causes some problems.
Continue reading “Calculate odds for lay markets based on back markets”
Validate Model: GS & PPG match rating (part 3)
The first and the second part of this series explained some basic methods to optimise the regression models for the GS & PPG match rating. You have now a set of 3 different regression models (linear, polynomial and polynomial without outliers) for each predictive variable. These models now have to not only compete against each other, but also of course against the Bookie odds and the Poisson prediction model.
Continue reading “Validate Model: GS & PPG match rating (part 3)”
Validate Model: GS & PPG match rating (part 2)
The first part of this series took a look at the GS match rating model. The post described, how you are able to identify a non-linear relationship between the predictor variables and the outcome variable. The same methods will now be applied to the PPG match rating model, so that we are able to compare the two different polynomial regression models. On top, I want to show, how you are able to figure out, whether outliers in your data have an influence on your regression model.
Continue reading “Validate Model: GS & PPG match rating (part 2)”
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